darknet Google Colab/Tiny_yolo_v3_explained.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Tiny yolo v3 explained",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X_dziP9RWHyE",
        "colab_type": "text"
      },
      "source": [
        "**Mount goodle drive**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ex4UvIlmWGMJ",
        "colab_type": "code",
        "outputId": "5d6577ed-1022-4a08-9202-d797b95b7220",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 124
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/gdrive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r-0w6VOO6xZD",
        "colab_type": "text"
      },
      "source": [
        "**Add /darknet and /darknet/cuDNN folders in google drive**\n",
        "\n",
        "install tree to check its contents\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XlKSZJJy7-7U",
        "colab_type": "code",
        "outputId": "d4dca0c7-d31e-4b75-a93e-44d1a7ed7d4b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 816
        }
      },
      "source": [
        "!ls -la \"/content/gdrive/My Drive/darknet\""
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "total 245821\n",
            "drwx------ 2 root root      4096 Jan 22 11:36 3rdparty\n",
            "-rw------- 1 root root        52 Jan 30 11:59 anchors.txt\n",
            "-rw------- 1 root root      5899 Jan 22 11:36 appveyor.yml\n",
            "drwx------ 2 root root      4096 Jan 27 22:32 backup\n",
            "drwx------ 2 root root      4096 Jan 27 22:31 backup_old\n",
            "-rw------- 1 root root      5831 Jan 22 16:57 bad.list\n",
            "drwx------ 2 root root      4096 Jan 22 11:44 bin\n",
            "drwx------ 2 root root      4096 Jan 22 11:36 build\n",
            "-rw------- 1 root root      8163 Jan 22 11:36 build.ps1\n",
            "-rw------- 1 root root      2886 Jan 22 11:36 build.sh\n",
            "drwx------ 2 root root      4096 Jan 22 11:36 cfg\n",
            "-rw------- 1 root root     70059 Jan 28 23:20 chart.png\n",
            "drwx------ 2 root root      4096 Jan 22 11:36 .circleci\n",
            "drwx------ 2 root root      4096 Jan 22 11:36 cmake\n",
            "-rw------- 1 root root     17356 Jan 22 11:36 CMakeLists.txt\n",
            "-rw------- 1 root root      6380 Jan 22 11:36 CMakeSettings.json\n",
            "-rwx------ 1 root root   3221752 Jan 22 11:43 darknet\n",
            "-rw------- 1 root root      1254 Jan 22 11:36 DarknetConfig.cmake.in\n",
            "-rw------- 1 root root     16255 Jan 22 11:36 darknet.py\n",
            "-rw------- 1 root root      4010 Jan 22 11:36 darknet_video.py\n",
            "drwx------ 2 root root      4096 Jan 22 11:36 data\n",
            "drwx------ 2 root root      4096 Jan 22 11:36 .git\n",
            "-rw------- 1 root root       316 Jan 22 11:36 .gitignore\n",
            "-rw------- 1 root root       108 Jan 22 11:36 image_yolov2.sh\n",
            "-rw------- 1 root root       110 Jan 22 11:36 image_yolov3.sh\n",
            "drwx------ 2 root root      4096 Jan 19 18:47 img\n",
            "drwx------ 2 root root      4096 Jan 22 11:36 include\n",
            "-rw------- 1 root root       345 Jan 22 11:36 json_mjpeg_streams.sh\n",
            "-rw------- 1 root root       515 Jan 22 11:36 LICENSE\n",
            "-rw------- 1 root root      4939 Jan 22 11:42 Makefile\n",
            "-rw------- 1 root root       159 Jan 22 11:36 net_cam_v3.sh\n",
            "drwx------ 2 root root      4096 Jan 22 11:42 obj\n",
            "-rw------- 1 root root       217 Jan 19 21:18 obj.data\n",
            "-rw------- 1 root root         7 Jan 19 21:19 obj.names\n",
            "-rw------- 1 root root     75460 Jan 22 11:58 predictions.jpg\n",
            "-rw------- 1 root root     51305 Jan 22 11:36 README.md\n",
            "drwx------ 2 root root      4096 Jan 22 11:42 results\n",
            "drwx------ 2 root root      4096 Jan 22 11:36 scripts\n",
            "drwx------ 2 root root      4096 Jan 22 11:36 src\n",
            "-rw------- 1 root root     21405 Jan 22 12:08 test.txt\n",
            "-rw------- 1 root root     86222 Jan 22 12:08 train.txt\n",
            "-rw------- 1 root root     34439 Jan 22 11:36 .travis.yml\n",
            "-rw------- 1 root root       108 Jan 22 11:36 video_v2.sh\n",
            "-rw------- 1 root root       108 Jan 22 11:36 video_yolov3.sh\n",
            "drwx------ 2 root root      4096 Jan 22 12:17 weights\n",
            "-rw------- 1 root root 248007048 Mar 25  2018 yolov3.weights\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YPFCP--l8ArC",
        "colab_type": "code",
        "outputId": "d1e0ac0c-c3d8-402c-cf09-2b3d47b1c9f8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 335
        }
      },
      "source": [
        "!sudo apt-get install tree"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-3-9a01be9cc27c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msystem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'sudo apt-get install tree'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/google/colab/_shell.py\u001b[0m in \u001b[0;36msystem\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m     94\u001b[0m       \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m'also_return_output'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     95\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 96\u001b[0;31m     \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_system_commands\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_system_compat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# pylint:disable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     97\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     98\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mpip_warn\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/google/colab/_system_commands.py\u001b[0m in \u001b[0;36m_system_compat\u001b[0;34m(shell, cmd, also_return_output)\u001b[0m\n\u001b[1;32m    436\u001b[0m   \u001b[0;31m# stack.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    437\u001b[0m   result = _run_command(\n\u001b[0;32m--> 438\u001b[0;31m       shell.var_expand(cmd, depth=2), clear_streamed_output=False)\n\u001b[0m\u001b[1;32m    439\u001b[0m   \u001b[0mshell\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0muser_ns\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'_exit_code'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreturncode\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    440\u001b[0m   \u001b[0;32mif\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreturncode\u001b[0m \u001b[0;32min\u001b[0m \u001b[0m_INTERRUPTED_SIGNALS\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/google/colab/_system_commands.py\u001b[0m in \u001b[0;36m_run_command\u001b[0;34m(cmd, clear_streamed_output)\u001b[0m\n\u001b[1;32m    179\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    180\u001b[0m     with temporary_clearer(), _display_stdin_widget(\n\u001b[0;32m--> 181\u001b[0;31m         delay_millis=500) as update_stdin_widget:\n\u001b[0m\u001b[1;32m    182\u001b[0m       \u001b[0;31m# TODO(b/115531839): Ensure that subprocesses are terminated upon\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    183\u001b[0m       \u001b[0;31m# interrupt.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.6/contextlib.py\u001b[0m in \u001b[0;36m__enter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     79\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__enter__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     80\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 81\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgen\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     82\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mStopIteration\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     83\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mRuntimeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"generator didn't yield\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/google/colab/_system_commands.py\u001b[0m in \u001b[0;36m_display_stdin_widget\u001b[0;34m(delay_millis)\u001b[0m\n\u001b[1;32m    339\u001b[0m   \u001b[0mshell\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_ipython\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    340\u001b[0m   \u001b[0mdisplay_args\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m'cell_display_stdin'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m'delayMillis'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mdelay_millis\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 341\u001b[0;31m   \u001b[0m_message\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mblocking_request\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mdisplay_args\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparent\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mshell\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparent_header\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    342\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    343\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0mecho_updater\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_echo_status\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/google/colab/_message.py\u001b[0m in \u001b[0;36mblocking_request\u001b[0;34m(request_type, request, timeout_sec, parent)\u001b[0m\n\u001b[1;32m    169\u001b[0m   \u001b[0;31m# unique.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    170\u001b[0m   \u001b[0mrequest_id\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msend_request\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrequest_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrequest\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparent\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mparent\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 171\u001b[0;31m   \u001b[0;32mreturn\u001b[0m \u001b[0mread_reply_from_input\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrequest_id\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout_sec\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/google/colab/_message.py\u001b[0m in \u001b[0;36mread_reply_from_input\u001b[0;34m(message_id, timeout_sec)\u001b[0m\n\u001b[1;32m     99\u001b[0m     \u001b[0mreply\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_read_next_input_message\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    100\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mreply\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0m_NOT_READY\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreply\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 101\u001b[0;31m       \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msleep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0.025\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    102\u001b[0m       \u001b[0;32mcontinue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    103\u001b[0m     if (reply.get('type') == 'colab_reply' and\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t114qLi78A38",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!tree /content/gdrive/My\\ Drive/darknet/"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jlAhquXQ8uI9",
        "colab_type": "text"
      },
      "source": [
        "**Check CUDA preinstalled version**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I8mTkkcN8hzT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "\n",
        "\n",
        "\n",
        "!/usr/local/cuda/bin/nvcc --version"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tvb6GyZA9_ZL",
        "colab_type": "text"
      },
      "source": [
        "**Upload cuDNN 10.0 to the folder /darknet/cuDNN**\n",
        "\n",
        "Then unzip it and check version. For some PCs it is not solitairetheme8 but tgz extension"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vJiKg1jX81Q-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# We're unzipping the cuDNN files from your Drive folder directly to the VM CUDA folders\n",
        "!tar -xzvf gdrive/My\\ Drive/darknet/cuDNN/cudnn-10.0-linux-x64-v7.6.5.32.solitairetheme8 -C /usr/local/\n",
        "!chmod a+r /usr/local/cuda/include/cudnn.h\n",
        "\n",
        "# Now we check the version we already installed. Can comment this line on future runs\n",
        "!cat /usr/local/cuda/include/cudnn.h | grep CUDNN_MAJOR -A 2"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qbrNtKwxAwNi",
        "colab_type": "text"
      },
      "source": [
        "**Installing and building darknet from github**\n",
        "\n",
        "**Replace cuDNN folder from darknet to one level higher and delete darknet folder to clone it from github**\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AHS3sGmzAY8G",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!git clone https://github.com/kriyeng/darknet/ /content/gdrive/My\\ Drive/darknet"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "raY7UGhdDcc3",
        "colab_type": "text"
      },
      "source": [
        "**Change the directory to darknet**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hsK56BlJCK2U",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "%cd /content/gdrive/My\\ Drive/darknet"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XBnm8zvcDlst",
        "colab_type": "text"
      },
      "source": [
        "**Compile the darknet**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ktGSMn3XDFs9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Check the folder\n",
        "!ls\n",
        "\n",
        "# I have a branch where I have done the changes commented above\n",
        "!git checkout feature/google-colab\n",
        "\n",
        "#Compile Darknet\n",
        "!make"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M7ReWgTwEbe0",
        "colab_type": "text"
      },
      "source": [
        "**Copy the compiled darknet to bin directory**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3QPXy6OWEYaT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Copies the Darknet compiled version to Google drive\n",
        "!mkdir /content/gdrive/My\\ Drive/darknet/bin/ \n",
        "!cp ./darknet /content/gdrive/My\\ Drive/darknet/bin/darknet"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6tEIMhoaEnUM",
        "colab_type": "text"
      },
      "source": [
        "**Make darknet file executable**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KzqpWMBlDOW0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Set execution permissions to Darknet\n",
        "!chmod +x ./darknet"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "31OXzFvXFLgS",
        "colab_type": "text"
      },
      "source": [
        "**To show up that darknet works lets run some commands**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "591zYFXkFB7G",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#download files\n",
        "def imShow(path):\n",
        "  import cv2\n",
        "  import matplotlib.pyplot as plt\n",
        "  %matplotlib inline\n",
        "\n",
        "  image = cv2.imread(path)\n",
        "  height, width = image.shape[:2]\n",
        "  resized_image = cv2.resize(image,(3*width, 3*height), interpolation = cv2.INTER_CUBIC)\n",
        "\n",
        "  fig = plt.gcf()\n",
        "  fig.set_size_inches(18, 10)\n",
        "  plt.axis(\"off\")\n",
        "  #plt.rcParams['figure.figsize'] = [10, 5]\n",
        "  plt.imshow(cv2.cvtColor(resized_image, cv2.COLOR_BGR2RGB))\n",
        "  plt.show()\n",
        "  \n",
        "  \n",
        "def upload():\n",
        "  from google.colab import files\n",
        "  uploaded = files.upload() \n",
        "  for name, data in uploaded.items():\n",
        "    with open(name, 'wb') as f:\n",
        "      f.write(data)\n",
        "      print ('saved file', name)\n",
        "def download(path):\n",
        "  from google.colab import files\n",
        "  files.download(path)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z51YJiW_FPZr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!wget https://pjreddie.com/media/files/yolov3.weights"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bYWHqHBVFVY7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!./darknet detect cfg/yolov3.cfg yolov3.weights data/person.jpg -dont-show\n",
        "\n",
        "# Show the result using the helper imgShow()\n",
        "imShow('predictions.jpg')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yXJuZNnKGshy",
        "colab_type": "text"
      },
      "source": [
        "**Training yolov3-tiny**\n",
        "\n",
        "Upload img folder with images and txt files to google drive /darknet/img\n",
        "\n",
        "create obj.data and obj.names files\n",
        "\n",
        "obj.data (count of classes, paths to train, test, class names and results folders) content:\n",
        "\n",
        "```\n",
        "classes= 1\n",
        "train = /content/gdrive/My\\ Drive/darknet/train.txt\n",
        "valid = /content/gdrive/My\\ Drive/darknet/test.txt \n",
        "names = /content/gdrive/My\\ Drive/darknet/obj.names\n",
        "backup = /content/gdrive/My\\ Drive/darknet/backup\n",
        "```\n",
        "\n",
        "obj.names (names of classes) content:\n",
        "\n",
        "`banana`\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4hG3vlXvJR42",
        "colab_type": "text"
      },
      "source": [
        "**List all jpg files **"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aMwvFh8qFdgZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import os\n",
        "directory = \"/content/gdrive/My Drive/darknet/img\"\n",
        "filelist=os.listdir(directory)\n",
        "for fichier in filelist[:]:# filelist[:] makes a copy of filelist.\n",
        "  if not(fichier.endswith(\".jpg\")):\n",
        "    filelist.remove(fichier)\n",
        "print(filelist)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M-Eza7dVJrIV",
        "colab_type": "text"
      },
      "source": [
        "Divide the list of files to training and testing set (80% and 20%)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "niRiaNSzJp5V",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train = open('/content/gdrive/My Drive/darknet/train.txt', 'w')\n",
        "test=open('/content/gdrive/My Drive/darknet/test.txt', 'w')\n",
        "len_of_list=len(filelist)\n",
        "eighty_limit=0.8*len_of_list\n",
        "iterator=1\n",
        "for item in filelist:\n",
        "  if(iterator > eighty_limit):\n",
        "    test.write(\"/content/gdrive/My Drive/darknet/img/%s\\r\\n\" % item)\n",
        "  else:\n",
        "    train.write(\"/content/gdrive/My Drive/darknet/img/%s\\r\\n\" % item)\n",
        "  iterator=iterator+1\n",
        "\n",
        "train.close()\n",
        "test.close()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "66Gg5OujJeUI",
        "colab_type": "text"
      },
      "source": [
        "**Training model**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PVaBtj6UkGMW",
        "colab_type": "text"
      },
      "source": [
        "First change cfg file so that batch is equal 64 and subdivision paramether is equal 16"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QhmhnXT_kVt7",
        "colab_type": "text"
      },
      "source": [
        "The best way to do it is by downloading it and changing locally cause after editing in Google Docs colab may not recognize this file. It should be like this"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xNXHPnjPNS_Y",
        "colab_type": "text"
      },
      "source": [
        "The results are stored in darknet/backup folder, (weights) files are generated every 1000 iterations and after every 100 iterations the file (*_last.weights) is updated. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lS-f_mrBdaq-",
        "colab_type": "code",
        "outputId": "968d5f19-09c1-4d6c-c055-9a1b8adb3e97",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        }
      },
      "source": [
        "%cd /content/gdrive/My\\ Drive/darknet\n",
        "!ls\n",
        "!chmod +x darknet"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/gdrive/My Drive/darknet\n",
            "3rdparty      chart.png\t\t      img\t\t     results\n",
            "anchors.txt   cmake\t\t      include\t\t     scripts\n",
            "appveyor.yml  CMakeLists.txt\t      json_mjpeg_streams.sh  src\n",
            "backup\t      CMakeSettings.json      LICENSE\t\t     test.txt\n",
            "backup_old    darknet\t\t      Makefile\t\t     train.txt\n",
            "bad.list      DarknetConfig.cmake.in  net_cam_v3.sh\t     video_v2.sh\n",
            "bin\t      darknet.py\t      obj\t\t     video_yolov3.sh\n",
            "build\t      darknet_video.py\t      obj.data\t\t     weights\n",
            "build.ps1     data\t\t      obj.names\t\t     yolov3.weights\n",
            "build.sh      image_yolov2.sh\t      predictions.jpg\n",
            "cfg\t      image_yolov3.sh\t      README.md\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wqkrIvdktcKC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!ls cfg"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ug-EzVTIauOU",
        "colab_type": "code",
        "outputId": "fe011fb6-59ca-45f7-b5e7-d650bd7ee2a0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289
        }
      },
      "source": [
        "!./darknet detector calc_anchors \"/content/gdrive/My Drive/darknet/obj.data\"  -num_of_clusters 6 -width 416 -height 416\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            " num_of_clusters = 6, width = 416, height = 416 \n",
            " read labels from 1471 images \n",
            " loaded \t image: 1471 \t box: 1469\n",
            " all loaded. \n",
            "\n",
            " calculating k-means++ ...\n",
            "\n",
            " iterations = 18 \n",
            "\n",
            "\n",
            " avg IoU = 73.80 % \n",
            "\n",
            "Saving anchors to the file: anchors.txt \n",
            "anchors =  36, 98,  64, 63,  55,123, 106,107,  80,207, 167,207\n",
            "^C\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IqSFjoM989O9",
        "colab_type": "code",
        "outputId": "be0af564-b96a-4a6d-cff8-3c5df039c49e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 527
        }
      },
      "source": [
        "!./darknet partial \"/content/gdrive/My Drive/darknet/cfg/yolov3-tiny-280120.cfg\" \"/content/gdrive/My Drive/darknet/cfg/yolov3-tiny.weights\" \"/content/gdrive/My Drive/darknet/cfg/yolov3-tiny.conv.15\" 15"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "layer     filters    size              input                output\n",
            "   0 conv     16  3 x 3 / 1   416 x 416 x   3   ->   416 x 416 x  16 0.150 BF\n",
            "   1 max          2 x 2 / 2   416 x 416 x  16   ->   208 x 208 x  16 0.003 BF\n",
            "   2 conv     32  3 x 3 / 1   208 x 208 x  16   ->   208 x 208 x  32 0.399 BF\n",
            "   3 max          2 x 2 / 2   208 x 208 x  32   ->   104 x 104 x  32 0.001 BF\n",
            "   4 conv     64  3 x 3 / 1   104 x 104 x  32   ->   104 x 104 x  64 0.399 BF\n",
            "   5 max          2 x 2 / 2   104 x 104 x  64   ->    52 x  52 x  64 0.001 BF\n",
            "   6 conv    128  3 x 3 / 1    52 x  52 x  64   ->    52 x  52 x 128 0.399 BF\n",
            "   7 max          2 x 2 / 2    52 x  52 x 128   ->    26 x  26 x 128 0.000 BF\n",
            "   8 conv    256  3 x 3 / 1    26 x  26 x 128   ->    26 x  26 x 256 0.399 BF\n",
            "   9 max          2 x 2 / 2    26 x  26 x 256   ->    13 x  13 x 256 0.000 BF\n",
            "  10 conv    512  3 x 3 / 1    13 x  13 x 256   ->    13 x  13 x 512 0.399 BF\n",
            "  11 max          2 x 2 / 1    13 x  13 x 512   ->    13 x  13 x 512 0.000 BF\n",
            "  12 conv   1024  3 x 3 / 1    13 x  13 x 512   ->    13 x  13 x1024 1.595 BF\n",
            "  13 conv    256  1 x 1 / 1    13 x  13 x1024   ->    13 x  13 x 256 0.089 BF\n",
            "  14 conv    512  3 x 3 / 1    13 x  13 x 256   ->    13 x  13 x 512 0.399 BF\n",
            "  15 conv     18  1 x 1 / 1    13 x  13 x 512   ->    13 x  13 x  18 0.003 BF\n",
            "  16 yolo\n",
            "  17 route  13\n",
            "  18 conv    128  1 x 1 / 1    13 x  13 x 256   ->    13 x  13 x 128 0.011 BF\n",
            "  19 upsample            2x    13 x  13 x 128   ->    26 x  26 x 128\n",
            "  20 route  19 8\n",
            "  21 conv    256  3 x 3 / 1    26 x  26 x 384   ->    26 x  26 x 256 1.196 BF\n",
            "  22 conv     18  1 x 1 / 1    26 x  26 x 256   ->    26 x  26 x  18 0.006 BF\n",
            "  23 yolo\n",
            "Total BFLOPS 5.448 \n",
            "Loading weights from /content/gdrive/My Drive/darknet/cfg/yolov3-tiny.weights...\n",
            " seen 64 \n",
            "Done!\n",
            "Saving weights to /content/gdrive/My Drive/darknet/cfg/yolov3-tiny.conv.15\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LzwpvAd7AKfh",
        "colab_type": "code",
        "outputId": "aa2b8dde-623a-43a1-8cd7-0466f0a596be",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "!./darknet detector train \"/content/gdrive/My Drive/darknet/obj.data\" \"/content/gdrive/My Drive/darknet/cfg/yolov3-tiny-280120.cfg\" \"/content/gdrive/My Drive/darknet/cfg/yolov3-tiny.conv.15\" -dont_show -map"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            " Prepare additional network for mAP calculation...\n",
            "layer     filters    size              input                output\n",
            "   0 conv     16  3 x 3 / 1   416 x 416 x   3   ->   416 x 416 x  16 0.150 BF\n",
            "   1 max          2 x 2 / 2   416 x 416 x  16   ->   208 x 208 x  16 0.003 BF\n",
            "   2 conv     32  3 x 3 / 1   208 x 208 x  16   ->   208 x 208 x  32 0.399 BF\n",
            "   3 max          2 x 2 / 2   208 x 208 x  32   ->   104 x 104 x  32 0.001 BF\n",
            "   4 conv     64  3 x 3 / 1   104 x 104 x  32   ->   104 x 104 x  64 0.399 BF\n",
            "   5 max          2 x 2 / 2   104 x 104 x  64   ->    52 x  52 x  64 0.001 BF\n",
            "   6 conv    128  3 x 3 / 1    52 x  52 x  64   ->    52 x  52 x 128 0.399 BF\n",
            "   7 max          2 x 2 / 2    52 x  52 x 128   ->    26 x  26 x 128 0.000 BF\n",
            "   8 conv    256  3 x 3 / 1    26 x  26 x 128   ->    26 x  26 x 256 0.399 BF\n",
            "   9 max          2 x 2 / 2    26 x  26 x 256   ->    13 x  13 x 256 0.000 BF\n",
            "  10 conv    512  3 x 3 / 1    13 x  13 x 256   ->    13 x  13 x 512 0.399 BF\n",
            "  11 max          2 x 2 / 1    13 x  13 x 512   ->    13 x  13 x 512 0.000 BF\n",
            "  12 conv   1024  3 x 3 / 1    13 x  13 x 512   ->    13 x  13 x1024 1.595 BF\n",
            "  13 conv    256  1 x 1 / 1    13 x  13 x1024   ->    13 x  13 x 256 0.089 BF\n",
            "  14 conv    512  3 x 3 / 1    13 x  13 x 256   ->    13 x  13 x 512 0.399 BF\n",
            "  15 conv     18  1 x 1 / 1    13 x  13 x 512   ->    13 x  13 x  18 0.003 BF\n",
            "  16 yolo\n",
            "  17 route  13\n",
            "  18 conv    128  1 x 1 / 1    13 x  13 x 256   ->    13 x  13 x 128 0.011 BF\n",
            "  19 upsample            2x    13 x  13 x 128   ->    26 x  26 x 128\n",
            "  20 route  19 8\n",
            "  21 conv    256  3 x 3 / 1    26 x  26 x 384   ->    26 x  26 x 256 1.196 BF\n",
            "  22 conv     18  1 x 1 / 1    26 x  26 x 256   ->    26 x  26 x  18 0.006 BF\n",
            "  23 yolo\n",
            "Total BFLOPS 5.448 \n",
            " Allocate additional workspace_size = 52.43 MB \n",
            "yolov3-tiny-280120\n",
            "layer     filters    size              input                output\n",
            "   0 conv     16  3 x 3 / 1   416 x 416 x   3   ->   416 x 416 x  16 0.150 BF\n",
            "   1 max          2 x 2 / 2   416 x 416 x  16   ->   208 x 208 x  16 0.003 BF\n",
            "   2 conv     32  3 x 3 / 1   208 x 208 x  16   ->   208 x 208 x  32 0.399 BF\n",
            "   3 max          2 x 2 / 2   208 x 208 x  32   ->   104 x 104 x  32 0.001 BF\n",
            "   4 conv     64  3 x 3 / 1   104 x 104 x  32   ->   104 x 104 x  64 0.399 BF\n",
            "   5 max          2 x 2 / 2   104 x 104 x  64   ->    52 x  52 x  64 0.001 BF\n",
            "   6 conv    128  3 x 3 / 1    52 x  52 x  64   ->    52 x  52 x 128 0.399 BF\n",
            "   7 max          2 x 2 / 2    52 x  52 x 128   ->    26 x  26 x 128 0.000 BF\n",
            "   8 conv    256  3 x 3 / 1    26 x  26 x 128   ->    26 x  26 x 256 0.399 BF\n",
            "   9 max          2 x 2 / 2    26 x  26 x 256   ->    13 x  13 x 256 0.000 BF\n",
            "  10 conv    512  3 x 3 / 1    13 x  13 x 256   ->    13 x  13 x 512 0.399 BF\n",
            "  11 max          2 x 2 / 1    13 x  13 x 512   ->    13 x  13 x 512 0.000 BF\n",
            "  12 conv   1024  3 x 3 / 1    13 x  13 x 512   ->    13 x  13 x1024 1.595 BF\n",
            "  13 conv    256  1 x 1 / 1    13 x  13 x1024   ->    13 x  13 x 256 0.089 BF\n",
            "  14 conv    512  3 x 3 / 1    13 x  13 x 256   ->    13 x  13 x 512 0.399 BF\n",
            "  15 conv     18  1 x 1 / 1    13 x  13 x 512   ->    13 x  13 x  18 0.003 BF\n",
            "  16 yolo\n",
            "  17 route  13\n",
            "  18 conv    128  1 x 1 / 1    13 x  13 x 256   ->    13 x  13 x 128 0.011 BF\n",
            "  19 upsample            2x    13 x  13 x 128   ->    26 x  26 x 128\n",
            "  20 route  19 8\n",
            "  21 conv    256  3 x 3 / 1    26 x  26 x 384   ->    26 x  26 x 256 1.196 BF\n",
            "  22 conv     18  1 x 1 / 1    26 x  26 x 256   ->    26 x  26 x  18 0.006 BF\n",
            "  23 yolo\n",
            "Total BFLOPS 5.448 \n",
            " Allocate additional workspace_size = 1302.33 MB \n",
            "Loading weights from /content/gdrive/My Drive/darknet/cfg/yolov3-tiny.conv.15...\n",
            " seen 64 \n",
            "Done!\n",
            "Learning Rate: 0.001, Momentum: 0.9, Decay: 0.0005\n",
            "Resizing\n",
            "608 x 608 \n",
            " try to allocate additional workspace_size = 4610.72 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 5.039188 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 1: 681.868652, 681.868652 avg loss, 0.000000 rate, 0.942761 seconds, 64 images\n",
            "Loaded: 8.669707 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 2: 681.890625, 681.870850 avg loss, 0.000000 rate, 0.935884 seconds, 128 images\n",
            "Loaded: 6.774074 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 3: 682.427002, 681.926453 avg loss, 0.000000 rate, 0.934533 seconds, 192 images\n",
            "Loaded: 6.840081 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 4: 683.007080, 682.034546 avg loss, 0.000000 rate, 0.915485 seconds, 256 images\n",
            "Loaded: 7.041430 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 5: 683.451050, 682.176208 avg loss, 0.000000 rate, 0.943713 seconds, 320 images\n",
            "Loaded: 6.543009 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 6: 684.016541, 682.360229 avg loss, 0.000000 rate, 0.919361 seconds, 384 images\n",
            "Loaded: 6.578170 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 7: 682.412964, 682.365479 avg loss, 0.000000 rate, 0.900738 seconds, 448 images\n",
            "Loaded: 6.141505 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 8: 683.347961, 682.463745 avg loss, 0.000000 rate, 0.913429 seconds, 512 images\n",
            "Loaded: 6.505880 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 9: 683.208557, 682.538208 avg loss, 0.000000 rate, 0.938490 seconds, 576 images\n",
            "Loaded: 6.223505 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 10: 682.585876, 682.542969 avg loss, 0.000000 rate, 0.935183 seconds, 640 images\n",
            "Resizing\n",
            "416 x 416 \n",
            " try to allocate additional workspace_size = 1302.33 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 3.731253 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 11: 319.755219, 646.264221 avg loss, 0.000000 rate, 0.449682 seconds, 704 images\n",
            "Loaded: 5.089391 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 12: 321.692322, 613.807007 avg loss, 0.000000 rate, 0.450669 seconds, 768 images\n",
            "Loaded: 5.768127 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 13: 321.148529, 584.541138 avg loss, 0.000000 rate, 0.488568 seconds, 832 images\n",
            "Loaded: 5.420180 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 14: 320.494690, 558.136475 avg loss, 0.000000 rate, 0.480353 seconds, 896 images\n",
            "Loaded: 5.510123 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 15: 320.495544, 534.372375 avg loss, 0.000000 rate, 0.434074 seconds, 960 images\n",
            "Loaded: 5.538972 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 16: 320.077637, 512.942871 avg loss, 0.000000 rate, 0.455762 seconds, 1024 images\n",
            "Loaded: 4.040781 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 17: 320.192688, 493.667847 avg loss, 0.000000 rate, 0.452889 seconds, 1088 images\n",
            "Loaded: 6.703744 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 18: 320.254517, 476.326508 avg loss, 0.000000 rate, 0.426076 seconds, 1152 images\n",
            "Loaded: 4.102301 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 19: 320.275970, 460.721466 avg loss, 0.000000 rate, 0.440087 seconds, 1216 images\n",
            "Loaded: 4.163655 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 20: 320.024597, 446.651794 avg loss, 0.000000 rate, 0.450247 seconds, 1280 images\n",
            "Resizing\n",
            "352 x 352 \n",
            " try to allocate additional workspace_size = 1264.58 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 3.179644 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 21: 230.728149, 425.059418 avg loss, 0.000000 rate, 0.324542 seconds, 1344 images\n",
            "Loaded: 3.823148 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 22: 230.241074, 405.577576 avg loss, 0.000000 rate, 0.351012 seconds, 1408 images\n",
            "Loaded: 4.362358 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 23: 229.420715, 387.961884 avg loss, 0.000000 rate, 0.356072 seconds, 1472 images\n",
            "Loaded: 3.128793 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 24: 229.933350, 372.159027 avg loss, 0.000000 rate, 0.333011 seconds, 1536 images\n",
            "Loaded: 3.641255 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 25: 230.553497, 357.998474 avg loss, 0.000000 rate, 0.351477 seconds, 1600 images\n",
            "Loaded: 3.800307 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 26: 230.333725, 345.231995 avg loss, 0.000000 rate, 0.349621 seconds, 1664 images\n",
            "Loaded: 3.115547 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 27: 231.029724, 333.811768 avg loss, 0.000000 rate, 0.346893 seconds, 1728 images\n",
            "Loaded: 4.216700 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 28: 230.779800, 323.508575 avg loss, 0.000000 rate, 0.325055 seconds, 1792 images\n",
            "Loaded: 2.753874 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 29: 230.178680, 314.175598 avg loss, 0.000000 rate, 0.338996 seconds, 1856 images\n",
            "Loaded: 3.273111 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 30: 230.552429, 305.813293 avg loss, 0.000000 rate, 0.354832 seconds, 1920 images\n",
            "Resizing\n",
            "576 x 576 \n",
            " try to allocate additional workspace_size = 4605.87 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.000043 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 31: 611.343384, 336.366302 avg loss, 0.000000 rate, 0.868771 seconds, 1984 images\n",
            "Loaded: 4.023537 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 32: 612.439819, 363.973663 avg loss, 0.000000 rate, 0.935626 seconds, 2048 images\n",
            "Loaded: 3.023115 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 33: 614.724731, 389.048767 avg loss, 0.000000 rate, 0.900121 seconds, 2112 images\n",
            "Loaded: 2.321530 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 34: 612.850586, 411.428955 avg loss, 0.000000 rate, 0.827403 seconds, 2176 images\n",
            "Loaded: 2.202588 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 35: 611.910461, 431.477112 avg loss, 0.000000 rate, 0.909106 seconds, 2240 images\n",
            "Loaded: 1.925015 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 36: 611.701782, 449.499573 avg loss, 0.000000 rate, 0.923027 seconds, 2304 images\n",
            "Loaded: 1.663470 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 37: 613.038330, 465.853455 avg loss, 0.000000 rate, 0.792849 seconds, 2368 images\n",
            "Loaded: 1.659087 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 38: 614.079712, 480.676086 avg loss, 0.000000 rate, 0.780291 seconds, 2432 images\n",
            "Loaded: 4.045407 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 39: 611.444702, 493.752960 avg loss, 0.000000 rate, 0.881367 seconds, 2496 images\n",
            "Loaded: 3.172121 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 40: 612.410950, 505.618774 avg loss, 0.000000 rate, 0.907140 seconds, 2560 images\n",
            "Resizing\n",
            "608 x 608 \n",
            " try to allocate additional workspace_size = 4610.72 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.000036 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 41: 681.457336, 523.202637 avg loss, 0.000000 rate, 1.031365 seconds, 2624 images\n",
            "Loaded: 2.757200 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 42: 680.997559, 538.982117 avg loss, 0.000000 rate, 0.993691 seconds, 2688 images\n",
            "Loaded: 1.508692 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 43: 680.350952, 553.119019 avg loss, 0.000000 rate, 1.019310 seconds, 2752 images\n",
            "Loaded: 1.712392 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 44: 680.593262, 565.866455 avg loss, 0.000000 rate, 1.029731 seconds, 2816 images\n",
            "Loaded: 1.544044 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 45: 680.115784, 577.291382 avg loss, 0.000000 rate, 1.028728 seconds, 2880 images\n",
            "Loaded: 1.797699 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 46: 678.194702, 587.381714 avg loss, 0.000000 rate, 0.954590 seconds, 2944 images\n",
            "Loaded: 2.787329 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 47: 678.386230, 596.482178 avg loss, 0.000000 rate, 1.076388 seconds, 3008 images\n",
            "Loaded: 1.530384 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 48: 676.904846, 604.524414 avg loss, 0.000000 rate, 1.116491 seconds, 3072 images\n",
            "Loaded: 1.835410 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 49: 679.419922, 612.013977 avg loss, 0.000000 rate, 0.922422 seconds, 3136 images\n",
            "Loaded: 2.379879 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 50: 676.561035, 618.468689 avg loss, 0.000000 rate, 1.056274 seconds, 3200 images\n",
            "Resizing\n",
            "512 x 512 \n",
            " try to allocate additional workspace_size = 4596.96 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 1.847505 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 51: 479.538971, 604.575745 avg loss, 0.000000 rate, 0.763530 seconds, 3264 images\n",
            "Loaded: 1.721528 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 52: 479.934448, 592.111633 avg loss, 0.000000 rate, 0.614861 seconds, 3328 images\n",
            "Loaded: 2.204932 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 53: 478.632355, 580.763733 avg loss, 0.000000 rate, 0.747864 seconds, 3392 images\n",
            "Loaded: 1.821333 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 54: 477.563080, 570.443665 avg loss, 0.000000 rate, 0.815787 seconds, 3456 images\n",
            "Loaded: 1.430292 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 55: 476.670776, 561.066406 avg loss, 0.000000 rate, 0.688500 seconds, 3520 images\n",
            "Loaded: 1.655671 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 56: 476.037018, 552.563477 avg loss, 0.000000 rate, 0.598501 seconds, 3584 images\n",
            "Loaded: 1.724190 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 57: 475.533630, 544.860474 avg loss, 0.000000 rate, 0.697576 seconds, 3648 images\n",
            "Loaded: 1.371864 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 58: 475.223328, 537.896729 avg loss, 0.000000 rate, 0.600204 seconds, 3712 images\n",
            "Loaded: 1.599076 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 59: 474.134491, 531.520508 avg loss, 0.000000 rate, 0.684125 seconds, 3776 images\n",
            "Loaded: 1.644385 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 60: 473.873718, 525.755859 avg loss, 0.000000 rate, 0.670677 seconds, 3840 images\n",
            "Resizing\n",
            "416 x 416 \n",
            " try to allocate additional workspace_size = 1302.33 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.000046 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 61: 312.296631, 504.409943 avg loss, 0.000000 rate, 0.458532 seconds, 3904 images\n",
            "Loaded: 3.016520 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 62: 312.695801, 485.238525 avg loss, 0.000000 rate, 0.483408 seconds, 3968 images\n",
            "Loaded: 1.852115 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 63: 311.659424, 467.880615 avg loss, 0.000000 rate, 0.475962 seconds, 4032 images\n",
            "Loaded: 1.632680 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 64: 312.186066, 452.311157 avg loss, 0.000000 rate, 0.480380 seconds, 4096 images\n",
            "Loaded: 1.577102 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 65: 310.956726, 438.175720 avg loss, 0.000000 rate, 0.413071 seconds, 4160 images\n",
            "Loaded: 1.989908 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 66: 309.845367, 425.342682 avg loss, 0.000000 rate, 0.556607 seconds, 4224 images\n",
            "Loaded: 1.969238 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 67: 307.979553, 413.606384 avg loss, 0.000000 rate, 0.579817 seconds, 4288 images\n",
            "Loaded: 1.367694 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 68: 307.504150, 402.996155 avg loss, 0.000000 rate, 0.410641 seconds, 4352 images\n",
            "Loaded: 2.056353 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 69: 307.737030, 393.470245 avg loss, 0.000000 rate, 0.558673 seconds, 4416 images\n",
            "Loaded: 1.378156 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 70: 307.550171, 384.878235 avg loss, 0.000000 rate, 0.435174 seconds, 4480 images\n",
            "Resizing\n",
            "448 x 448 \n",
            " try to allocate additional workspace_size = 1233.65 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.000040 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 71: 353.392273, 381.729645 avg loss, 0.000000 rate, 0.523906 seconds, 4544 images\n",
            "Loaded: 1.677220 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 72: 352.824127, 378.839081 avg loss, 0.000000 rate, 0.555461 seconds, 4608 images\n",
            "Loaded: 1.338114 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 73: 352.716644, 376.226837 avg loss, 0.000000 rate, 0.456643 seconds, 4672 images\n",
            "Loaded: 1.741613 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 74: 350.567444, 373.660889 avg loss, 0.000000 rate, 0.565083 seconds, 4736 images\n",
            "Loaded: 1.385005 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 75: 350.420227, 371.336823 avg loss, 0.000000 rate, 0.478524 seconds, 4800 images\n",
            "Loaded: 1.797489 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 76: 347.180969, 368.921234 avg loss, 0.000000 rate, 0.563479 seconds, 4864 images\n",
            "Loaded: 1.410491 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 77: 345.956299, 366.624756 avg loss, 0.000000 rate, 0.554247 seconds, 4928 images\n",
            "Loaded: 1.619367 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 78: 345.735413, 364.535828 avg loss, 0.000000 rate, 0.513059 seconds, 4992 images\n",
            "Loaded: 1.734981 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 79: 343.407837, 362.423035 avg loss, 0.000000 rate, 0.498396 seconds, 5056 images\n",
            "Loaded: 1.819908 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 80: 342.904907, 360.471222 avg loss, 0.000000 rate, 0.477364 seconds, 5120 images\n",
            "Resizing\n",
            "480 x 480 \n",
            " try to allocate additional workspace_size = 4592.89 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.000169 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 81: 393.277222, 363.751831 avg loss, 0.000000 rate, 0.736718 seconds, 5184 images\n",
            "Loaded: 1.678749 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 82: 390.135498, 366.390198 avg loss, 0.000000 rate, 0.713783 seconds, 5248 images\n",
            "Loaded: 1.463817 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 83: 387.749023, 368.526093 avg loss, 0.000000 rate, 0.591696 seconds, 5312 images\n",
            "Loaded: 1.430064 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 84: 386.679810, 370.341461 avg loss, 0.000000 rate, 0.599209 seconds, 5376 images\n",
            "Loaded: 1.597122 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 85: 384.171906, 371.724518 avg loss, 0.000000 rate, 0.651556 seconds, 5440 images\n",
            "Loaded: 1.336018 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 86: 381.533722, 372.705444 avg loss, 0.000000 rate, 0.672659 seconds, 5504 images\n",
            "Loaded: 1.598045 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 87: 379.828674, 373.417755 avg loss, 0.000000 rate, 0.649858 seconds, 5568 images\n",
            "Loaded: 1.650074 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 88: 375.255127, 373.601501 avg loss, 0.000000 rate, 0.587482 seconds, 5632 images\n",
            "Loaded: 1.396131 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 89: 372.989288, 373.540283 avg loss, 0.000000 rate, 0.626292 seconds, 5696 images\n",
            "Loaded: 1.349940 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 90: 370.384033, 373.224670 avg loss, 0.000000 rate, 0.643334 seconds, 5760 images\n",
            "Resizing\n",
            "352 x 352 \n",
            " try to allocate additional workspace_size = 1264.58 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.114569 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 91: 199.349396, 355.837158 avg loss, 0.000000 rate, 0.342410 seconds, 5824 images\n",
            "Loaded: 1.778012 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 92: 197.500214, 340.003479 avg loss, 0.000000 rate, 0.298675 seconds, 5888 images\n",
            "Loaded: 1.473776 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 93: 196.851196, 325.688263 avg loss, 0.000000 rate, 0.348606 seconds, 5952 images\n",
            "Loaded: 1.530135 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 94: 194.426208, 312.562073 avg loss, 0.000000 rate, 0.281405 seconds, 6016 images\n",
            "Loaded: 1.452226 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 95: 193.714142, 300.677277 avg loss, 0.000000 rate, 0.290045 seconds, 6080 images\n",
            "Loaded: 1.490026 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 96: 190.889038, 289.698456 avg loss, 0.000000 rate, 0.276316 seconds, 6144 images\n",
            "Loaded: 1.517068 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 97: 189.974792, 279.726074 avg loss, 0.000000 rate, 0.295532 seconds, 6208 images\n",
            "Loaded: 1.477115 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 98: 188.040207, 270.557495 avg loss, 0.000000 rate, 0.313189 seconds, 6272 images\n",
            "Loaded: 2.234783 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 99: 186.075348, 262.109283 avg loss, 0.000000 rate, 0.365162 seconds, 6336 images\n",
            "Loaded: 1.432493 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 100: 186.419342, 254.540283 avg loss, 0.000000 rate, 0.321514 seconds, 6400 images\n",
            "Saving weights to /content/gdrive/My Drive/darknet/backup/yolov3-tiny-280120_last.weights\n",
            "Resizing\n",
            "416 x 416 \n",
            " try to allocate additional workspace_size = 1302.33 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.000037 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 101: 256.228790, 254.709137 avg loss, 0.000000 rate, 0.540007 seconds, 6464 images\n",
            "Loaded: 1.360090 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 102: 252.731049, 254.511322 avg loss, 0.000000 rate, 0.504109 seconds, 6528 images\n",
            "Loaded: 1.509376 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 103: 250.786255, 254.138809 avg loss, 0.000000 rate, 0.483274 seconds, 6592 images\n",
            "Loaded: 1.407872 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 104: 247.201660, 253.445099 avg loss, 0.000000 rate, 0.428559 seconds, 6656 images\n",
            "Loaded: 1.473951 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 105: 245.443939, 252.644989 avg loss, 0.000000 rate, 0.413547 seconds, 6720 images\n",
            "Loaded: 1.628504 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 106: 243.501282, 251.730621 avg loss, 0.000000 rate, 0.390252 seconds, 6784 images\n",
            "Loaded: 1.470641 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 107: 240.595795, 250.617142 avg loss, 0.000000 rate, 0.446557 seconds, 6848 images\n",
            "Loaded: 1.883043 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 108: 236.582428, 249.213669 avg loss, 0.000000 rate, 0.429249 seconds, 6912 images\n",
            "Loaded: 1.513858 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 109: 234.758255, 247.768127 avg loss, 0.000000 rate, 0.465125 seconds, 6976 images\n",
            "Loaded: 1.447808 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 110: 231.827042, 246.174011 avg loss, 0.000000 rate, 0.438557 seconds, 7040 images\n",
            "Resizing\n",
            "384 x 384 \n",
            " try to allocate additional workspace_size = 1302.33 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 1.602516 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 111: 195.812347, 241.137848 avg loss, 0.000000 rate, 0.384297 seconds, 7104 images\n",
            "Loaded: 1.398444 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 112: 193.122162, 236.336273 avg loss, 0.000000 rate, 0.362850 seconds, 7168 images\n",
            "Loaded: 1.410595 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 113: 190.385330, 231.741180 avg loss, 0.000000 rate, 0.399232 seconds, 7232 images\n",
            "Loaded: 1.367043 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 114: 187.194794, 227.286545 avg loss, 0.000000 rate, 0.443482 seconds, 7296 images\n",
            "Loaded: 1.382224 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 115: 184.183380, 222.976227 avg loss, 0.000000 rate, 0.397386 seconds, 7360 images\n",
            "Loaded: 1.431349 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 116: 181.208405, 218.799438 avg loss, 0.000000 rate, 0.381681 seconds, 7424 images\n",
            "Loaded: 1.444682 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 117: 178.709778, 214.790466 avg loss, 0.000000 rate, 0.396560 seconds, 7488 images\n",
            "Loaded: 1.440960 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 118: 176.544540, 210.965881 avg loss, 0.000000 rate, 0.360131 seconds, 7552 images\n",
            "Loaded: 1.451918 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 119: 172.545624, 207.123856 avg loss, 0.000000 rate, 0.397357 seconds, 7616 images\n",
            "Loaded: 1.490316 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 120: 170.799408, 203.491409 avg loss, 0.000000 rate, 0.383773 seconds, 7680 images\n",
            "Resizing\n",
            "544 x 544 \n",
            " try to allocate additional workspace_size = 4601.28 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.000067 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 121: 335.781097, 216.720383 avg loss, 0.000000 rate, 0.839842 seconds, 7744 images\n",
            "Loaded: 1.310472 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 122: 331.453430, 228.193695 avg loss, 0.000000 rate, 0.806193 seconds, 7808 images\n",
            "Loaded: 1.374279 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 123: 320.532837, 237.427612 avg loss, 0.000000 rate, 0.813449 seconds, 7872 images\n",
            "Loaded: 1.337104 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 124: 316.300354, 245.314880 avg loss, 0.000000 rate, 0.891126 seconds, 7936 images\n",
            "Loaded: 1.320010 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 125: 306.948151, 251.478210 avg loss, 0.000000 rate, 0.764938 seconds, 8000 images\n",
            "Loaded: 1.343577 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 126: 298.161987, 256.146576 avg loss, 0.000000 rate, 0.895078 seconds, 8064 images\n",
            "Loaded: 1.216762 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 127: 291.344360, 259.666351 avg loss, 0.000000 rate, 0.850528 seconds, 8128 images\n",
            "Loaded: 1.260561 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 128: 282.230621, 261.922791 avg loss, 0.000000 rate, 0.770193 seconds, 8192 images\n",
            "Loaded: 1.272517 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 129: 274.819397, 263.212463 avg loss, 0.000000 rate, 0.771326 seconds, 8256 images\n",
            "Loaded: 2.025423 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 130: 263.094055, 263.200623 avg loss, 0.000000 rate, 0.874287 seconds, 8320 images\n",
            "Resizing\n",
            "352 x 352 \n",
            " try to allocate additional workspace_size = 1264.58 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.345503 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 131: 107.556976, 247.636261 avg loss, 0.000000 rate, 0.347580 seconds, 8384 images\n",
            "Loaded: 1.418920 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 132: 103.415886, 233.214218 avg loss, 0.000000 rate, 0.284989 seconds, 8448 images\n",
            "Loaded: 1.463355 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 133: 99.050568, 219.797852 avg loss, 0.000000 rate, 0.292357 seconds, 8512 images\n",
            "Loaded: 1.456517 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 134: 97.040787, 207.522141 avg loss, 0.000000 rate, 0.281242 seconds, 8576 images\n",
            "Loaded: 1.542962 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 135: 93.921860, 196.162109 avg loss, 0.000000 rate, 0.327195 seconds, 8640 images\n",
            "Loaded: 1.439700 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 136: 90.930832, 185.638977 avg loss, 0.000000 rate, 0.292552 seconds, 8704 images\n",
            "Loaded: 1.478796 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 137: 87.439796, 175.819061 avg loss, 0.000000 rate, 0.291680 seconds, 8768 images\n",
            "Loaded: 1.599292 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 138: 84.539398, 166.691101 avg loss, 0.000000 rate, 0.309860 seconds, 8832 images\n",
            "Loaded: 1.406927 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 139: 81.697937, 158.191788 avg loss, 0.000000 rate, 0.288196 seconds, 8896 images\n",
            "Loaded: 1.475892 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 140: 79.191475, 150.291763 avg loss, 0.000000 rate, 0.367831 seconds, 8960 images\n",
            "Resizing\n",
            "480 x 480 \n",
            " try to allocate additional workspace_size = 4592.89 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.000034 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 141: 140.975891, 149.360168 avg loss, 0.000000 rate, 0.657803 seconds, 9024 images\n",
            "Loaded: 1.391838 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 142: 136.043594, 148.028519 avg loss, 0.000000 rate, 0.691906 seconds, 9088 images\n",
            "Loaded: 1.364663 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 143: 133.580643, 146.583725 avg loss, 0.000000 rate, 0.614289 seconds, 9152 images\n",
            "Loaded: 1.411130 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 144: 127.109695, 144.636322 avg loss, 0.000000 rate, 0.608282 seconds, 9216 images\n",
            "Loaded: 1.501597 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 145: 123.448120, 142.517502 avg loss, 0.000000 rate, 0.645286 seconds, 9280 images\n",
            "Loaded: 1.412992 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 146: 119.179100, 140.183655 avg loss, 0.000000 rate, 0.596954 seconds, 9344 images\n",
            "Loaded: 1.458357 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 147: 112.961075, 137.461395 avg loss, 0.000000 rate, 0.587575 seconds, 9408 images\n",
            "Loaded: 1.370292 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 148: 111.517014, 134.866959 avg loss, 0.000000 rate, 0.619069 seconds, 9472 images\n",
            "Loaded: 1.337996 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 149: 105.639038, 131.944168 avg loss, 0.000000 rate, 0.597946 seconds, 9536 images\n",
            "Loaded: 1.359251 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 150: 103.177505, 129.067505 avg loss, 0.000001 rate, 0.676246 seconds, 9600 images\n",
            "Resizing\n",
            "320 x 320 \n",
            " try to allocate additional workspace_size = 868.75 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.094898 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 151: 44.677765, 120.628532 avg loss, 0.000001 rate, 0.352825 seconds, 9664 images\n",
            "Loaded: 1.361685 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 152: 41.675552, 112.733238 avg loss, 0.000001 rate, 0.253804 seconds, 9728 images\n",
            "Loaded: 1.397506 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 153: 40.715981, 105.531509 avg loss, 0.000001 rate, 0.244427 seconds, 9792 images\n",
            "Loaded: 1.438084 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 154: 39.202660, 98.898621 avg loss, 0.000001 rate, 0.254571 seconds, 9856 images\n",
            "Loaded: 1.404177 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 155: 37.848267, 92.793587 avg loss, 0.000001 rate, 0.249756 seconds, 9920 images\n",
            "Loaded: 1.423412 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 156: 36.192638, 87.133492 avg loss, 0.000001 rate, 0.237857 seconds, 9984 images\n",
            "Loaded: 1.529729 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 157: 34.428520, 81.862991 avg loss, 0.000001 rate, 0.272312 seconds, 10048 images\n",
            "Loaded: 1.484016 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 158: 34.372383, 77.113930 avg loss, 0.000001 rate, 0.235975 seconds, 10112 images\n",
            "Loaded: 1.479829 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 159: 31.984894, 72.601028 avg loss, 0.000001 rate, 0.241779 seconds, 10176 images\n",
            "Loaded: 1.483362 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 160: 30.854395, 68.426369 avg loss, 0.000001 rate, 0.242787 seconds, 10240 images\n",
            "Resizing\n",
            "512 x 512 \n",
            " try to allocate additional workspace_size = 4596.96 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.000041 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 161: 73.086548, 68.892387 avg loss, 0.000001 rate, 0.777610 seconds, 10304 images\n",
            "Loaded: 1.414617 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 162: 73.031265, 69.306274 avg loss, 0.000001 rate, 0.745437 seconds, 10368 images\n",
            "Loaded: 1.256606 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 163: 69.600418, 69.335686 avg loss, 0.000001 rate, 0.679485 seconds, 10432 images\n",
            "Loaded: 1.319968 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 164: 66.845932, 69.086708 avg loss, 0.000001 rate, 0.648931 seconds, 10496 images\n",
            "Loaded: 1.410435 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 165: 64.078712, 68.585907 avg loss, 0.000001 rate, 0.687214 seconds, 10560 images\n",
            "Loaded: 1.363932 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 166: 60.339928, 67.761307 avg loss, 0.000001 rate, 0.693058 seconds, 10624 images\n",
            "Loaded: 1.300935 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 167: 58.832802, 66.868454 avg loss, 0.000001 rate, 0.787394 seconds, 10688 images\n",
            "Loaded: 1.265289 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 168: 55.725044, 65.754112 avg loss, 0.000001 rate, 0.787509 seconds, 10752 images\n",
            "Loaded: 1.282011 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 169: 52.522022, 64.430901 avg loss, 0.000001 rate, 0.706936 seconds, 10816 images\n",
            "Loaded: 1.408671 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 170: 49.894566, 62.977268 avg loss, 0.000001 rate, 0.764345 seconds, 10880 images\n",
            "Resizing\n",
            "576 x 576 \n",
            " try to allocate additional workspace_size = 4605.87 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.000034 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 171: 59.894711, 62.669014 avg loss, 0.000001 rate, 0.873466 seconds, 10944 images\n",
            "Loaded: 1.521555 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 172: 55.857349, 61.987846 avg loss, 0.000001 rate, 0.939539 seconds, 11008 images\n",
            "Loaded: 1.201929 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 173: 53.441647, 61.133224 avg loss, 0.000001 rate, 0.824280 seconds, 11072 images\n",
            "Loaded: 1.378711 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 174: 50.489117, 60.068813 avg loss, 0.000001 rate, 0.996080 seconds, 11136 images\n",
            "Loaded: 1.224603 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 175: 45.340618, 58.595993 avg loss, 0.000001 rate, 0.985470 seconds, 11200 images\n",
            "Loaded: 1.228900 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 176: 45.849915, 57.321384 avg loss, 0.000001 rate, 0.876564 seconds, 11264 images\n",
            "Loaded: 1.341096 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 177: 41.096062, 55.698853 avg loss, 0.000001 rate, 0.868386 seconds, 11328 images\n",
            "Loaded: 1.276215 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 178: 38.356369, 53.964603 avg loss, 0.000001 rate, 0.850505 seconds, 11392 images\n",
            "Loaded: 1.355894 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 179: 35.939392, 52.162083 avg loss, 0.000001 rate, 0.985669 seconds, 11456 images\n",
            "Loaded: 1.233024 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 180: 33.357998, 50.281673 avg loss, 0.000001 rate, 0.830273 seconds, 11520 images\n",
            "Resizing\n",
            "544 x 544 \n",
            " try to allocate additional workspace_size = 4601.28 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 1.466404 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 181: 27.510973, 48.004604 avg loss, 0.000001 rate, 0.847034 seconds, 11584 images\n",
            "Loaded: 1.299581 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 182: 25.433443, 45.747490 avg loss, 0.000001 rate, 0.795271 seconds, 11648 images\n",
            "Loaded: 1.327196 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 183: 24.804390, 43.653179 avg loss, 0.000001 rate, 0.795281 seconds, 11712 images\n",
            "Loaded: 1.368434 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 184: 22.529343, 41.540794 avg loss, 0.000001 rate, 0.819396 seconds, 11776 images\n",
            "Loaded: 1.225323 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 185: 22.043602, 39.591076 avg loss, 0.000001 rate, 0.778969 seconds, 11840 images\n",
            "Loaded: 1.295765 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 186: 20.853031, 37.717270 avg loss, 0.000001 rate, 0.798625 seconds, 11904 images\n",
            "Loaded: 1.372333 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 187: 19.404346, 35.885979 avg loss, 0.000001 rate, 0.832284 seconds, 11968 images\n",
            "Loaded: 1.328359 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 188: 16.660358, 33.963417 avg loss, 0.000001 rate, 0.887719 seconds, 12032 images\n",
            "Loaded: 1.277247 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 189: 17.135553, 32.280632 avg loss, 0.000001 rate, 0.789911 seconds, 12096 images\n",
            "Loaded: 1.321349 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 190: 16.516312, 30.704201 avg loss, 0.000001 rate, 0.781572 seconds, 12160 images\n",
            "Resizing\n",
            "480 x 480 \n",
            " try to allocate additional workspace_size = 4592.89 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 1.402635 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 191: 13.754638, 29.009245 avg loss, 0.000001 rate, 0.675306 seconds, 12224 images\n",
            "Loaded: 1.314242 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 192: 11.230427, 27.231363 avg loss, 0.000001 rate, 0.574504 seconds, 12288 images\n",
            "Loaded: 1.468177 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 193: 11.318522, 25.640079 avg loss, 0.000001 rate, 0.630959 seconds, 12352 images\n",
            "Loaded: 1.348526 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 194: 10.744650, 24.150536 avg loss, 0.000001 rate, 0.583186 seconds, 12416 images\n",
            "Loaded: 1.505398 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 195: 9.671960, 22.702679 avg loss, 0.000001 rate, 0.683126 seconds, 12480 images\n",
            "Loaded: 1.510083 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 196: 9.490746, 21.381485 avg loss, 0.000001 rate, 0.610141 seconds, 12544 images\n",
            "Loaded: 1.396861 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 197: 8.633905, 20.106728 avg loss, 0.000002 rate, 0.677777 seconds, 12608 images\n",
            "Loaded: 1.351364 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 198: 9.715552, 19.067610 avg loss, 0.000002 rate, 0.595807 seconds, 12672 images\n",
            "Loaded: 1.351216 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 199: 9.199223, 18.080770 avg loss, 0.000002 rate, 0.575281 seconds, 12736 images\n",
            "Loaded: 1.398764 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 200: 8.053100, 17.078003 avg loss, 0.000002 rate, 0.627304 seconds, 12800 images\n",
            "Saving weights to /content/gdrive/My Drive/darknet/backup/yolov3-tiny-280120_last.weights\n",
            "Resizing\n",
            "352 x 352 \n",
            " try to allocate additional workspace_size = 1264.58 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.000044 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 201: 5.947679, 15.964971 avg loss, 0.000002 rate, 0.352224 seconds, 12864 images\n",
            "Loaded: 1.446537 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 202: 6.068647, 14.975338 avg loss, 0.000002 rate, 0.291233 seconds, 12928 images\n",
            "Loaded: 1.602470 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 203: 5.892485, 14.067053 avg loss, 0.000002 rate, 0.309643 seconds, 12992 images\n",
            "Loaded: 1.475975 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 204: 5.569901, 13.217338 avg loss, 0.000002 rate, 0.306247 seconds, 13056 images\n",
            "Loaded: 1.483007 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 205: 4.694074, 12.365011 avg loss, 0.000002 rate, 0.301207 seconds, 13120 images\n",
            "Loaded: 1.429250 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 206: 6.158814, 11.744391 avg loss, 0.000002 rate, 0.307353 seconds, 13184 images\n",
            "Loaded: 1.435719 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 207: 4.720222, 11.041974 avg loss, 0.000002 rate, 0.348529 seconds, 13248 images\n",
            "Loaded: 1.477174 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 208: 5.426757, 10.480453 avg loss, 0.000002 rate, 0.296753 seconds, 13312 images\n",
            "Loaded: 1.488051 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 209: 4.826071, 9.915014 avg loss, 0.000002 rate, 0.284183 seconds, 13376 images\n",
            "Loaded: 1.492682 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 210: 5.004823, 9.423995 avg loss, 0.000002 rate, 0.297188 seconds, 13440 images\n",
            "Resizing\n",
            "448 x 448 \n",
            " try to allocate additional workspace_size = 1233.65 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.000045 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 211: 6.074608, 9.089056 avg loss, 0.000002 rate, 0.599412 seconds, 13504 images\n",
            "Loaded: 1.455711 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 212: 5.437471, 8.723898 avg loss, 0.000002 rate, 0.475770 seconds, 13568 images\n",
            "Loaded: 1.584593 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 213: 5.746700, 8.426178 avg loss, 0.000002 rate, 0.466892 seconds, 13632 images\n",
            "Loaded: 1.464120 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 214: 5.085042, 8.092065 avg loss, 0.000002 rate, 0.490108 seconds, 13696 images\n",
            "Loaded: 1.414019 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 215: 4.903989, 7.773257 avg loss, 0.000002 rate, 0.501858 seconds, 13760 images\n",
            "Loaded: 1.427954 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 216: 5.437892, 7.539721 avg loss, 0.000002 rate, 0.469602 seconds, 13824 images\n",
            "Loaded: 1.402984 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 217: 4.967233, 7.282472 avg loss, 0.000002 rate, 0.473137 seconds, 13888 images\n",
            "Loaded: 1.380377 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 218: 5.154804, 7.069705 avg loss, 0.000002 rate, 0.486196 seconds, 13952 images\n",
            "Loaded: 1.472979 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 219: 4.810050, 6.843740 avg loss, 0.000002 rate, 0.560773 seconds, 14016 images\n",
            "Loaded: 1.443448 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 220: 4.724873, 6.631853 avg loss, 0.000002 rate, 0.485937 seconds, 14080 images\n",
            "Resizing\n",
            "352 x 352 \n",
            " try to allocate additional workspace_size = 1264.58 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 1.543047 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 221: 3.753924, 6.344060 avg loss, 0.000002 rate, 0.362742 seconds, 14144 images\n",
            "Loaded: 1.462928 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 222: 3.820870, 6.091741 avg loss, 0.000002 rate, 0.295713 seconds, 14208 images\n",
            "Loaded: 1.650027 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 223: 4.210415, 5.903608 avg loss, 0.000002 rate, 0.304619 seconds, 14272 images\n",
            "Loaded: 1.491902 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 224: 3.820185, 5.695266 avg loss, 0.000003 rate, 0.350538 seconds, 14336 images\n",
            "Loaded: 1.546216 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 225: 4.620812, 5.587821 avg loss, 0.000003 rate, 0.282339 seconds, 14400 images\n",
            "Loaded: 1.542523 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 226: 3.953058, 5.424344 avg loss, 0.000003 rate, 0.310343 seconds, 14464 images\n",
            "Loaded: 1.460556 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 227: 4.250351, 5.306945 avg loss, 0.000003 rate, 0.287841 seconds, 14528 images\n",
            "Loaded: 1.446608 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 228: 3.618551, 5.138105 avg loss, 0.000003 rate, 0.305165 seconds, 14592 images\n",
            "Loaded: 1.420609 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 229: 4.687359, 5.093031 avg loss, 0.000003 rate, 0.281633 seconds, 14656 images\n",
            "Loaded: 1.477541 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 230: 3.360617, 4.919789 avg loss, 0.000003 rate, 0.290895 seconds, 14720 images\n",
            "Resizing\n",
            "512 x 512 \n",
            " try to allocate additional workspace_size = 4596.96 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.000102 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 231: 4.574306, 4.885241 avg loss, 0.000003 rate, 0.830779 seconds, 14784 images\n",
            "Loaded: 1.220294 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 232: 5.160087, 4.912725 avg loss, 0.000003 rate, 0.648602 seconds, 14848 images\n",
            "Loaded: 1.423724 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 233: 4.285843, 4.850037 avg loss, 0.000003 rate, 0.699762 seconds, 14912 images\n",
            "Loaded: 1.310029 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 234: 3.984356, 4.763469 avg loss, 0.000003 rate, 0.700832 seconds, 14976 images\n",
            "Loaded: 1.374628 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 235: 4.427482, 4.729870 avg loss, 0.000003 rate, 0.686589 seconds, 15040 images\n",
            "Loaded: 1.385045 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 236: 4.517390, 4.708622 avg loss, 0.000003 rate, 0.730416 seconds, 15104 images\n",
            "Loaded: 1.310108 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 237: 4.859230, 4.723683 avg loss, 0.000003 rate, 0.686467 seconds, 15168 images\n",
            "Loaded: 1.322485 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 238: 3.653666, 4.616682 avg loss, 0.000003 rate, 0.800959 seconds, 15232 images\n",
            "Loaded: 1.373800 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 239: 4.323945, 4.587408 avg loss, 0.000003 rate, 0.762820 seconds, 15296 images\n",
            "Loaded: 1.455887 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 240: 4.094077, 4.538075 avg loss, 0.000003 rate, 0.664735 seconds, 15360 images\n",
            "Resizing\n",
            "608 x 608 \n",
            " try to allocate additional workspace_size = 4610.72 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.000082 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 241: 3.821600, 4.466427 avg loss, 0.000003 rate, 1.096936 seconds, 15424 images\n",
            "Loaded: 1.295232 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 242: 4.746751, 4.494460 avg loss, 0.000003 rate, 1.129396 seconds, 15488 images\n",
            "Loaded: 1.160308 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 243: 4.636013, 4.508615 avg loss, 0.000003 rate, 1.109906 seconds, 15552 images\n",
            "Loaded: 1.277470 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 244: 4.376876, 4.495441 avg loss, 0.000004 rate, 1.182931 seconds, 15616 images\n",
            "Loaded: 1.079028 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 245: 4.434317, 4.489328 avg loss, 0.000004 rate, 0.997532 seconds, 15680 images\n",
            "Loaded: 1.331943 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 246: 4.469316, 4.487327 avg loss, 0.000004 rate, 1.094457 seconds, 15744 images\n",
            "Loaded: 1.297389 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 247: 3.936828, 4.432277 avg loss, 0.000004 rate, 1.021787 seconds, 15808 images\n",
            "Loaded: 1.320019 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 248: 5.106030, 4.499652 avg loss, 0.000004 rate, 1.170807 seconds, 15872 images\n",
            "Loaded: 1.152563 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 249: 3.846297, 4.434317 avg loss, 0.000004 rate, 1.027481 seconds, 15936 images\n",
            "Loaded: 1.280648 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 250: 3.356683, 4.326553 avg loss, 0.000004 rate, 0.999143 seconds, 16000 images\n",
            "Resizing\n",
            "448 x 448 \n",
            " try to allocate additional workspace_size = 1233.65 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 1.426767 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 251: 3.363453, 4.230243 avg loss, 0.000004 rate, 0.495492 seconds, 16064 images\n",
            "Loaded: 1.440385 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 252: 3.066769, 4.113896 avg loss, 0.000004 rate, 0.562148 seconds, 16128 images\n",
            "Loaded: 1.401153 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 253: 3.074399, 4.009946 avg loss, 0.000004 rate, 0.477388 seconds, 16192 images\n",
            "Loaded: 1.490699 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 254: 3.402477, 3.949199 avg loss, 0.000004 rate, 0.484997 seconds, 16256 images\n",
            "Loaded: 1.434533 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 255: 3.507218, 3.905001 avg loss, 0.000004 rate, 0.485884 seconds, 16320 images\n",
            "Loaded: 1.456860 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 256: 3.543211, 3.868822 avg loss, 0.000004 rate, 0.492697 seconds, 16384 images\n",
            "Loaded: 1.515796 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 257: 3.262298, 3.808170 avg loss, 0.000004 rate, 0.485703 seconds, 16448 images\n",
            "Loaded: 1.522690 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 258: 3.471565, 3.774509 avg loss, 0.000004 rate, 0.487702 seconds, 16512 images\n",
            "Loaded: 1.415760 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 259: 3.250170, 3.722075 avg loss, 0.000004 rate, 0.593584 seconds, 16576 images\n",
            "Loaded: 1.401749 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 260: 2.432346, 3.593102 avg loss, 0.000005 rate, 0.462947 seconds, 16640 images\n",
            "Resizing\n",
            "448 x 448 \n",
            " try to allocate additional workspace_size = 1233.65 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 1.547237 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 261: 3.266347, 3.560427 avg loss, 0.000005 rate, 0.532825 seconds, 16704 images\n",
            "Loaded: 1.421028 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 262: 3.156869, 3.520071 avg loss, 0.000005 rate, 0.514113 seconds, 16768 images\n",
            "Loaded: 1.533777 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 263: 3.413139, 3.509378 avg loss, 0.000005 rate, 0.492743 seconds, 16832 images\n",
            "Loaded: 1.493588 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 264: 3.235967, 3.482037 avg loss, 0.000005 rate, 0.485699 seconds, 16896 images\n",
            "Loaded: 1.338818 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 265: 2.989238, 3.432757 avg loss, 0.000005 rate, 0.475405 seconds, 16960 images\n",
            "Loaded: 1.453848 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 266: 2.944686, 3.383950 avg loss, 0.000005 rate, 0.550549 seconds, 17024 images\n",
            "Loaded: 1.389228 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 267: 3.035338, 3.349089 avg loss, 0.000005 rate, 0.526978 seconds, 17088 images\n",
            "Loaded: 1.378522 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 268: 2.876928, 3.301872 avg loss, 0.000005 rate, 0.545993 seconds, 17152 images\n",
            "Loaded: 1.456471 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 269: 2.744304, 3.246116 avg loss, 0.000005 rate, 0.516734 seconds, 17216 images\n",
            "Loaded: 1.425303 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 270: 2.878726, 3.209377 avg loss, 0.000005 rate, 0.576483 seconds, 17280 images\n",
            "Resizing\n",
            "544 x 544 \n",
            " try to allocate additional workspace_size = 4601.28 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.000046 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 271: 3.151203, 3.203559 avg loss, 0.000005 rate, 0.857920 seconds, 17344 images\n",
            "Loaded: 1.336220 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 272: 2.617483, 3.144952 avg loss, 0.000005 rate, 0.801702 seconds, 17408 images\n",
            "Loaded: 1.423459 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 273: 2.834327, 3.113889 avg loss, 0.000006 rate, 0.841684 seconds, 17472 images\n",
            "Loaded: 1.385129 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 274: 2.646565, 3.067157 avg loss, 0.000006 rate, 0.800986 seconds, 17536 images\n",
            "Loaded: 1.395351 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 275: 2.989301, 3.059371 avg loss, 0.000006 rate, 0.755675 seconds, 17600 images\n",
            "Loaded: 1.339953 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 276: 2.534029, 3.006837 avg loss, 0.000006 rate, 0.899385 seconds, 17664 images\n",
            "Loaded: 1.213010 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 277: 3.405139, 3.046667 avg loss, 0.000006 rate, 0.778772 seconds, 17728 images\n",
            "Loaded: 1.378109 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 278: 3.038810, 3.045881 avg loss, 0.000006 rate, 0.834201 seconds, 17792 images\n",
            "Loaded: 1.311392 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 279: 3.145934, 3.055887 avg loss, 0.000006 rate, 0.873700 seconds, 17856 images\n",
            "Loaded: 1.220317 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 280: 2.495452, 2.999843 avg loss, 0.000006 rate, 0.793138 seconds, 17920 images\n",
            "Resizing\n",
            "512 x 512 \n",
            " try to allocate additional workspace_size = 4596.96 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 1.367514 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 281: 3.254068, 3.025266 avg loss, 0.000006 rate, 0.759023 seconds, 17984 images\n",
            "Loaded: 1.220887 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 282: 2.816118, 3.004351 avg loss, 0.000006 rate, 0.729110 seconds, 18048 images\n",
            "Loaded: 1.322648 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 283: 2.560936, 2.960009 avg loss, 0.000006 rate, 0.653153 seconds, 18112 images\n",
            "Loaded: 1.398802 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 284: 2.753782, 2.939387 avg loss, 0.000007 rate, 0.671893 seconds, 18176 images\n",
            "Loaded: 1.335043 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 285: 2.795713, 2.925019 avg loss, 0.000007 rate, 0.759895 seconds, 18240 images\n",
            "Loaded: 1.317124 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 286: 2.874128, 2.919930 avg loss, 0.000007 rate, 0.755652 seconds, 18304 images\n",
            "Loaded: 1.316114 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 287: 2.692554, 2.897193 avg loss, 0.000007 rate, 0.710808 seconds, 18368 images\n",
            "Loaded: 1.290874 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 288: 3.312035, 2.938677 avg loss, 0.000007 rate, 0.654784 seconds, 18432 images\n",
            "Loaded: 1.513356 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 289: 3.059658, 2.950775 avg loss, 0.000007 rate, 0.762754 seconds, 18496 images\n",
            "Loaded: 1.407782 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 290: 3.235487, 2.979246 avg loss, 0.000007 rate, 0.758185 seconds, 18560 images\n",
            "Resizing\n",
            "576 x 576 \n",
            " try to allocate additional workspace_size = 4605.87 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.000038 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 291: 2.414440, 2.922765 avg loss, 0.000007 rate, 0.993861 seconds, 18624 images\n",
            "Loaded: 1.181536 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 292: 2.504085, 2.880898 avg loss, 0.000007 rate, 0.969190 seconds, 18688 images\n",
            "Loaded: 1.228985 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 293: 2.431813, 2.835989 avg loss, 0.000007 rate, 0.852938 seconds, 18752 images\n",
            "Loaded: 1.352214 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 294: 2.605023, 2.812892 avg loss, 0.000007 rate, 0.851535 seconds, 18816 images\n",
            "Loaded: 1.356815 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 295: 2.303629, 2.761966 avg loss, 0.000008 rate, 0.826323 seconds, 18880 images\n",
            "Loaded: 1.296367 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 296: 2.642524, 2.750022 avg loss, 0.000008 rate, 0.932752 seconds, 18944 images\n",
            "Loaded: 1.219792 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 297: 2.977116, 2.772731 avg loss, 0.000008 rate, 0.871671 seconds, 19008 images\n",
            "Loaded: 1.343049 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 298: 2.453640, 2.740822 avg loss, 0.000008 rate, 0.927357 seconds, 19072 images\n",
            "Loaded: 1.485609 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 299: 2.893023, 2.756042 avg loss, 0.000008 rate, 0.842938 seconds, 19136 images\n",
            "Loaded: 1.417660 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 300: 2.406174, 2.721056 avg loss, 0.000008 rate, 0.995464 seconds, 19200 images\n",
            "Saving weights to /content/gdrive/My Drive/darknet/backup/yolov3-tiny-280120_last.weights\n",
            "Resizing\n",
            "544 x 544 \n",
            " try to allocate additional workspace_size = 4601.28 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 1.333579 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 301: 2.579312, 2.706881 avg loss, 0.000008 rate, 0.921242 seconds, 19264 images\n",
            "Loaded: 1.236491 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 302: 2.517684, 2.687961 avg loss, 0.000008 rate, 0.812036 seconds, 19328 images\n",
            "Loaded: 1.437304 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 303: 2.727477, 2.691913 avg loss, 0.000008 rate, 0.831612 seconds, 19392 images\n",
            "Loaded: 1.341142 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 304: 2.378014, 2.660523 avg loss, 0.000009 rate, 0.805989 seconds, 19456 images\n",
            "Loaded: 1.353068 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 305: 2.419969, 2.636467 avg loss, 0.000009 rate, 0.868310 seconds, 19520 images\n",
            "Loaded: 1.218308 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 306: 2.622175, 2.635038 avg loss, 0.000009 rate, 0.750729 seconds, 19584 images\n",
            "Loaded: 1.350335 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 307: 2.362414, 2.607776 avg loss, 0.000009 rate, 0.786397 seconds, 19648 images\n",
            "Loaded: 1.288324 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 308: 2.274243, 2.574422 avg loss, 0.000009 rate, 0.839210 seconds, 19712 images\n",
            "Loaded: 1.319274 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 309: 2.551502, 2.572130 avg loss, 0.000009 rate, 0.890487 seconds, 19776 images\n",
            "Loaded: 1.274284 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 310: 2.656140, 2.580531 avg loss, 0.000009 rate, 0.814144 seconds, 19840 images\n",
            "Resizing\n",
            "576 x 576 \n",
            " try to allocate additional workspace_size = 4605.87 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.000040 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 311: 2.863600, 2.608838 avg loss, 0.000009 rate, 0.859832 seconds, 19904 images\n",
            "Loaded: 1.310302 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 312: 2.379295, 2.585884 avg loss, 0.000009 rate, 0.922667 seconds, 19968 images\n",
            "Loaded: 1.371058 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 313: 2.438044, 2.571100 avg loss, 0.000010 rate, 0.837874 seconds, 20032 images\n",
            "Loaded: 1.366900 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 314: 2.226201, 2.536610 avg loss, 0.000010 rate, 0.918836 seconds, 20096 images\n",
            "Loaded: 1.252054 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 315: 2.333463, 2.516295 avg loss, 0.000010 rate, 0.944939 seconds, 20160 images\n",
            "Loaded: 1.194576 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 316: 2.604107, 2.525077 avg loss, 0.000010 rate, 0.926023 seconds, 20224 images\n",
            "Loaded: 1.349528 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 317: 2.428893, 2.515458 avg loss, 0.000010 rate, 0.977126 seconds, 20288 images\n",
            "Loaded: 1.174137 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 318: 2.297438, 2.493656 avg loss, 0.000010 rate, 0.879428 seconds, 20352 images\n",
            "Loaded: 1.353831 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 319: 2.752192, 2.519510 avg loss, 0.000010 rate, 0.991643 seconds, 20416 images\n",
            "Loaded: 1.170936 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 320: 2.091498, 2.476709 avg loss, 0.000010 rate, 0.896760 seconds, 20480 images\n",
            "Resizing\n",
            "416 x 416 \n",
            " try to allocate additional workspace_size = 1302.33 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.000045 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 321: 2.358411, 2.464879 avg loss, 0.000011 rate, 0.461626 seconds, 20544 images\n",
            "Loaded: 1.454756 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 322: 2.637408, 2.482132 avg loss, 0.000011 rate, 0.444147 seconds, 20608 images\n",
            "Loaded: 1.486181 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 323: 2.844647, 2.518383 avg loss, 0.000011 rate, 0.513716 seconds, 20672 images\n",
            "Loaded: 1.507714 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 324: 2.548741, 2.521419 avg loss, 0.000011 rate, 0.419843 seconds, 20736 images\n",
            "Loaded: 1.454887 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 325: 2.329662, 2.502243 avg loss, 0.000011 rate, 0.431830 seconds, 20800 images\n",
            "Loaded: 1.397250 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 326: 2.911537, 2.543173 avg loss, 0.000011 rate, 0.447395 seconds, 20864 images\n",
            "Loaded: 1.345267 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 327: 2.692223, 2.558078 avg loss, 0.000011 rate, 0.429640 seconds, 20928 images\n",
            "Loaded: 1.475843 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 328: 2.151650, 2.517435 avg loss, 0.000012 rate, 0.508823 seconds, 20992 images\n",
            "Loaded: 1.393016 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 329: 2.850770, 2.550768 avg loss, 0.000012 rate, 0.470828 seconds, 21056 images\n",
            "Loaded: 1.372689 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 330: 2.225505, 2.518242 avg loss, 0.000012 rate, 0.488873 seconds, 21120 images\n",
            "Resizing\n",
            "352 x 352 \n",
            " try to allocate additional workspace_size = 1264.58 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 1.520242 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 331: 2.338006, 2.500218 avg loss, 0.000012 rate, 0.339834 seconds, 21184 images\n",
            "Loaded: 1.371286 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 332: 2.939940, 2.544191 avg loss, 0.000012 rate, 0.288743 seconds, 21248 images\n",
            "Loaded: 1.419473 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 333: 2.376429, 2.527415 avg loss, 0.000012 rate, 0.315828 seconds, 21312 images\n",
            "Loaded: 1.422778 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 334: 2.403620, 2.515035 avg loss, 0.000012 rate, 0.292789 seconds, 21376 images\n",
            "Loaded: 1.424491 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 335: 2.054242, 2.468956 avg loss, 0.000013 rate, 0.306617 seconds, 21440 images\n",
            "Loaded: 1.496967 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 336: 2.391765, 2.461237 avg loss, 0.000013 rate, 0.291442 seconds, 21504 images\n",
            "Loaded: 1.515507 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 337: 1.863254, 2.401439 avg loss, 0.000013 rate, 0.324690 seconds, 21568 images\n",
            "Loaded: 1.453891 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 338: 2.532939, 2.414589 avg loss, 0.000013 rate, 0.288101 seconds, 21632 images\n",
            "Loaded: 1.448213 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 339: 2.388562, 2.411986 avg loss, 0.000013 rate, 0.298620 seconds, 21696 images\n",
            "Loaded: 1.512332 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 340: 2.334201, 2.404208 avg loss, 0.000013 rate, 0.296824 seconds, 21760 images\n",
            "Resizing\n",
            "512 x 512 \n",
            " try to allocate additional workspace_size = 4596.96 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.000050 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 341: 2.519077, 2.415695 avg loss, 0.000014 rate, 0.724049 seconds, 21824 images\n",
            "Loaded: 1.340190 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 342: 2.149240, 2.389049 avg loss, 0.000014 rate, 0.644626 seconds, 21888 images\n",
            "Loaded: 1.415663 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 343: 2.411974, 2.391342 avg loss, 0.000014 rate, 0.661992 seconds, 21952 images\n",
            "Loaded: 1.352709 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 344: 2.705692, 2.422777 avg loss, 0.000014 rate, 0.662217 seconds, 22016 images\n",
            "Loaded: 1.311209 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 345: 3.220876, 2.502587 avg loss, 0.000014 rate, 0.722271 seconds, 22080 images\n",
            "Loaded: 1.257509 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 346: 2.501134, 2.502441 avg loss, 0.000014 rate, 0.633445 seconds, 22144 images\n",
            "Loaded: 1.462370 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 347: 2.148826, 2.467080 avg loss, 0.000014 rate, 0.638976 seconds, 22208 images\n",
            "Loaded: 1.368750 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 348: 2.133109, 2.433683 avg loss, 0.000015 rate, 0.645563 seconds, 22272 images\n",
            "Loaded: 1.448454 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 349: 2.356220, 2.425936 avg loss, 0.000015 rate, 0.683996 seconds, 22336 images\n",
            "Loaded: 1.359157 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 350: 2.308205, 2.414163 avg loss, 0.000015 rate, 0.684434 seconds, 22400 images\n",
            "Resizing\n",
            "352 x 352 \n",
            " try to allocate additional workspace_size = 1264.58 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.000036 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 351: 2.126069, 2.385354 avg loss, 0.000015 rate, 0.379819 seconds, 22464 images\n",
            "Loaded: 1.407845 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 352: 2.000433, 2.346862 avg loss, 0.000015 rate, 0.301352 seconds, 22528 images\n",
            "Loaded: 1.457468 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 353: 2.227867, 2.334962 avg loss, 0.000016 rate, 0.299786 seconds, 22592 images\n",
            "Loaded: 1.406073 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 354: 2.130363, 2.314502 avg loss, 0.000016 rate, 0.294953 seconds, 22656 images\n",
            "Loaded: 1.410771 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 355: 2.098092, 2.292861 avg loss, 0.000016 rate, 0.314204 seconds, 22720 images\n",
            "Loaded: 1.433065 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 356: 2.163815, 2.279957 avg loss, 0.000016 rate, 0.272983 seconds, 22784 images\n",
            "Loaded: 1.449281 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 357: 2.188117, 2.270773 avg loss, 0.000016 rate, 0.308254 seconds, 22848 images\n",
            "Loaded: 1.390618 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 358: 2.216533, 2.265349 avg loss, 0.000016 rate, 0.294281 seconds, 22912 images\n",
            "Loaded: 1.418289 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 359: 1.832631, 2.222077 avg loss, 0.000017 rate, 0.304858 seconds, 22976 images\n",
            "Loaded: 1.438059 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 360: 2.666635, 2.266533 avg loss, 0.000017 rate, 0.288965 seconds, 23040 images\n",
            "Resizing\n",
            "352 x 352 \n",
            " try to allocate additional workspace_size = 1264.58 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 1.505178 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 361: 1.784407, 2.218320 avg loss, 0.000017 rate, 0.327795 seconds, 23104 images\n",
            "Loaded: 1.356948 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 362: 2.755226, 2.272011 avg loss, 0.000017 rate, 0.322888 seconds, 23168 images\n",
            "Loaded: 1.462884 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 363: 2.657360, 2.310546 avg loss, 0.000017 rate, 0.293649 seconds, 23232 images\n",
            "Loaded: 1.416413 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 364: 2.104586, 2.289950 avg loss, 0.000018 rate, 0.318552 seconds, 23296 images\n",
            "Loaded: 1.394787 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 365: 2.232408, 2.284196 avg loss, 0.000018 rate, 0.287669 seconds, 23360 images\n",
            "Loaded: 1.460270 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 366: 1.858503, 2.241626 avg loss, 0.000018 rate, 0.302384 seconds, 23424 images\n",
            "Loaded: 1.462551 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 367: 2.787530, 2.296217 avg loss, 0.000018 rate, 0.291896 seconds, 23488 images\n",
            "Loaded: 1.433556 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 368: 2.564319, 2.323027 avg loss, 0.000018 rate, 0.331398 seconds, 23552 images\n",
            "Loaded: 1.528080 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 369: 1.752713, 2.265996 avg loss, 0.000019 rate, 0.301438 seconds, 23616 images\n",
            "Loaded: 1.525796 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 370: 2.216081, 2.261004 avg loss, 0.000019 rate, 0.304037 seconds, 23680 images\n",
            "Resizing\n",
            "576 x 576 \n",
            " try to allocate additional workspace_size = 4605.87 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.000038 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 371: 2.097346, 2.244638 avg loss, 0.000019 rate, 1.047243 seconds, 23744 images\n",
            "Loaded: 1.138933 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 372: 1.707870, 2.190961 avg loss, 0.000019 rate, 0.838389 seconds, 23808 images\n",
            "Loaded: 1.347370 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 373: 2.430354, 2.214900 avg loss, 0.000019 rate, 0.981290 seconds, 23872 images\n",
            "Loaded: 1.230591 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 374: 2.625539, 2.255964 avg loss, 0.000020 rate, 0.884524 seconds, 23936 images\n",
            "Loaded: 1.383512 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 375: 1.877532, 2.218121 avg loss, 0.000020 rate, 0.988389 seconds, 24000 images\n",
            "Loaded: 1.243909 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 376: 1.937032, 2.190012 avg loss, 0.000020 rate, 0.907704 seconds, 24064 images\n",
            "Loaded: 1.276588 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 377: 2.535092, 2.224520 avg loss, 0.000020 rate, 0.917210 seconds, 24128 images\n",
            "Loaded: 1.493333 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 378: 2.040340, 2.206102 avg loss, 0.000020 rate, 0.926766 seconds, 24192 images\n",
            "Loaded: 1.426912 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 379: 1.973097, 2.182802 avg loss, 0.000021 rate, 0.984083 seconds, 24256 images\n",
            "Loaded: 1.180364 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 380: 2.462517, 2.210773 avg loss, 0.000021 rate, 0.846287 seconds, 24320 images\n",
            "Resizing\n",
            "320 x 320 \n",
            " try to allocate additional workspace_size = 868.75 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.101444 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 381: 2.050554, 2.194751 avg loss, 0.000021 rate, 0.319667 seconds, 24384 images\n",
            "Loaded: 1.379187 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 382: 1.866016, 2.161878 avg loss, 0.000021 rate, 0.280138 seconds, 24448 images\n",
            "Loaded: 1.368446 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 383: 2.572593, 2.202949 avg loss, 0.000022 rate, 0.232886 seconds, 24512 images\n",
            "Loaded: 1.492135 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 384: 1.853962, 2.168050 avg loss, 0.000022 rate, 0.275715 seconds, 24576 images\n",
            "Loaded: 1.433703 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 385: 2.639635, 2.215209 avg loss, 0.000022 rate, 0.252122 seconds, 24640 images\n",
            "Loaded: 1.427021 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 386: 2.080536, 2.201741 avg loss, 0.000022 rate, 0.251488 seconds, 24704 images\n",
            "Loaded: 1.410659 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 387: 1.976492, 2.179217 avg loss, 0.000022 rate, 0.250264 seconds, 24768 images\n",
            "Loaded: 1.378673 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 388: 2.130847, 2.174380 avg loss, 0.000023 rate, 0.277142 seconds, 24832 images\n",
            "Loaded: 1.374355 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 389: 2.355719, 2.192513 avg loss, 0.000023 rate, 0.258540 seconds, 24896 images\n",
            "Loaded: 1.444808 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 390: 2.264978, 2.199760 avg loss, 0.000023 rate, 0.245821 seconds, 24960 images\n",
            "Resizing\n",
            "608 x 608 \n",
            " try to allocate additional workspace_size = 4610.72 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.000061 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 391: 2.029325, 2.182717 avg loss, 0.000023 rate, 1.367741 seconds, 25024 images\n",
            "Loaded: 0.968149 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 392: 1.601038, 2.124549 avg loss, 0.000024 rate, 1.049823 seconds, 25088 images\n",
            "Loaded: 1.440110 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 393: 2.051329, 2.117227 avg loss, 0.000024 rate, 0.979394 seconds, 25152 images\n",
            "Loaded: 1.506703 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 394: 2.100658, 2.115570 avg loss, 0.000024 rate, 0.971183 seconds, 25216 images\n",
            "Loaded: 1.289995 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 395: 1.920057, 2.096019 avg loss, 0.000024 rate, 1.105609 seconds, 25280 images\n",
            "Loaded: 1.141311 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 396: 1.808920, 2.067309 avg loss, 0.000025 rate, 1.047581 seconds, 25344 images\n",
            "Loaded: 1.250945 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 397: 2.247141, 2.085292 avg loss, 0.000025 rate, 1.075661 seconds, 25408 images\n",
            "Loaded: 1.221428 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 398: 1.953443, 2.072107 avg loss, 0.000025 rate, 1.184257 seconds, 25472 images\n",
            "Loaded: 1.110427 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 399: 2.662074, 2.131104 avg loss, 0.000025 rate, 1.037068 seconds, 25536 images\n",
            "Loaded: 1.231795 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 400: 2.342761, 2.152270 avg loss, 0.000026 rate, 1.069781 seconds, 25600 images\n",
            "Saving weights to /content/gdrive/My Drive/darknet/backup/yolov3-tiny-280120_last.weights\n",
            "Resizing\n",
            "576 x 576 \n",
            " try to allocate additional workspace_size = 4605.87 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 1.451285 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 401: 2.251994, 2.162242 avg loss, 0.000026 rate, 0.923264 seconds, 25664 images\n",
            "Loaded: 1.386489 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 402: 1.849866, 2.131005 avg loss, 0.000026 rate, 0.840456 seconds, 25728 images\n",
            "Loaded: 1.386507 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 403: 1.725871, 2.090491 avg loss, 0.000026 rate, 0.874295 seconds, 25792 images\n",
            "Loaded: 1.455924 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 404: 2.080150, 2.089457 avg loss, 0.000027 rate, 0.905724 seconds, 25856 images\n",
            "Loaded: 1.359027 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 405: 2.049057, 2.085417 avg loss, 0.000027 rate, 1.003242 seconds, 25920 images\n",
            "Loaded: 1.140524 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 406: 2.318424, 2.108718 avg loss, 0.000027 rate, 0.884865 seconds, 25984 images\n",
            "Loaded: 1.345899 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 407: 2.085835, 2.106430 avg loss, 0.000027 rate, 0.912108 seconds, 26048 images\n",
            "Loaded: 1.356087 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 408: 2.058300, 2.101617 avg loss, 0.000028 rate, 0.919124 seconds, 26112 images\n",
            "Loaded: 1.299730 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 409: 1.996781, 2.091133 avg loss, 0.000028 rate, 0.979097 seconds, 26176 images\n",
            "Loaded: 1.249804 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 410: 1.577245, 2.039744 avg loss, 0.000028 rate, 0.987714 seconds, 26240 images\n",
            "Resizing\n",
            "512 x 512 \n",
            " try to allocate additional workspace_size = 4596.96 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 1.463312 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 411: 1.613497, 1.997120 avg loss, 0.000029 rate, 0.755194 seconds, 26304 images\n",
            "Loaded: 1.317176 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 412: 2.099850, 2.007393 avg loss, 0.000029 rate, 0.749055 seconds, 26368 images\n",
            "Loaded: 1.385895 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 413: 1.889277, 1.995581 avg loss, 0.000029 rate, 0.745739 seconds, 26432 images\n",
            "Loaded: 1.337521 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 414: 1.836777, 1.979701 avg loss, 0.000029 rate, 0.677089 seconds, 26496 images\n",
            "Loaded: 1.484320 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 415: 1.737947, 1.955525 avg loss, 0.000030 rate, 0.680664 seconds, 26560 images\n",
            "Loaded: 1.352353 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 416: 1.943416, 1.954314 avg loss, 0.000030 rate, 0.684166 seconds, 26624 images\n",
            "Loaded: 1.362720 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 417: 2.172250, 1.976108 avg loss, 0.000030 rate, 0.668177 seconds, 26688 images\n",
            "Loaded: 1.478695 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 418: 2.107586, 1.989256 avg loss, 0.000031 rate, 0.819986 seconds, 26752 images\n",
            "Loaded: 1.288107 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 419: 1.964154, 1.986746 avg loss, 0.000031 rate, 0.662816 seconds, 26816 images\n",
            "Loaded: 1.413607 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 420: 1.804639, 1.968535 avg loss, 0.000031 rate, 0.687762 seconds, 26880 images\n",
            "Resizing\n",
            "608 x 608 \n",
            " try to allocate additional workspace_size = 4610.72 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.000038 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 421: 2.267081, 1.998389 avg loss, 0.000031 rate, 1.030830 seconds, 26944 images\n",
            "Loaded: 1.165707 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 422: 2.138356, 2.012386 avg loss, 0.000032 rate, 0.998091 seconds, 27008 images\n",
            "Loaded: 1.309825 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 423: 2.066453, 2.017793 avg loss, 0.000032 rate, 1.094451 seconds, 27072 images\n",
            "Loaded: 1.164816 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 424: 1.864994, 2.002513 avg loss, 0.000032 rate, 1.065450 seconds, 27136 images\n",
            "Loaded: 1.152952 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 425: 1.973420, 1.999604 avg loss, 0.000033 rate, 1.151352 seconds, 27200 images\n",
            "Loaded: 1.210271 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 426: 1.834186, 1.983062 avg loss, 0.000033 rate, 1.133187 seconds, 27264 images\n",
            "Loaded: 1.281685 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 427: 2.023579, 1.987114 avg loss, 0.000033 rate, 0.973846 seconds, 27328 images\n",
            "Loaded: 1.447693 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 428: 1.970372, 1.985440 avg loss, 0.000034 rate, 1.144323 seconds, 27392 images\n",
            "Loaded: 1.110667 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 429: 1.889091, 1.975805 avg loss, 0.000034 rate, 1.036874 seconds, 27456 images\n",
            "Loaded: 1.162831 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 430: 1.917141, 1.969938 avg loss, 0.000034 rate, 1.148046 seconds, 27520 images\n",
            "Resizing\n",
            "480 x 480 \n",
            " try to allocate additional workspace_size = 4592.89 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 1.316127 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 431: 2.533470, 2.026291 avg loss, 0.000035 rate, 0.646048 seconds, 27584 images\n",
            "Loaded: 1.275727 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 432: 2.347295, 2.058392 avg loss, 0.000035 rate, 0.576946 seconds, 27648 images\n",
            "Loaded: 1.365337 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 433: 2.154514, 2.068004 avg loss, 0.000035 rate, 0.664497 seconds, 27712 images\n",
            "Loaded: 1.350527 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 434: 2.289302, 2.090134 avg loss, 0.000035 rate, 0.620706 seconds, 27776 images\n",
            "Loaded: 1.333918 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 435: 2.121445, 2.093265 avg loss, 0.000036 rate, 0.633392 seconds, 27840 images\n",
            "Loaded: 1.315389 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 436: 2.040979, 2.088037 avg loss, 0.000036 rate, 0.577420 seconds, 27904 images\n",
            "Loaded: 1.391173 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 437: 1.771106, 2.056344 avg loss, 0.000036 rate, 0.591243 seconds, 27968 images\n",
            "Loaded: 1.347787 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 438: 2.116066, 2.062316 avg loss, 0.000037 rate, 0.619710 seconds, 28032 images\n",
            "Loaded: 1.338785 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 439: 2.355814, 2.091666 avg loss, 0.000037 rate, 0.600437 seconds, 28096 images\n",
            "Loaded: 1.369213 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 440: 1.601732, 2.042672 avg loss, 0.000037 rate, 0.648965 seconds, 28160 images\n",
            "Resizing\n",
            "352 x 352 \n",
            " try to allocate additional workspace_size = 1264.58 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.000035 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 441: 1.934801, 2.031885 avg loss, 0.000038 rate, 0.375528 seconds, 28224 images\n",
            "Loaded: 1.508096 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 442: 2.140885, 2.042785 avg loss, 0.000038 rate, 0.324690 seconds, 28288 images\n",
            "Loaded: 1.506373 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 443: 2.460950, 2.084602 avg loss, 0.000039 rate, 0.290393 seconds, 28352 images\n",
            "Loaded: 1.522480 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 444: 2.228102, 2.098952 avg loss, 0.000039 rate, 0.310482 seconds, 28416 images\n",
            "Loaded: 1.466362 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 445: 1.693300, 2.058387 avg loss, 0.000039 rate, 0.298423 seconds, 28480 images\n",
            "Loaded: 1.488820 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 446: 2.063114, 2.058859 avg loss, 0.000040 rate, 0.286222 seconds, 28544 images\n",
            "Loaded: 1.420764 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 447: 1.700660, 2.023039 avg loss, 0.000040 rate, 0.300632 seconds, 28608 images\n",
            "Loaded: 1.444386 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 448: 1.563280, 1.977063 avg loss, 0.000040 rate, 0.296698 seconds, 28672 images\n",
            "Loaded: 1.445962 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 449: 1.854322, 1.964789 avg loss, 0.000041 rate, 0.288894 seconds, 28736 images\n",
            "Loaded: 1.525330 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 450: 1.807701, 1.949080 avg loss, 0.000041 rate, 0.286445 seconds, 28800 images\n",
            "Resizing\n",
            "544 x 544 \n",
            " try to allocate additional workspace_size = 4601.28 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.000041 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 451: 2.041879, 1.958360 avg loss, 0.000041 rate, 0.892786 seconds, 28864 images\n",
            "Loaded: 1.223890 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 452: 1.668098, 1.929334 avg loss, 0.000042 rate, 0.894302 seconds, 28928 images\n",
            "Loaded: 1.377729 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 453: 1.787109, 1.915112 avg loss, 0.000042 rate, 0.829220 seconds, 28992 images\n",
            "Loaded: 1.372761 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 454: 1.937388, 1.917339 avg loss, 0.000042 rate, 0.816449 seconds, 29056 images\n",
            "Loaded: 1.376450 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 455: 1.755419, 1.901147 avg loss, 0.000043 rate, 0.867272 seconds, 29120 images\n",
            "Loaded: 1.319480 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 456: 1.613359, 1.872368 avg loss, 0.000043 rate, 0.804844 seconds, 29184 images\n",
            "Loaded: 1.338025 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 457: 1.738734, 1.859005 avg loss, 0.000044 rate, 0.763170 seconds, 29248 images\n",
            "Loaded: 1.311686 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 458: 1.544473, 1.827552 avg loss, 0.000044 rate, 0.730857 seconds, 29312 images\n",
            "Loaded: 1.360862 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 459: 1.528003, 1.797597 avg loss, 0.000044 rate, 0.791648 seconds, 29376 images\n",
            "Loaded: 1.363776 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 460: 1.611352, 1.778973 avg loss, 0.000045 rate, 0.785538 seconds, 29440 images\n",
            "Resizing\n",
            "608 x 608 \n",
            " try to allocate additional workspace_size = 4610.72 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.000040 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 461: 1.800330, 1.781108 avg loss, 0.000045 rate, 1.128168 seconds, 29504 images\n",
            "Loaded: 1.102525 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 462: 1.743052, 1.777303 avg loss, 0.000046 rate, 1.035987 seconds, 29568 images\n",
            "Loaded: 1.162412 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 463: 1.610061, 1.760579 avg loss, 0.000046 rate, 0.996768 seconds, 29632 images\n",
            "Loaded: 1.222166 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 464: 1.370489, 1.721570 avg loss, 0.000046 rate, 0.953170 seconds, 29696 images\n",
            "Loaded: 1.300978 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 465: 1.899252, 1.739338 avg loss, 0.000047 rate, 1.120627 seconds, 29760 images\n",
            "Loaded: 1.298480 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 466: 1.754865, 1.740891 avg loss, 0.000047 rate, 1.091452 seconds, 29824 images\n",
            "Loaded: 1.365320 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 467: 1.805693, 1.747371 avg loss, 0.000048 rate, 1.103267 seconds, 29888 images\n",
            "Loaded: 1.177561 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 468: 2.051059, 1.777740 avg loss, 0.000048 rate, 1.113510 seconds, 29952 images\n",
            "Loaded: 1.112689 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 469: 1.858202, 1.785786 avg loss, 0.000048 rate, 1.078161 seconds, 30016 images\n",
            "Loaded: 1.188837 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 470: 1.881247, 1.795332 avg loss, 0.000049 rate, 1.129406 seconds, 30080 images\n",
            "Resizing\n",
            "544 x 544 \n",
            " try to allocate additional workspace_size = 4601.28 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 1.363064 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 471: 1.748536, 1.790653 avg loss, 0.000049 rate, 0.869757 seconds, 30144 images\n",
            "Loaded: 1.301864 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 472: 1.586984, 1.770286 avg loss, 0.000050 rate, 0.778448 seconds, 30208 images\n",
            "Loaded: 1.374553 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 473: 1.571165, 1.750374 avg loss, 0.000050 rate, 0.870716 seconds, 30272 images\n",
            "Loaded: 1.229541 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 474: 1.701173, 1.745453 avg loss, 0.000050 rate, 0.781475 seconds, 30336 images\n",
            "Loaded: 1.372808 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 475: 1.784544, 1.749362 avg loss, 0.000051 rate, 0.777762 seconds, 30400 images\n",
            "Loaded: 1.468397 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 476: 1.781294, 1.752556 avg loss, 0.000051 rate, 0.834184 seconds, 30464 images\n",
            "Loaded: 1.370252 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 477: 1.866084, 1.763908 avg loss, 0.000052 rate, 0.876631 seconds, 30528 images\n",
            "Loaded: 1.289358 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 478: 1.824909, 1.770008 avg loss, 0.000052 rate, 0.838257 seconds, 30592 images\n",
            "Loaded: 1.292008 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 479: 2.024487, 1.795456 avg loss, 0.000053 rate, 0.823398 seconds, 30656 images\n",
            "Loaded: 1.311298 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 480: 1.873820, 1.803293 avg loss, 0.000053 rate, 0.782390 seconds, 30720 images\n",
            "Resizing\n",
            "416 x 416 \n",
            " try to allocate additional workspace_size = 1302.33 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.000039 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 481: 1.431410, 1.766104 avg loss, 0.000054 rate, 0.486906 seconds, 30784 images\n",
            "Loaded: 1.434254 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 482: 1.944629, 1.783957 avg loss, 0.000054 rate, 0.445266 seconds, 30848 images\n",
            "Loaded: 1.438790 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 483: 1.451263, 1.750687 avg loss, 0.000054 rate, 0.417547 seconds, 30912 images\n",
            "Loaded: 1.424755 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 484: 1.961757, 1.771794 avg loss, 0.000055 rate, 0.417613 seconds, 30976 images\n",
            "Loaded: 1.420257 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 485: 1.496166, 1.744232 avg loss, 0.000055 rate, 0.437065 seconds, 31040 images\n",
            "Loaded: 1.376871 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 486: 1.471363, 1.716945 avg loss, 0.000056 rate, 0.441951 seconds, 31104 images\n",
            "Loaded: 1.512927 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 487: 1.549371, 1.700187 avg loss, 0.000056 rate, 0.514189 seconds, 31168 images\n",
            "Loaded: 1.398484 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 488: 1.564596, 1.686628 avg loss, 0.000057 rate, 0.445489 seconds, 31232 images\n",
            "Loaded: 1.447022 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 489: 1.703138, 1.688279 avg loss, 0.000057 rate, 0.425203 seconds, 31296 images\n",
            "Loaded: 1.469017 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 490: 1.524618, 1.671913 avg loss, 0.000058 rate, 0.417322 seconds, 31360 images\n",
            "Resizing\n",
            "576 x 576 \n",
            " try to allocate additional workspace_size = 4605.87 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.000043 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 491: 1.812982, 1.686020 avg loss, 0.000058 rate, 1.074207 seconds, 31424 images\n",
            "Loaded: 1.111232 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 492: 1.491598, 1.666578 avg loss, 0.000059 rate, 0.904444 seconds, 31488 images\n",
            "Loaded: 1.247455 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 493: 1.891747, 1.689095 avg loss, 0.000059 rate, 0.928318 seconds, 31552 images\n",
            "Loaded: 1.323422 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 494: 1.757152, 1.695901 avg loss, 0.000060 rate, 0.981248 seconds, 31616 images\n",
            "Loaded: 1.235270 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 495: 1.935410, 1.719851 avg loss, 0.000060 rate, 0.858046 seconds, 31680 images\n",
            "Loaded: 1.301835 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 496: 1.382676, 1.686134 avg loss, 0.000061 rate, 0.972248 seconds, 31744 images\n",
            "Loaded: 1.203202 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 497: 1.316121, 1.649133 avg loss, 0.000061 rate, 0.973124 seconds, 31808 images\n",
            "Loaded: 1.222414 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 498: 1.544008, 1.638620 avg loss, 0.000062 rate, 0.870209 seconds, 31872 images\n",
            "Loaded: 1.304088 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 499: 1.775538, 1.652312 avg loss, 0.000062 rate, 0.932161 seconds, 31936 images\n",
            "Loaded: 1.263423 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 500: 1.295403, 1.616621 avg loss, 0.000063 rate, 0.836636 seconds, 32000 images\n",
            "Saving weights to /content/gdrive/My Drive/darknet/backup/yolov3-tiny-280120_last.weights\n",
            "Resizing\n",
            "608 x 608 \n",
            " try to allocate additional workspace_size = 4610.72 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.000041 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 501: 1.619523, 1.616911 avg loss, 0.000063 rate, 1.216197 seconds, 32064 images\n",
            "Loaded: 1.105565 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 502: 1.270615, 1.582282 avg loss, 0.000064 rate, 0.983989 seconds, 32128 images\n",
            "Loaded: 1.244508 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 503: 1.454726, 1.569526 avg loss, 0.000064 rate, 1.066990 seconds, 32192 images\n",
            "Loaded: 1.207366 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 504: 1.729759, 1.585549 avg loss, 0.000065 rate, 1.101572 seconds, 32256 images\n",
            "Loaded: 1.162963 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 505: 1.952399, 1.622234 avg loss, 0.000065 rate, 1.004305 seconds, 32320 images\n",
            "Loaded: 1.228878 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 506: 1.841587, 1.644170 avg loss, 0.000066 rate, 1.103596 seconds, 32384 images\n",
            "Loaded: 1.114350 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 507: 1.680813, 1.647834 avg loss, 0.000066 rate, 1.002357 seconds, 32448 images\n",
            "Loaded: 1.279386 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 508: 1.604014, 1.643452 avg loss, 0.000067 rate, 1.031592 seconds, 32512 images\n",
            "Loaded: 1.292235 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 509: 1.575409, 1.636648 avg loss, 0.000067 rate, 1.018618 seconds, 32576 images\n",
            "Loaded: 1.261035 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 510: 1.477656, 1.620749 avg loss, 0.000068 rate, 1.139719 seconds, 32640 images\n",
            "Resizing\n",
            "448 x 448 \n",
            " try to allocate additional workspace_size = 1233.65 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.000036 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 511: 1.393245, 1.597998 avg loss, 0.000068 rate, 0.545997 seconds, 32704 images\n",
            "Loaded: 1.498903 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 512: 1.403183, 1.578517 avg loss, 0.000069 rate, 0.528309 seconds, 32768 images\n",
            "Loaded: 1.598494 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 513: 1.365586, 1.557224 avg loss, 0.000069 rate, 0.559006 seconds, 32832 images\n",
            "Loaded: 1.361630 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 514: 1.930268, 1.594528 avg loss, 0.000070 rate, 0.475035 seconds, 32896 images\n",
            "Loaded: 1.481239 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 515: 2.083239, 1.643399 avg loss, 0.000070 rate, 0.510110 seconds, 32960 images\n",
            "Loaded: 1.474922 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 516: 1.883076, 1.667367 avg loss, 0.000071 rate, 0.508470 seconds, 33024 images\n",
            "Loaded: 1.421086 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 517: 1.503173, 1.650947 avg loss, 0.000071 rate, 0.477028 seconds, 33088 images\n",
            "Loaded: 1.422349 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 518: 1.568020, 1.642655 avg loss, 0.000072 rate, 0.553590 seconds, 33152 images\n",
            "Loaded: 1.374212 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 519: 1.617295, 1.640119 avg loss, 0.000073 rate, 0.492455 seconds, 33216 images\n",
            "Loaded: 1.375690 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 520: 1.410830, 1.617190 avg loss, 0.000073 rate, 0.530372 seconds, 33280 images\n",
            "Resizing\n",
            "320 x 320 \n",
            " try to allocate additional workspace_size = 868.75 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 1.496604 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 521: 1.480591, 1.603530 avg loss, 0.000074 rate, 0.322419 seconds, 33344 images\n",
            "Loaded: 1.348284 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 522: 1.825134, 1.625690 avg loss, 0.000074 rate, 0.287582 seconds, 33408 images\n",
            "Loaded: 1.445800 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 523: 1.949685, 1.658090 avg loss, 0.000075 rate, 0.273356 seconds, 33472 images\n",
            "Loaded: 1.535707 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 524: 1.692610, 1.661542 avg loss, 0.000075 rate, 0.249969 seconds, 33536 images\n",
            "Loaded: 1.602797 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 525: 1.633015, 1.658689 avg loss, 0.000076 rate, 0.271665 seconds, 33600 images\n",
            "Loaded: 1.369037 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 526: 2.178770, 1.710697 avg loss, 0.000077 rate, 0.253316 seconds, 33664 images\n",
            "Loaded: 1.440403 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 527: 1.611982, 1.700826 avg loss, 0.000077 rate, 0.250371 seconds, 33728 images\n",
            "Loaded: 1.411336 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 528: 2.136847, 1.744428 avg loss, 0.000078 rate, 0.283302 seconds, 33792 images\n",
            "Loaded: 1.386364 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 529: 1.756621, 1.745647 avg loss, 0.000078 rate, 0.257100 seconds, 33856 images\n",
            "Loaded: 1.392493 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 530: 1.497416, 1.720824 avg loss, 0.000079 rate, 0.262425 seconds, 33920 images\n",
            "Resizing\n",
            "416 x 416 \n",
            " try to allocate additional workspace_size = 1302.33 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.081705 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 531: 1.512274, 1.699969 avg loss, 0.000080 rate, 0.521037 seconds, 33984 images\n",
            "Loaded: 1.307595 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 532: 1.486235, 1.678596 avg loss, 0.000080 rate, 0.412159 seconds, 34048 images\n",
            "Loaded: 1.445478 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 533: 1.560458, 1.666782 avg loss, 0.000081 rate, 0.464806 seconds, 34112 images\n",
            "Loaded: 1.312783 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 534: 1.302725, 1.630376 avg loss, 0.000081 rate, 0.434111 seconds, 34176 images\n",
            "Loaded: 1.460395 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 535: 1.624231, 1.629762 avg loss, 0.000082 rate, 0.473373 seconds, 34240 images\n",
            "Loaded: 1.394148 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 536: 1.347702, 1.601556 avg loss, 0.000083 rate, 0.405811 seconds, 34304 images\n",
            "Loaded: 1.466746 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 537: 1.430617, 1.584462 avg loss, 0.000083 rate, 0.496658 seconds, 34368 images\n",
            "Loaded: 1.368931 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 538: 1.373269, 1.563343 avg loss, 0.000084 rate, 0.417733 seconds, 34432 images\n",
            "Loaded: 1.456190 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 539: 1.541159, 1.561124 avg loss, 0.000084 rate, 0.429210 seconds, 34496 images\n",
            "Loaded: 1.468410 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 540: 1.436711, 1.548683 avg loss, 0.000085 rate, 0.452253 seconds, 34560 images\n",
            "Resizing\n",
            "352 x 352 \n",
            " try to allocate additional workspace_size = 1264.58 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 1.545902 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 541: 1.320138, 1.525828 avg loss, 0.000086 rate, 0.370010 seconds, 34624 images\n",
            "Loaded: 1.515010 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 542: 1.327813, 1.506027 avg loss, 0.000086 rate, 0.333659 seconds, 34688 images\n",
            "Loaded: 1.486614 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 543: 1.371749, 1.492599 avg loss, 0.000087 rate, 0.286079 seconds, 34752 images\n",
            "Loaded: 1.464057 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 544: 1.489407, 1.492280 avg loss, 0.000088 rate, 0.297296 seconds, 34816 images\n",
            "Loaded: 1.504379 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 545: 1.469308, 1.489983 avg loss, 0.000088 rate, 0.312837 seconds, 34880 images\n",
            "Loaded: 1.436734 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 546: 1.454368, 1.486421 avg loss, 0.000089 rate, 0.275746 seconds, 34944 images\n",
            "Loaded: 1.470295 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 547: 1.262167, 1.463996 avg loss, 0.000090 rate, 0.312525 seconds, 35008 images\n",
            "Loaded: 1.431353 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 548: 1.512376, 1.468834 avg loss, 0.000090 rate, 0.309398 seconds, 35072 images\n",
            "Loaded: 1.476803 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 549: 1.374120, 1.459363 avg loss, 0.000091 rate, 0.322349 seconds, 35136 images\n",
            "Loaded: 1.439544 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 550: 1.450803, 1.458507 avg loss, 0.000092 rate, 0.305582 seconds, 35200 images\n",
            "Resizing\n",
            "384 x 384 \n",
            " try to allocate additional workspace_size = 1302.33 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.464230 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 551: 1.427045, 1.455361 avg loss, 0.000092 rate, 0.397155 seconds, 35264 images\n",
            "Loaded: 1.371587 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 552: 1.347684, 1.444593 avg loss, 0.000093 rate, 0.377647 seconds, 35328 images\n",
            "Loaded: 1.510327 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 553: 1.240208, 1.424155 avg loss, 0.000094 rate, 0.377590 seconds, 35392 images\n",
            "Loaded: 1.487863 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 554: 1.258252, 1.407564 avg loss, 0.000094 rate, 0.361883 seconds, 35456 images\n",
            "Loaded: 1.535825 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 555: 1.557542, 1.422562 avg loss, 0.000095 rate, 0.353270 seconds, 35520 images\n",
            "Loaded: 1.431663 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 556: 1.279398, 1.408246 avg loss, 0.000096 rate, 0.370647 seconds, 35584 images\n",
            "Loaded: 1.381758 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 557: 1.617540, 1.429175 avg loss, 0.000096 rate, 0.369077 seconds, 35648 images\n",
            "Loaded: 1.394892 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 558: 1.397110, 1.425969 avg loss, 0.000097 rate, 0.381110 seconds, 35712 images\n",
            "Loaded: 1.413368 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 559: 1.725446, 1.455916 avg loss, 0.000098 rate, 0.394331 seconds, 35776 images\n",
            "Loaded: 1.434308 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 560: 1.424887, 1.452813 avg loss, 0.000098 rate, 0.356052 seconds, 35840 images\n",
            "Resizing\n",
            "384 x 384 \n",
            " try to allocate additional workspace_size = 1302.33 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 1.601422 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 561: 1.224760, 1.430008 avg loss, 0.000099 rate, 0.425042 seconds, 35904 images\n",
            "Loaded: 1.346153 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 562: 1.562082, 1.443215 avg loss, 0.000100 rate, 0.388274 seconds, 35968 images\n",
            "Loaded: 1.384655 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 563: 1.263722, 1.425266 avg loss, 0.000100 rate, 0.358540 seconds, 36032 images\n",
            "Loaded: 1.390916 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 564: 1.338161, 1.416556 avg loss, 0.000101 rate, 0.391213 seconds, 36096 images\n",
            "Loaded: 1.402643 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 565: 1.454796, 1.420380 avg loss, 0.000102 rate, 0.359686 seconds, 36160 images\n",
            "Loaded: 1.481033 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 566: 1.217943, 1.400136 avg loss, 0.000103 rate, 0.357299 seconds, 36224 images\n",
            "Loaded: 1.396035 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 567: 1.151458, 1.375268 avg loss, 0.000103 rate, 0.393932 seconds, 36288 images\n",
            "Loaded: 1.419438 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 568: 1.432133, 1.380955 avg loss, 0.000104 rate, 0.384048 seconds, 36352 images\n",
            "Loaded: 1.436581 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 569: 1.441018, 1.386961 avg loss, 0.000105 rate, 0.364897 seconds, 36416 images\n",
            "Loaded: 1.414904 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 570: 1.229688, 1.371234 avg loss, 0.000106 rate, 0.336427 seconds, 36480 images\n",
            "Resizing\n",
            "576 x 576 \n",
            " try to allocate additional workspace_size = 4605.87 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.000029 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 571: 1.527137, 1.386824 avg loss, 0.000106 rate, 1.071305 seconds, 36544 images\n",
            "Loaded: 1.095984 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 572: 1.479426, 1.396084 avg loss, 0.000107 rate, 0.829197 seconds, 36608 images\n",
            "Loaded: 1.291095 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 573: 1.105068, 1.366983 avg loss, 0.000108 rate, 0.976546 seconds, 36672 images\n",
            "Loaded: 1.203424 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 574: 1.274173, 1.357702 avg loss, 0.000109 rate, 0.830609 seconds, 36736 images\n",
            "Loaded: 1.333338 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 575: 1.573929, 1.379325 avg loss, 0.000109 rate, 0.846941 seconds, 36800 images\n",
            "Loaded: 1.292996 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 576: 1.317539, 1.373146 avg loss, 0.000110 rate, 1.013028 seconds, 36864 images\n",
            "Loaded: 1.198625 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 577: 1.379170, 1.373748 avg loss, 0.000111 rate, 0.886375 seconds, 36928 images\n",
            "Loaded: 1.293786 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 578: 1.553966, 1.391770 avg loss, 0.000112 rate, 0.916591 seconds, 36992 images\n",
            "Loaded: 1.338651 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 579: 1.759750, 1.428568 avg loss, 0.000112 rate, 0.832938 seconds, 37056 images\n",
            "Loaded: 1.398128 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 580: 1.449084, 1.430620 avg loss, 0.000113 rate, 1.002328 seconds, 37120 images\n",
            "Resizing\n",
            "544 x 544 \n",
            " try to allocate additional workspace_size = 4601.28 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 1.552705 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 581: 1.261880, 1.413746 avg loss, 0.000114 rate, 0.816430 seconds, 37184 images\n",
            "Loaded: 1.286480 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 582: 1.613562, 1.433727 avg loss, 0.000115 rate, 0.795657 seconds, 37248 images\n",
            "Loaded: 1.314503 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 583: 1.253382, 1.415693 avg loss, 0.000116 rate, 0.783769 seconds, 37312 images\n",
            "Loaded: 1.248599 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 584: 1.871582, 1.461282 avg loss, 0.000116 rate, 0.815702 seconds, 37376 images\n",
            "Loaded: 1.288348 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 585: 1.121462, 1.427300 avg loss, 0.000117 rate, 0.813360 seconds, 37440 images\n",
            "Loaded: 1.268969 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 586: 1.425976, 1.427167 avg loss, 0.000118 rate, 0.826702 seconds, 37504 images\n",
            "Loaded: 1.235050 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 587: 1.352818, 1.419733 avg loss, 0.000119 rate, 0.785132 seconds, 37568 images\n",
            "Loaded: 1.383265 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 588: 1.595236, 1.437283 avg loss, 0.000120 rate, 0.800851 seconds, 37632 images\n",
            "Loaded: 1.286994 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 589: 1.497020, 1.443257 avg loss, 0.000120 rate, 0.889009 seconds, 37696 images\n",
            "Loaded: 1.238080 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 590: 1.215442, 1.420475 avg loss, 0.000121 rate, 0.819341 seconds, 37760 images\n",
            "Resizing\n",
            "576 x 576 \n",
            " try to allocate additional workspace_size = 4605.87 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.000040 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 591: 1.553572, 1.433785 avg loss, 0.000122 rate, 0.892653 seconds, 37824 images\n",
            "Loaded: 1.334860 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 592: 1.485906, 1.438997 avg loss, 0.000123 rate, 0.972020 seconds, 37888 images\n",
            "Loaded: 1.250084 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 593: 1.441579, 1.439255 avg loss, 0.000124 rate, 0.919948 seconds, 37952 images\n",
            "Loaded: 1.320823 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 594: 1.173359, 1.412666 avg loss, 0.000124 rate, 0.903595 seconds, 38016 images\n",
            "Loaded: 1.421165 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 595: 1.075884, 1.378987 avg loss, 0.000125 rate, 0.915552 seconds, 38080 images\n",
            "Loaded: 1.317080 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 596: 1.356611, 1.376750 avg loss, 0.000126 rate, 0.880748 seconds, 38144 images\n",
            "Loaded: 1.263957 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 597: 1.563604, 1.395435 avg loss, 0.000127 rate, 0.841029 seconds, 38208 images\n",
            "Loaded: 1.365654 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 598: 1.501888, 1.406080 avg loss, 0.000128 rate, 0.914529 seconds, 38272 images\n",
            "Loaded: 1.211673 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 599: 1.287874, 1.394260 avg loss, 0.000129 rate, 0.892957 seconds, 38336 images\n",
            "Loaded: 1.306052 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 600: 1.411651, 1.395999 avg loss, 0.000130 rate, 0.922869 seconds, 38400 images\n",
            "Saving weights to /content/gdrive/My Drive/darknet/backup/yolov3-tiny-280120_last.weights\n",
            "Resizing\n",
            "352 x 352 \n",
            " try to allocate additional workspace_size = 1264.58 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.000052 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 601: 1.270847, 1.383484 avg loss, 0.000130 rate, 0.374748 seconds, 38464 images\n",
            "Loaded: 1.368957 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 602: 1.262946, 1.371430 avg loss, 0.000131 rate, 0.287534 seconds, 38528 images\n",
            "Loaded: 1.450059 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 603: 1.294445, 1.363732 avg loss, 0.000132 rate, 0.314665 seconds, 38592 images\n",
            "Loaded: 1.557930 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 604: 1.421437, 1.369502 avg loss, 0.000133 rate, 0.326595 seconds, 38656 images\n",
            "Loaded: 1.645804 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 605: 1.330415, 1.365593 avg loss, 0.000134 rate, 0.306906 seconds, 38720 images\n",
            "Loaded: 1.459585 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 606: 1.231841, 1.352218 avg loss, 0.000135 rate, 0.314849 seconds, 38784 images\n",
            "Loaded: 1.508326 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 607: 1.311449, 1.348141 avg loss, 0.000136 rate, 0.282107 seconds, 38848 images\n",
            "Loaded: 1.468726 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 608: 1.644699, 1.377797 avg loss, 0.000137 rate, 0.305718 seconds, 38912 images\n",
            "Loaded: 1.345266 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 609: 1.136783, 1.353696 avg loss, 0.000138 rate, 0.290810 seconds, 38976 images\n",
            "Loaded: 1.426910 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 610: 1.238859, 1.342212 avg loss, 0.000138 rate, 0.295094 seconds, 39040 images\n",
            "Resizing\n",
            "384 x 384 \n",
            " try to allocate additional workspace_size = 1302.33 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.000030 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 611: 1.235878, 1.331579 avg loss, 0.000139 rate, 0.390450 seconds, 39104 images\n",
            "Loaded: 1.372984 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 612: 1.257568, 1.324178 avg loss, 0.000140 rate, 0.403605 seconds, 39168 images\n",
            "Loaded: 1.383176 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 613: 1.315546, 1.323314 avg loss, 0.000141 rate, 0.378944 seconds, 39232 images\n",
            "Loaded: 1.467461 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 614: 1.155711, 1.306554 avg loss, 0.000142 rate, 0.369620 seconds, 39296 images\n",
            "Loaded: 1.376126 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 615: 1.758392, 1.351738 avg loss, 0.000143 rate, 0.359053 seconds, 39360 images\n",
            "Loaded: 1.403712 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 616: 0.900782, 1.306642 avg loss, 0.000144 rate, 0.369988 seconds, 39424 images\n",
            "Loaded: 1.463653 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 617: 1.161481, 1.292126 avg loss, 0.000145 rate, 0.409220 seconds, 39488 images\n",
            "Loaded: 1.445527 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 618: 1.317215, 1.294635 avg loss, 0.000146 rate, 0.379008 seconds, 39552 images\n",
            "Loaded: 1.358386 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 619: 1.313680, 1.296540 avg loss, 0.000147 rate, 0.375865 seconds, 39616 images\n",
            "Loaded: 1.473731 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 620: 1.508726, 1.317758 avg loss, 0.000148 rate, 0.376492 seconds, 39680 images\n",
            "Resizing\n",
            "480 x 480 \n",
            " try to allocate additional workspace_size = 4592.89 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.000039 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 621: 1.099555, 1.295938 avg loss, 0.000149 rate, 0.659919 seconds, 39744 images\n",
            "Loaded: 1.492987 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 622: 1.150243, 1.281368 avg loss, 0.000150 rate, 0.587304 seconds, 39808 images\n",
            "Loaded: 1.405241 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 623: 1.273013, 1.280533 avg loss, 0.000151 rate, 0.617922 seconds, 39872 images\n",
            "Loaded: 1.412442 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 624: 1.174254, 1.269905 avg loss, 0.000152 rate, 0.573395 seconds, 39936 images\n",
            "Loaded: 1.412955 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 625: 1.305901, 1.273504 avg loss, 0.000153 rate, 0.588918 seconds, 40000 images\n",
            "Loaded: 1.428887 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 626: 1.320538, 1.278208 avg loss, 0.000154 rate, 0.615092 seconds, 40064 images\n",
            "Loaded: 1.350867 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 627: 1.045433, 1.254930 avg loss, 0.000155 rate, 0.565057 seconds, 40128 images\n",
            "Loaded: 1.447269 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 628: 1.160625, 1.245500 avg loss, 0.000156 rate, 0.636581 seconds, 40192 images\n",
            "Loaded: 1.393012 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 629: 1.240021, 1.244952 avg loss, 0.000157 rate, 0.674330 seconds, 40256 images\n",
            "Loaded: 1.333307 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 630: 1.325201, 1.252977 avg loss, 0.000158 rate, 0.628099 seconds, 40320 images\n",
            "Resizing\n",
            "352 x 352 \n",
            " try to allocate additional workspace_size = 1264.58 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 1.469977 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 631: 1.254303, 1.253109 avg loss, 0.000159 rate, 0.288498 seconds, 40384 images\n",
            "Loaded: 1.674132 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 632: 0.834396, 1.211238 avg loss, 0.000160 rate, 0.288775 seconds, 40448 images\n",
            "Loaded: 1.449240 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 633: 1.761990, 1.266313 avg loss, 0.000161 rate, 0.349843 seconds, 40512 images\n",
            "Loaded: 1.551826 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 634: 1.375610, 1.277243 avg loss, 0.000162 rate, 0.336419 seconds, 40576 images\n",
            "Loaded: 1.411776 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 635: 1.449799, 1.294499 avg loss, 0.000163 rate, 0.294262 seconds, 40640 images\n",
            "Loaded: 1.421093 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 636: 0.942737, 1.259323 avg loss, 0.000164 rate, 0.285110 seconds, 40704 images\n",
            "Loaded: 1.551848 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 637: 1.150401, 1.248430 avg loss, 0.000165 rate, 0.287228 seconds, 40768 images\n",
            "Loaded: 1.467247 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 638: 1.040972, 1.227685 avg loss, 0.000166 rate, 0.310399 seconds, 40832 images\n",
            "Loaded: 1.485149 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 639: 1.116813, 1.216597 avg loss, 0.000167 rate, 0.322042 seconds, 40896 images\n",
            "Loaded: 1.473922 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 640: 1.031414, 1.198079 avg loss, 0.000168 rate, 0.315356 seconds, 40960 images\n",
            "Resizing\n",
            "416 x 416 \n",
            " try to allocate additional workspace_size = 1302.33 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.446941 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 641: 1.062961, 1.184567 avg loss, 0.000169 rate, 0.438033 seconds, 41024 images\n",
            "Loaded: 1.558877 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 642: 1.052092, 1.171320 avg loss, 0.000170 rate, 0.532833 seconds, 41088 images\n",
            "Loaded: 1.420490 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 643: 1.256599, 1.179848 avg loss, 0.000171 rate, 0.422076 seconds, 41152 images\n",
            "Loaded: 1.533654 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 644: 1.283725, 1.190235 avg loss, 0.000172 rate, 0.434468 seconds, 41216 images\n",
            "Loaded: 1.431329 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 645: 1.030210, 1.174233 avg loss, 0.000173 rate, 0.471346 seconds, 41280 images\n",
            "Loaded: 1.409726 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 646: 1.183864, 1.175196 avg loss, 0.000174 rate, 0.487076 seconds, 41344 images\n",
            "Loaded: 1.500828 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 647: 1.143662, 1.172042 avg loss, 0.000175 rate, 0.454478 seconds, 41408 images\n",
            "Loaded: 1.586273 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 648: 1.230452, 1.177883 avg loss, 0.000176 rate, 0.446040 seconds, 41472 images\n",
            "Loaded: 1.424417 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 649: 1.028962, 1.162991 avg loss, 0.000177 rate, 0.476921 seconds, 41536 images\n",
            "Loaded: 1.593858 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 650: 0.967652, 1.143457 avg loss, 0.000179 rate, 0.432633 seconds, 41600 images\n",
            "Resizing\n",
            "352 x 352 \n",
            " try to allocate additional workspace_size = 1264.58 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 1.432921 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 651: 1.066910, 1.135803 avg loss, 0.000180 rate, 0.345616 seconds, 41664 images\n",
            "Loaded: 1.428054 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 652: 1.296598, 1.151882 avg loss, 0.000181 rate, 0.318628 seconds, 41728 images\n",
            "Loaded: 1.462887 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 653: 1.094360, 1.146130 avg loss, 0.000182 rate, 0.323237 seconds, 41792 images\n",
            "Loaded: 1.551660 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 654: 1.073156, 1.138833 avg loss, 0.000183 rate, 0.302665 seconds, 41856 images\n",
            "Loaded: 1.455513 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 655: 1.244753, 1.149425 avg loss, 0.000184 rate, 0.293316 seconds, 41920 images\n",
            "Loaded: 1.583759 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 656: 1.108215, 1.145304 avg loss, 0.000185 rate, 0.309074 seconds, 41984 images\n",
            "Loaded: 1.449421 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 657: 1.232730, 1.154046 avg loss, 0.000186 rate, 0.299532 seconds, 42048 images\n",
            "Loaded: 1.520633 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 658: 1.076784, 1.146320 avg loss, 0.000187 rate, 0.285929 seconds, 42112 images\n",
            "Loaded: 1.531666 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 659: 1.075855, 1.139274 avg loss, 0.000189 rate, 0.316016 seconds, 42176 images\n",
            "Loaded: 1.438841 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 660: 1.239341, 1.149280 avg loss, 0.000190 rate, 0.292761 seconds, 42240 images\n",
            "Resizing\n",
            "448 x 448 \n",
            " try to allocate additional workspace_size = 1233.65 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.108454 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 661: 1.330789, 1.167431 avg loss, 0.000191 rate, 0.615603 seconds, 42304 images\n",
            "Loaded: 1.502677 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 662: 1.259489, 1.176637 avg loss, 0.000192 rate, 0.557063 seconds, 42368 images\n",
            "Loaded: 1.425064 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 663: 1.077943, 1.166768 avg loss, 0.000193 rate, 0.447051 seconds, 42432 images\n",
            "Loaded: 1.519768 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 664: 1.138039, 1.163895 avg loss, 0.000194 rate, 0.471844 seconds, 42496 images\n",
            "Loaded: 1.462957 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 665: 1.070735, 1.154579 avg loss, 0.000196 rate, 0.479000 seconds, 42560 images\n",
            "Loaded: 1.477449 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 666: 1.227487, 1.161870 avg loss, 0.000197 rate, 0.459944 seconds, 42624 images\n",
            "Loaded: 1.456506 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 667: 0.965207, 1.142203 avg loss, 0.000198 rate, 0.483060 seconds, 42688 images\n",
            "Loaded: 1.417419 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 668: 1.114111, 1.139394 avg loss, 0.000199 rate, 0.490329 seconds, 42752 images\n",
            "Loaded: 1.396886 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 669: 1.095379, 1.134993 avg loss, 0.000200 rate, 0.502049 seconds, 42816 images\n",
            "Loaded: 1.423209 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 670: 1.139782, 1.135472 avg loss, 0.000202 rate, 0.583606 seconds, 42880 images\n",
            "Resizing\n",
            "352 x 352 \n",
            " try to allocate additional workspace_size = 1264.58 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 1.418539 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 671: 1.155576, 1.137482 avg loss, 0.000203 rate, 0.364837 seconds, 42944 images\n",
            "Loaded: 1.416083 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 672: 1.130610, 1.136795 avg loss, 0.000204 rate, 0.300523 seconds, 43008 images\n",
            "Loaded: 1.506870 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 673: 1.346583, 1.157774 avg loss, 0.000205 rate, 0.273941 seconds, 43072 images\n",
            "Loaded: 1.439880 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 674: 1.359565, 1.177953 avg loss, 0.000206 rate, 0.303948 seconds, 43136 images\n",
            "Loaded: 1.434539 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 675: 1.186587, 1.178816 avg loss, 0.000208 rate, 0.280087 seconds, 43200 images\n",
            "Loaded: 1.460164 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 676: 1.192293, 1.180164 avg loss, 0.000209 rate, 0.305223 seconds, 43264 images\n",
            "Loaded: 1.410817 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 677: 1.230283, 1.185176 avg loss, 0.000210 rate, 0.321617 seconds, 43328 images\n",
            "Loaded: 1.418599 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 678: 1.170195, 1.183678 avg loss, 0.000211 rate, 0.291966 seconds, 43392 images\n",
            "Loaded: 1.494830 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 679: 1.080910, 1.173401 avg loss, 0.000213 rate, 0.316094 seconds, 43456 images\n",
            "Loaded: 1.562768 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 680: 1.081388, 1.164200 avg loss, 0.000214 rate, 0.282462 seconds, 43520 images\n",
            "Resizing\n",
            "576 x 576 \n",
            " try to allocate additional workspace_size = 4605.87 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.000037 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 681: 1.250996, 1.172879 avg loss, 0.000215 rate, 1.092427 seconds, 43584 images\n",
            "Loaded: 1.103996 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 682: 1.204492, 1.176040 avg loss, 0.000216 rate, 0.943150 seconds, 43648 images\n",
            "Loaded: 1.263768 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 683: 1.465907, 1.205027 avg loss, 0.000218 rate, 0.999339 seconds, 43712 images\n",
            "Loaded: 1.182199 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 684: 1.266273, 1.211152 avg loss, 0.000219 rate, 0.937827 seconds, 43776 images\n",
            "Loaded: 1.255335 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 685: 1.180282, 1.208065 avg loss, 0.000220 rate, 0.865259 seconds, 43840 images\n",
            "Loaded: 1.392178 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 686: 1.210532, 1.208311 avg loss, 0.000221 rate, 0.917529 seconds, 43904 images\n",
            "Loaded: 1.325956 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 687: 0.915979, 1.179078 avg loss, 0.000223 rate, 0.970071 seconds, 43968 images\n",
            "Loaded: 1.393611 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 688: 1.305261, 1.191696 avg loss, 0.000224 rate, 0.983704 seconds, 44032 images\n",
            "Loaded: 1.328372 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 689: 1.119306, 1.184457 avg loss, 0.000225 rate, 0.953980 seconds, 44096 images\n",
            "Loaded: 1.239243 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 690: 1.080748, 1.174086 avg loss, 0.000227 rate, 0.812994 seconds, 44160 images\n",
            "Resizing\n",
            "416 x 416 \n",
            " try to allocate additional workspace_size = 1302.33 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 1.388425 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 691: 1.099350, 1.166613 avg loss, 0.000228 rate, 0.409063 seconds, 44224 images\n",
            "Loaded: 1.499998 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 692: 1.045841, 1.154536 avg loss, 0.000229 rate, 0.399846 seconds, 44288 images\n",
            "Loaded: 1.573986 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 693: 1.081683, 1.147250 avg loss, 0.000231 rate, 0.437400 seconds, 44352 images\n",
            "Loaded: 1.421154 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 694: 1.026004, 1.135126 avg loss, 0.000232 rate, 0.446662 seconds, 44416 images\n",
            "Loaded: 1.390760 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 695: 1.157376, 1.137351 avg loss, 0.000233 rate, 0.424113 seconds, 44480 images\n",
            "Loaded: 1.608179 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 696: 1.394231, 1.163039 avg loss, 0.000235 rate, 0.441518 seconds, 44544 images\n",
            "Loaded: 1.412075 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 697: 1.235882, 1.170323 avg loss, 0.000236 rate, 0.413791 seconds, 44608 images\n",
            "Loaded: 1.469618 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 698: 1.066070, 1.159898 avg loss, 0.000237 rate, 0.437771 seconds, 44672 images\n",
            "Loaded: 1.527098 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 699: 1.089674, 1.152875 avg loss, 0.000239 rate, 0.438099 seconds, 44736 images\n",
            "Loaded: 1.458456 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 700: 1.385053, 1.176093 avg loss, 0.000240 rate, 0.413057 seconds, 44800 images\n",
            "Saving weights to /content/gdrive/My Drive/darknet/backup/yolov3-tiny-280120_last.weights\n",
            "Resizing\n",
            "512 x 512 \n",
            " try to allocate additional workspace_size = 4596.96 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.000040 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 701: 1.140751, 1.172559 avg loss, 0.000241 rate, 0.648337 seconds, 44864 images\n",
            "Loaded: 1.369648 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 702: 1.066636, 1.161967 avg loss, 0.000243 rate, 0.732586 seconds, 44928 images\n",
            "Loaded: 1.430305 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 703: 1.126809, 1.158451 avg loss, 0.000244 rate, 0.700828 seconds, 44992 images\n",
            "Loaded: 1.442520 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 704: 1.347459, 1.177352 avg loss, 0.000246 rate, 0.770734 seconds, 45056 images\n",
            "Loaded: 1.267003 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 705: 0.866794, 1.146296 avg loss, 0.000247 rate, 0.682881 seconds, 45120 images\n",
            "Loaded: 1.438260 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 706: 0.915586, 1.123225 avg loss, 0.000248 rate, 0.602488 seconds, 45184 images\n",
            "Loaded: 1.732221 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 707: 0.900114, 1.100914 avg loss, 0.000250 rate, 0.747576 seconds, 45248 images\n",
            "Loaded: 1.486996 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 708: 0.961449, 1.086967 avg loss, 0.000251 rate, 0.682858 seconds, 45312 images\n",
            "Loaded: 1.459886 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 709: 0.990925, 1.077363 avg loss, 0.000253 rate, 0.630382 seconds, 45376 images\n",
            "Loaded: 1.422423 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 710: 1.307251, 1.100352 avg loss, 0.000254 rate, 0.690673 seconds, 45440 images\n",
            "Resizing\n",
            "448 x 448 \n",
            " try to allocate additional workspace_size = 1233.65 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 1.511484 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 711: 1.126642, 1.102981 avg loss, 0.000256 rate, 0.556644 seconds, 45504 images\n",
            "Loaded: 1.457840 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 712: 1.045310, 1.097214 avg loss, 0.000257 rate, 0.490853 seconds, 45568 images\n",
            "Loaded: 1.634141 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 713: 1.033726, 1.090865 avg loss, 0.000258 rate, 0.499767 seconds, 45632 images\n",
            "Loaded: 1.524180 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 714: 1.144554, 1.096234 avg loss, 0.000260 rate, 0.550194 seconds, 45696 images\n",
            "Loaded: 1.526962 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 715: 1.077752, 1.094386 avg loss, 0.000261 rate, 0.483129 seconds, 45760 images\n",
            "Loaded: 1.354486 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 716: 1.258464, 1.110793 avg loss, 0.000263 rate, 0.516850 seconds, 45824 images\n",
            "Loaded: 1.487804 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 717: 0.833336, 1.083048 avg loss, 0.000264 rate, 0.552869 seconds, 45888 images\n",
            "Loaded: 1.367521 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 718: 0.921749, 1.066918 avg loss, 0.000266 rate, 0.474671 seconds, 45952 images\n",
            "Loaded: 1.546942 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 719: 1.224714, 1.082698 avg loss, 0.000267 rate, 0.468632 seconds, 46016 images\n",
            "Loaded: 1.534230 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 720: 0.930458, 1.067474 avg loss, 0.000269 rate, 0.495386 seconds, 46080 images\n",
            "Resizing\n",
            "384 x 384 \n",
            " try to allocate additional workspace_size = 1302.33 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.000040 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 721: 1.044779, 1.065204 avg loss, 0.000270 rate, 0.436355 seconds, 46144 images\n",
            "Loaded: 1.425776 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 722: 0.990645, 1.057748 avg loss, 0.000272 rate, 0.433004 seconds, 46208 images\n",
            "Loaded: 1.442299 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 723: 0.898457, 1.041819 avg loss, 0.000273 rate, 0.340104 seconds, 46272 images\n",
            "Loaded: 1.485097 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 724: 0.781794, 1.015817 avg loss, 0.000275 rate, 0.379948 seconds, 46336 images\n",
            "Loaded: 1.460998 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 725: 0.928466, 1.007082 avg loss, 0.000276 rate, 0.348309 seconds, 46400 images\n",
            "Loaded: 1.532611 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 726: 0.972124, 1.003586 avg loss, 0.000278 rate, 0.375116 seconds, 46464 images\n",
            "Loaded: 1.512420 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 727: 0.906841, 0.993911 avg loss, 0.000279 rate, 0.413103 seconds, 46528 images\n",
            "Loaded: 1.442537 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 728: 1.044549, 0.998975 avg loss, 0.000281 rate, 0.374059 seconds, 46592 images\n",
            "Loaded: 1.424469 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 729: 0.959775, 0.995055 avg loss, 0.000282 rate, 0.368609 seconds, 46656 images\n",
            "Loaded: 1.429273 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 730: 0.916123, 0.987162 avg loss, 0.000284 rate, 0.329560 seconds, 46720 images\n",
            "Resizing\n",
            "608 x 608 \n",
            " try to allocate additional workspace_size = 4610.72 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.000041 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 731: 1.023750, 0.990821 avg loss, 0.000286 rate, 1.204654 seconds, 46784 images\n",
            "Loaded: 1.081385 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 732: 1.146466, 1.006385 avg loss, 0.000287 rate, 1.194717 seconds, 46848 images\n",
            "Loaded: 1.144180 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 733: 0.982978, 1.004045 avg loss, 0.000289 rate, 1.133202 seconds, 46912 images\n",
            "Loaded: 1.204206 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 734: 1.234977, 1.027138 avg loss, 0.000290 rate, 1.002956 seconds, 46976 images\n",
            "Loaded: 1.343153 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 735: 1.181947, 1.042619 avg loss, 0.000292 rate, 1.037691 seconds, 47040 images\n",
            "Loaded: 1.200660 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 736: 0.966513, 1.035008 avg loss, 0.000293 rate, 1.119892 seconds, 47104 images\n",
            "Loaded: 1.180199 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 737: 1.012898, 1.032797 avg loss, 0.000295 rate, 1.022773 seconds, 47168 images\n",
            "Loaded: 1.260698 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 738: 1.033595, 1.032877 avg loss, 0.000297 rate, 1.202379 seconds, 47232 images\n",
            "Loaded: 1.339408 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 739: 1.268232, 1.056412 avg loss, 0.000298 rate, 1.060041 seconds, 47296 images\n",
            "Loaded: 1.454505 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 740: 1.093933, 1.060165 avg loss, 0.000300 rate, 1.018737 seconds, 47360 images\n",
            "Resizing\n",
            "480 x 480 \n",
            " try to allocate additional workspace_size = 4592.89 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 1.494118 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 741: 0.747218, 1.028870 avg loss, 0.000301 rate, 0.694396 seconds, 47424 images\n",
            "Loaded: 1.289952 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 742: 0.777225, 1.003705 avg loss, 0.000303 rate, 0.672264 seconds, 47488 images\n",
            "Loaded: 1.316319 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 743: 0.999168, 1.003252 avg loss, 0.000305 rate, 0.591304 seconds, 47552 images\n",
            "Loaded: 1.383416 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 744: 1.072771, 1.010203 avg loss, 0.000306 rate, 0.642196 seconds, 47616 images\n",
            "Loaded: 1.539744 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 745: 0.894365, 0.998620 avg loss, 0.000308 rate, 0.619756 seconds, 47680 images\n",
            "Loaded: 1.314369 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 746: 1.056465, 1.004404 avg loss, 0.000310 rate, 0.625424 seconds, 47744 images\n",
            "Loaded: 1.351441 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 747: 1.104437, 1.014408 avg loss, 0.000311 rate, 0.596055 seconds, 47808 images\n",
            "Loaded: 1.394729 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 748: 1.004366, 1.013403 avg loss, 0.000313 rate, 0.627079 seconds, 47872 images\n",
            "Loaded: 1.399802 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 749: 0.899465, 1.002010 avg loss, 0.000315 rate, 0.687281 seconds, 47936 images\n",
            "Loaded: 1.390885 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 750: 0.878579, 0.989667 avg loss, 0.000316 rate, 0.615471 seconds, 48000 images\n",
            "Resizing\n",
            "576 x 576 \n",
            " try to allocate additional workspace_size = 4605.87 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.000056 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 751: 0.976354, 0.988335 avg loss, 0.000318 rate, 1.046804 seconds, 48064 images\n",
            "Loaded: 1.226768 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 752: 1.214900, 1.010992 avg loss, 0.000320 rate, 0.921173 seconds, 48128 images\n",
            "Loaded: 1.261010 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 753: 0.886355, 0.998528 avg loss, 0.000321 rate, 0.874893 seconds, 48192 images\n",
            "Loaded: 1.461515 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 754: 1.091274, 1.007803 avg loss, 0.000323 rate, 0.838714 seconds, 48256 images\n",
            "Loaded: 1.409720 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 755: 1.065163, 1.013539 avg loss, 0.000325 rate, 0.878264 seconds, 48320 images\n",
            "Loaded: 1.378119 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 756: 1.204867, 1.032671 avg loss, 0.000327 rate, 0.915213 seconds, 48384 images\n",
            "Loaded: 1.246308 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 757: 1.191251, 1.048529 avg loss, 0.000328 rate, 1.002190 seconds, 48448 images\n",
            "Loaded: 1.240882 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 758: 0.975550, 1.041231 avg loss, 0.000330 rate, 0.991646 seconds, 48512 images\n",
            "Loaded: 1.214302 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 759: 0.812266, 1.018335 avg loss, 0.000332 rate, 0.932535 seconds, 48576 images\n",
            "Loaded: 1.240737 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 760: 1.105521, 1.027053 avg loss, 0.000334 rate, 0.970554 seconds, 48640 images\n",
            "Resizing\n",
            "576 x 576 \n",
            " try to allocate additional workspace_size = 4605.87 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 1.364568 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 761: 1.086129, 1.032961 avg loss, 0.000335 rate, 0.919259 seconds, 48704 images\n",
            "Loaded: 1.271193 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 762: 1.091218, 1.038787 avg loss, 0.000337 rate, 0.919929 seconds, 48768 images\n",
            "Loaded: 1.421451 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 763: 0.981393, 1.033047 avg loss, 0.000339 rate, 0.912584 seconds, 48832 images\n",
            "Loaded: 1.422560 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 764: 0.977871, 1.027530 avg loss, 0.000341 rate, 0.913925 seconds, 48896 images\n",
            "Loaded: 1.244147 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 765: 1.102749, 1.035052 avg loss, 0.000342 rate, 0.990153 seconds, 48960 images\n",
            "Loaded: 1.221282 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 766: 1.009771, 1.032524 avg loss, 0.000344 rate, 0.957227 seconds, 49024 images\n",
            "Loaded: 1.242494 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 767: 0.877897, 1.017061 avg loss, 0.000346 rate, 0.814506 seconds, 49088 images\n",
            "Loaded: 1.394314 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 768: 1.011816, 1.016536 avg loss, 0.000348 rate, 0.912868 seconds, 49152 images\n",
            "Loaded: 1.353112 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 769: 1.114236, 1.026306 avg loss, 0.000350 rate, 0.904436 seconds, 49216 images\n",
            "Loaded: 1.387014 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 770: 0.979527, 1.021628 avg loss, 0.000352 rate, 0.854564 seconds, 49280 images\n",
            "Resizing\n",
            "384 x 384 \n",
            " try to allocate additional workspace_size = 1302.33 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.000033 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 771: 0.808226, 1.000288 avg loss, 0.000353 rate, 0.478658 seconds, 49344 images\n",
            "Loaded: 1.242156 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 772: 0.843851, 0.984644 avg loss, 0.000355 rate, 0.381849 seconds, 49408 images\n",
            "Loaded: 1.464658 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 773: 0.895323, 0.975712 avg loss, 0.000357 rate, 0.377703 seconds, 49472 images\n",
            "Loaded: 1.462317 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 774: 0.899771, 0.968118 avg loss, 0.000359 rate, 0.372211 seconds, 49536 images\n",
            "Loaded: 1.397664 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 775: 0.999654, 0.971272 avg loss, 0.000361 rate, 0.338867 seconds, 49600 images\n",
            "Loaded: 1.517716 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 776: 0.973580, 0.971503 avg loss, 0.000363 rate, 0.326641 seconds, 49664 images\n",
            "Loaded: 1.424300 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 777: 0.912651, 0.965617 avg loss, 0.000364 rate, 0.382245 seconds, 49728 images\n",
            "Loaded: 1.490853 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 778: 0.912688, 0.960325 avg loss, 0.000366 rate, 0.386773 seconds, 49792 images\n",
            "Loaded: 1.401028 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 779: 0.999082, 0.964200 avg loss, 0.000368 rate, 0.380707 seconds, 49856 images\n",
            "Loaded: 1.509013 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 780: 0.887433, 0.956524 avg loss, 0.000370 rate, 0.367890 seconds, 49920 images\n",
            "Resizing\n",
            "512 x 512 \n",
            " try to allocate additional workspace_size = 4596.96 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.000039 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 781: 1.679687, 1.028840 avg loss, 0.000372 rate, 0.674819 seconds, 49984 images\n",
            "Loaded: 1.393073 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 782: 1.009529, 1.026909 avg loss, 0.000374 rate, 0.640146 seconds, 50048 images\n",
            "Loaded: 1.389321 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 783: 0.823490, 1.006567 avg loss, 0.000376 rate, 0.675194 seconds, 50112 images\n",
            "Loaded: 1.352250 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 784: 1.030736, 1.008984 avg loss, 0.000378 rate, 0.681855 seconds, 50176 images\n",
            "Loaded: 1.416079 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 785: 0.895814, 0.997667 avg loss, 0.000380 rate, 0.675312 seconds, 50240 images\n",
            "Loaded: 1.403863 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 786: 0.929341, 0.990834 avg loss, 0.000382 rate, 0.660707 seconds, 50304 images\n",
            "Loaded: 1.452913 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 787: 0.872445, 0.978995 avg loss, 0.000384 rate, 0.676368 seconds, 50368 images\n",
            "Loaded: 1.396766 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 788: 0.978122, 0.978908 avg loss, 0.000386 rate, 0.657766 seconds, 50432 images\n",
            "Loaded: 1.723470 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 789: 0.963544, 0.977372 avg loss, 0.000388 rate, 0.640348 seconds, 50496 images\n",
            "Loaded: 1.600641 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 790: 0.753398, 0.954974 avg loss, 0.000390 rate, 0.802622 seconds, 50560 images\n",
            "Resizing\n",
            "352 x 352 \n",
            " try to allocate additional workspace_size = 1264.58 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.000035 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 791: 0.985299, 0.958007 avg loss, 0.000391 rate, 0.360544 seconds, 50624 images\n",
            "Loaded: 1.429295 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 792: 0.874533, 0.949659 avg loss, 0.000393 rate, 0.288416 seconds, 50688 images\n",
            "Loaded: 1.505780 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 793: 0.932440, 0.947937 avg loss, 0.000395 rate, 0.287925 seconds, 50752 images\n",
            "Loaded: 1.433089 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 794: 0.852691, 0.938413 avg loss, 0.000397 rate, 0.323463 seconds, 50816 images\n",
            "Loaded: 1.451326 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 795: 0.925790, 0.937150 avg loss, 0.000399 rate, 0.315184 seconds, 50880 images\n",
            "Loaded: 1.543130 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 796: 0.934955, 0.936931 avg loss, 0.000401 rate, 0.304089 seconds, 50944 images\n",
            "Loaded: 1.456287 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 797: 0.807866, 0.924024 avg loss, 0.000403 rate, 0.304660 seconds, 51008 images\n",
            "Loaded: 1.445477 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 798: 1.000028, 0.931625 avg loss, 0.000406 rate, 0.323202 seconds, 51072 images\n",
            "Loaded: 1.433598 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 799: 0.844966, 0.922959 avg loss, 0.000408 rate, 0.306749 seconds, 51136 images\n",
            "Loaded: 1.480602 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 800: 1.020945, 0.932757 avg loss, 0.000410 rate, 0.297372 seconds, 51200 images\n",
            "Saving weights to /content/gdrive/My Drive/darknet/backup/yolov3-tiny-280120_last.weights\n",
            "Resizing\n",
            "416 x 416 \n",
            " try to allocate additional workspace_size = 1302.33 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.000037 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 801: 0.705869, 0.910069 avg loss, 0.000412 rate, 0.462169 seconds, 51264 images\n",
            "Loaded: 1.383116 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 802: 0.880301, 0.907092 avg loss, 0.000414 rate, 0.417365 seconds, 51328 images\n",
            "Loaded: 1.512455 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 803: 0.902639, 0.906647 avg loss, 0.000416 rate, 0.429479 seconds, 51392 images\n",
            "Loaded: 1.468528 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 804: 0.817127, 0.897695 avg loss, 0.000418 rate, 0.409330 seconds, 51456 images\n",
            "Loaded: 1.419458 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 805: 1.035976, 0.911523 avg loss, 0.000420 rate, 0.450938 seconds, 51520 images\n",
            "Loaded: 1.539890 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 806: 1.052972, 0.925668 avg loss, 0.000422 rate, 0.455238 seconds, 51584 images\n",
            "Loaded: 1.409042 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 807: 0.817199, 0.914821 avg loss, 0.000424 rate, 0.448645 seconds, 51648 images\n",
            "Loaded: 1.381035 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 808: 0.850699, 0.908409 avg loss, 0.000426 rate, 0.466956 seconds, 51712 images\n",
            "Loaded: 1.511485 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 809: 0.710057, 0.888574 avg loss, 0.000428 rate, 0.492158 seconds, 51776 images\n",
            "Loaded: 1.433187 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 810: 0.861838, 0.885900 avg loss, 0.000430 rate, 0.424477 seconds, 51840 images\n",
            "Resizing\n",
            "576 x 576 \n",
            " try to allocate additional workspace_size = 4605.87 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.000036 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 811: 1.005453, 0.897855 avg loss, 0.000433 rate, 0.984320 seconds, 51904 images\n",
            "Loaded: 1.161218 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 812: 1.257462, 0.933816 avg loss, 0.000435 rate, 0.937840 seconds, 51968 images\n",
            "Loaded: 1.262823 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 813: 0.922980, 0.932732 avg loss, 0.000437 rate, 0.890752 seconds, 52032 images\n",
            "Loaded: 1.289248 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 814: 0.750370, 0.914496 avg loss, 0.000439 rate, 0.886470 seconds, 52096 images\n",
            "Loaded: 1.451146 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 815: 0.853290, 0.908375 avg loss, 0.000441 rate, 0.904435 seconds, 52160 images\n",
            "Loaded: 1.429346 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 816: 1.289496, 0.946487 avg loss, 0.000443 rate, 0.819290 seconds, 52224 images\n",
            "Loaded: 1.365171 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 817: 0.866639, 0.938503 avg loss, 0.000446 rate, 0.882820 seconds, 52288 images\n",
            "Loaded: 1.312691 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 818: 0.904335, 0.935086 avg loss, 0.000448 rate, 0.929175 seconds, 52352 images\n",
            "Loaded: 1.275250 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 819: 1.138038, 0.955381 avg loss, 0.000450 rate, 0.944322 seconds, 52416 images\n",
            "Loaded: 1.213412 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 820: 1.137622, 0.973605 avg loss, 0.000452 rate, 0.901628 seconds, 52480 images\n",
            "Resizing\n",
            "384 x 384 \n",
            " try to allocate additional workspace_size = 1302.33 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.000041 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 821: 0.788952, 0.955140 avg loss, 0.000454 rate, 0.439622 seconds, 52544 images\n",
            "Loaded: 1.311102 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 822: 0.834003, 0.943026 avg loss, 0.000457 rate, 0.414228 seconds, 52608 images\n",
            "Loaded: 1.382890 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 823: 0.987311, 0.947455 avg loss, 0.000459 rate, 0.377327 seconds, 52672 images\n",
            "Loaded: 1.461488 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 824: 0.762660, 0.928975 avg loss, 0.000461 rate, 0.365860 seconds, 52736 images\n",
            "Loaded: 1.393634 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 825: 0.894090, 0.925487 avg loss, 0.000463 rate, 0.377328 seconds, 52800 images\n",
            "Loaded: 1.458367 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 826: 0.758956, 0.908834 avg loss, 0.000466 rate, 0.395274 seconds, 52864 images\n",
            "Loaded: 1.454952 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 827: 0.825569, 0.900507 avg loss, 0.000468 rate, 0.361696 seconds, 52928 images\n",
            "Loaded: 1.466215 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 828: 0.972429, 0.907699 avg loss, 0.000470 rate, 0.339192 seconds, 52992 images\n",
            "Loaded: 1.502211 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 829: 0.692658, 0.886195 avg loss, 0.000472 rate, 0.369266 seconds, 53056 images\n",
            "Loaded: 1.438527 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 830: 0.902457, 0.887822 avg loss, 0.000475 rate, 0.368917 seconds, 53120 images\n",
            "Resizing\n",
            "352 x 352 \n",
            " try to allocate additional workspace_size = 1264.58 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 1.569666 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 831: 0.657881, 0.864828 avg loss, 0.000477 rate, 0.408676 seconds, 53184 images\n",
            "Loaded: 1.430028 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 832: 0.852362, 0.863581 avg loss, 0.000479 rate, 0.338168 seconds, 53248 images\n",
            "Loaded: 1.369357 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 833: 0.814231, 0.858646 avg loss, 0.000481 rate, 0.288666 seconds, 53312 images\n",
            "Loaded: 1.435322 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 834: 0.857772, 0.858559 avg loss, 0.000484 rate, 0.315585 seconds, 53376 images\n",
            "Loaded: 1.441677 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 835: 0.734497, 0.846152 avg loss, 0.000486 rate, 0.310992 seconds, 53440 images\n",
            "Loaded: 1.380662 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 836: 0.846691, 0.846206 avg loss, 0.000488 rate, 0.310170 seconds, 53504 images\n",
            "Loaded: 1.372351 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 837: 1.040273, 0.865613 avg loss, 0.000491 rate, 0.300299 seconds, 53568 images\n",
            "Loaded: 1.443991 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 838: 1.359853, 0.915037 avg loss, 0.000493 rate, 0.293079 seconds, 53632 images\n",
            "Loaded: 1.454901 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 839: 0.834855, 0.907019 avg loss, 0.000496 rate, 0.315743 seconds, 53696 images\n",
            "Loaded: 1.435860 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 840: 0.914468, 0.907764 avg loss, 0.000498 rate, 0.301607 seconds, 53760 images\n",
            "Resizing\n",
            "416 x 416 \n",
            " try to allocate additional workspace_size = 1302.33 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.000036 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 841: 0.978817, 0.914869 avg loss, 0.000500 rate, 0.551603 seconds, 53824 images\n",
            "Loaded: 1.414848 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 842: 0.824498, 0.905832 avg loss, 0.000503 rate, 0.454806 seconds, 53888 images\n",
            "Loaded: 1.476809 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 843: 0.744315, 0.889680 avg loss, 0.000505 rate, 0.491008 seconds, 53952 images\n",
            "Loaded: 1.453871 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 844: 0.811674, 0.881880 avg loss, 0.000507 rate, 0.413143 seconds, 54016 images\n",
            "Loaded: 1.446246 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 845: 1.013292, 0.895021 avg loss, 0.000510 rate, 0.401917 seconds, 54080 images\n",
            "Loaded: 1.451276 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 846: 0.871127, 0.892631 avg loss, 0.000512 rate, 0.461861 seconds, 54144 images\n",
            "Loaded: 1.391324 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 847: 0.912771, 0.894645 avg loss, 0.000515 rate, 0.481870 seconds, 54208 images\n",
            "Loaded: 1.367701 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 848: 0.930108, 0.898192 avg loss, 0.000517 rate, 0.429452 seconds, 54272 images\n",
            "Loaded: 1.437523 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 849: 0.848840, 0.893256 avg loss, 0.000520 rate, 0.408431 seconds, 54336 images\n",
            "Loaded: 1.578586 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 850: 0.776904, 0.881621 avg loss, 0.000522 rate, 0.438704 seconds, 54400 images\n",
            "Resizing\n",
            "416 x 416 \n",
            " try to allocate additional workspace_size = 1302.33 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 1.434242 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 851: 0.723284, 0.865788 avg loss, 0.000524 rate, 0.531006 seconds, 54464 images\n",
            "Loaded: 1.337231 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 852: 0.699265, 0.849135 avg loss, 0.000527 rate, 0.410885 seconds, 54528 images\n",
            "Loaded: 1.467007 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 853: 0.823001, 0.846522 avg loss, 0.000529 rate, 0.421784 seconds, 54592 images\n",
            "Loaded: 1.426163 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 854: 0.960693, 0.857939 avg loss, 0.000532 rate, 0.441789 seconds, 54656 images\n",
            "Loaded: 1.465983 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 855: 0.716797, 0.843825 avg loss, 0.000534 rate, 0.435021 seconds, 54720 images\n",
            "Loaded: 1.389474 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 856: 0.680105, 0.827453 avg loss, 0.000537 rate, 0.424494 seconds, 54784 images\n",
            "Loaded: 1.464911 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 857: 0.707918, 0.815499 avg loss, 0.000539 rate, 0.428838 seconds, 54848 images\n",
            "Loaded: 1.483176 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 858: 0.724013, 0.806351 avg loss, 0.000542 rate, 0.433345 seconds, 54912 images\n",
            "Loaded: 1.398727 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 859: 0.745400, 0.800256 avg loss, 0.000544 rate, 0.447613 seconds, 54976 images\n",
            "Loaded: 1.443908 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 860: 0.865173, 0.806747 avg loss, 0.000547 rate, 0.422599 seconds, 55040 images\n",
            "Resizing\n",
            "544 x 544 \n",
            " try to allocate additional workspace_size = 4601.28 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.000055 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 861: 0.744537, 0.800526 avg loss, 0.000550 rate, 0.970136 seconds, 55104 images\n",
            "Loaded: 1.141467 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 862: 0.890434, 0.809517 avg loss, 0.000552 rate, 0.890426 seconds, 55168 images\n",
            "Loaded: 1.244584 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 863: 0.796768, 0.808242 avg loss, 0.000555 rate, 0.819591 seconds, 55232 images\n",
            "Loaded: 1.293843 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 864: 0.840941, 0.811512 avg loss, 0.000557 rate, 0.931707 seconds, 55296 images\n",
            "Loaded: 1.255860 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 865: 0.892818, 0.819643 avg loss, 0.000560 rate, 0.759598 seconds, 55360 images\n",
            "Loaded: 1.374195 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 866: 0.885261, 0.826204 avg loss, 0.000562 rate, 0.813759 seconds, 55424 images\n",
            "Loaded: 1.287731 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 867: 0.848722, 0.828456 avg loss, 0.000565 rate, 0.822546 seconds, 55488 images\n",
            "Loaded: 1.322842 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 868: 0.899678, 0.835578 avg loss, 0.000568 rate, 0.786886 seconds, 55552 images\n",
            "Loaded: 1.403312 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 869: 0.756804, 0.827701 avg loss, 0.000570 rate, 0.829588 seconds, 55616 images\n",
            "Loaded: 1.320940 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 870: 0.973636, 0.842294 avg loss, 0.000573 rate, 0.884038 seconds, 55680 images\n",
            "Resizing\n",
            "416 x 416 \n",
            " try to allocate additional workspace_size = 1302.33 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 1.497739 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 871: 0.696734, 0.827738 avg loss, 0.000576 rate, 0.538850 seconds, 55744 images\n",
            "Loaded: 1.310295 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 872: 0.899618, 0.834926 avg loss, 0.000578 rate, 0.470182 seconds, 55808 images\n",
            "Loaded: 1.400013 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 873: 0.823542, 0.833788 avg loss, 0.000581 rate, 0.440724 seconds, 55872 images\n",
            "Loaded: 1.451986 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 874: 0.637116, 0.814121 avg loss, 0.000584 rate, 0.418610 seconds, 55936 images\n",
            "Loaded: 1.456385 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 875: 0.853845, 0.818093 avg loss, 0.000586 rate, 0.417950 seconds, 56000 images\n",
            "Loaded: 1.619590 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 876: 0.727811, 0.809065 avg loss, 0.000589 rate, 0.480703 seconds, 56064 images\n",
            "Loaded: 1.395173 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 877: 0.678687, 0.796027 avg loss, 0.000592 rate, 0.451861 seconds, 56128 images\n",
            "Loaded: 1.417555 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 878: 0.792672, 0.795692 avg loss, 0.000594 rate, 0.418756 seconds, 56192 images\n",
            "Loaded: 1.455835 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 879: 0.775718, 0.793694 avg loss, 0.000597 rate, 0.427756 seconds, 56256 images\n",
            "Loaded: 1.400262 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 880: 0.675355, 0.781860 avg loss, 0.000600 rate, 0.447022 seconds, 56320 images\n",
            "Resizing\n",
            "544 x 544 \n",
            " try to allocate additional workspace_size = 4601.28 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.000057 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 881: 0.710667, 0.774741 avg loss, 0.000602 rate, 0.795723 seconds, 56384 images\n",
            "Loaded: 1.382205 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 882: 0.890959, 0.786363 avg loss, 0.000605 rate, 0.740003 seconds, 56448 images\n",
            "Loaded: 1.363960 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 883: 0.938825, 0.801609 avg loss, 0.000608 rate, 0.776567 seconds, 56512 images\n",
            "Loaded: 1.332764 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 884: 0.644576, 0.785906 avg loss, 0.000611 rate, 0.807335 seconds, 56576 images\n",
            "Loaded: 1.321699 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 885: 0.968733, 0.804188 avg loss, 0.000613 rate, 0.800496 seconds, 56640 images\n",
            "Loaded: 1.428463 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 886: 0.887611, 0.812531 avg loss, 0.000616 rate, 0.796507 seconds, 56704 images\n",
            "Loaded: 1.379647 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 887: 0.694946, 0.800772 avg loss, 0.000619 rate, 0.782517 seconds, 56768 images\n",
            "Loaded: 1.373784 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 888: 0.804926, 0.801188 avg loss, 0.000622 rate, 0.877168 seconds, 56832 images\n",
            "Loaded: 1.275495 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 889: 0.915406, 0.812609 avg loss, 0.000625 rate, 0.856059 seconds, 56896 images\n",
            "Loaded: 1.275766 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 890: 0.768419, 0.808190 avg loss, 0.000627 rate, 0.777290 seconds, 56960 images\n",
            "Resizing\n",
            "544 x 544 \n",
            " try to allocate additional workspace_size = 4601.28 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 1.418064 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 891: 0.679111, 0.795282 avg loss, 0.000630 rate, 0.935266 seconds, 57024 images\n",
            "Loaded: 1.224615 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 892: 0.803003, 0.796054 avg loss, 0.000633 rate, 0.782804 seconds, 57088 images\n",
            "Loaded: 1.442193 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 893: 0.805786, 0.797028 avg loss, 0.000636 rate, 0.802319 seconds, 57152 images\n",
            "Loaded: 1.352915 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 894: 0.785017, 0.795826 avg loss, 0.000639 rate, 0.754829 seconds, 57216 images\n",
            "Loaded: 1.475792 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 895: 0.838600, 0.800104 avg loss, 0.000642 rate, 0.862351 seconds, 57280 images\n",
            "Loaded: 1.412187 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 896: 0.758771, 0.795971 avg loss, 0.000645 rate, 0.854841 seconds, 57344 images\n",
            "Loaded: 1.241367 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 897: 0.806528, 0.797026 avg loss, 0.000647 rate, 0.743759 seconds, 57408 images\n",
            "Loaded: 1.380873 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 898: 0.785252, 0.795849 avg loss, 0.000650 rate, 0.895699 seconds, 57472 images\n",
            "Loaded: 1.271682 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 899: 0.703133, 0.786577 avg loss, 0.000653 rate, 0.905275 seconds, 57536 images\n",
            "Loaded: 1.699831 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 900: 0.830445, 0.790964 avg loss, 0.000656 rate, 0.799382 seconds, 57600 images\n",
            "Saving weights to /content/gdrive/My Drive/darknet/backup/yolov3-tiny-280120_last.weights\n",
            "Resizing\n",
            "416 x 416 \n",
            " try to allocate additional workspace_size = 1302.33 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 1.574705 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 901: 1.048592, 0.816727 avg loss, 0.000659 rate, 0.508125 seconds, 57664 images\n",
            "Loaded: 1.383917 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 902: 0.668887, 0.801943 avg loss, 0.000662 rate, 0.432579 seconds, 57728 images\n",
            "Loaded: 1.411114 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 903: 0.610726, 0.782821 avg loss, 0.000665 rate, 0.474189 seconds, 57792 images\n",
            "Loaded: 1.439811 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 904: 0.771751, 0.781714 avg loss, 0.000668 rate, 0.464834 seconds, 57856 images\n",
            "Loaded: 1.375366 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 905: 0.711780, 0.774721 avg loss, 0.000671 rate, 0.419097 seconds, 57920 images\n",
            "Loaded: 1.496053 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 906: 0.795004, 0.776749 avg loss, 0.000674 rate, 0.417459 seconds, 57984 images\n",
            "Loaded: 1.440402 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 907: 0.683660, 0.767440 avg loss, 0.000677 rate, 0.471506 seconds, 58048 images\n",
            "Loaded: 1.424751 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 908: 0.979495, 0.788646 avg loss, 0.000680 rate, 0.440318 seconds, 58112 images\n",
            "Loaded: 1.325304 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 909: 0.749213, 0.784702 avg loss, 0.000683 rate, 0.413846 seconds, 58176 images\n",
            "Loaded: 1.441534 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 910: 0.742688, 0.780501 avg loss, 0.000686 rate, 0.412310 seconds, 58240 images\n",
            "Resizing\n",
            "384 x 384 \n",
            " try to allocate additional workspace_size = 1302.33 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.000133 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 911: 0.689675, 0.771418 avg loss, 0.000689 rate, 0.488125 seconds, 58304 images\n",
            "Loaded: 1.357424 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 912: 0.686988, 0.762975 avg loss, 0.000692 rate, 0.403096 seconds, 58368 images\n",
            "Loaded: 1.379079 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 913: 0.739666, 0.760644 avg loss, 0.000695 rate, 0.370320 seconds, 58432 images\n",
            "Loaded: 1.487788 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 914: 0.656843, 0.750264 avg loss, 0.000698 rate, 0.356583 seconds, 58496 images\n",
            "Loaded: 1.475645 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 915: 0.757005, 0.750938 avg loss, 0.000701 rate, 0.366701 seconds, 58560 images\n",
            "Loaded: 1.344218 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 916: 0.670956, 0.742940 avg loss, 0.000704 rate, 0.367127 seconds, 58624 images\n",
            "Loaded: 1.399277 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 917: 0.755888, 0.744235 avg loss, 0.000707 rate, 0.370029 seconds, 58688 images\n",
            "Loaded: 1.370721 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 918: 0.774410, 0.747252 avg loss, 0.000710 rate, 0.332761 seconds, 58752 images\n",
            "Loaded: 1.438760 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 919: 0.823407, 0.754868 avg loss, 0.000713 rate, 0.353615 seconds, 58816 images\n",
            "Loaded: 1.479057 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 920: 0.742766, 0.753658 avg loss, 0.000716 rate, 0.391083 seconds, 58880 images\n",
            "Resizing\n",
            "512 x 512 \n",
            " try to allocate additional workspace_size = 4596.96 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.000103 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 921: 0.812948, 0.759587 avg loss, 0.000720 rate, 0.732627 seconds, 58944 images\n",
            "Loaded: 1.371186 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 922: 0.711977, 0.754826 avg loss, 0.000723 rate, 0.681846 seconds, 59008 images\n",
            "Loaded: 1.477981 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 923: 0.713349, 0.750678 avg loss, 0.000726 rate, 0.681932 seconds, 59072 images\n",
            "Loaded: 1.325993 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 924: 0.752547, 0.750865 avg loss, 0.000729 rate, 0.637970 seconds, 59136 images\n",
            "Loaded: 1.388182 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 925: 1.006410, 0.776419 avg loss, 0.000732 rate, 0.713693 seconds, 59200 images\n",
            "Loaded: 1.381495 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 926: 0.813838, 0.780161 avg loss, 0.000735 rate, 0.780002 seconds, 59264 images\n",
            "Loaded: 1.317886 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 927: 0.672811, 0.769426 avg loss, 0.000738 rate, 0.749734 seconds, 59328 images\n",
            "Loaded: 1.309074 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 928: 0.807721, 0.773256 avg loss, 0.000742 rate, 0.851248 seconds, 59392 images\n",
            "Loaded: 1.346918 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 929: 0.688122, 0.764742 avg loss, 0.000745 rate, 0.765095 seconds, 59456 images\n",
            "Loaded: 1.340430 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 930: 0.741831, 0.762451 avg loss, 0.000748 rate, 0.744106 seconds, 59520 images\n",
            "Resizing\n",
            "544 x 544 \n",
            " try to allocate additional workspace_size = 4601.28 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.000039 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 931: 0.809359, 0.767142 avg loss, 0.000751 rate, 0.890486 seconds, 59584 images\n",
            "Loaded: 1.181848 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 932: 0.780798, 0.768508 avg loss, 0.000755 rate, 0.781424 seconds, 59648 images\n",
            "Loaded: 1.345721 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 933: 0.650086, 0.756665 avg loss, 0.000758 rate, 0.861440 seconds, 59712 images\n",
            "Loaded: 1.227055 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 934: 0.897030, 0.770702 avg loss, 0.000761 rate, 0.760035 seconds, 59776 images\n",
            "Loaded: 1.352290 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 935: 0.755797, 0.769211 avg loss, 0.000764 rate, 0.739369 seconds, 59840 images\n",
            "Loaded: 1.448112 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 936: 0.663504, 0.758641 avg loss, 0.000768 rate, 0.879528 seconds, 59904 images\n",
            "Loaded: 1.365335 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 937: 0.689944, 0.751771 avg loss, 0.000771 rate, 0.761786 seconds, 59968 images\n",
            "Loaded: 1.389877 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 938: 0.626678, 0.739262 avg loss, 0.000774 rate, 0.891311 seconds, 60032 images\n",
            "Loaded: 1.182533 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 939: 0.780186, 0.743354 avg loss, 0.000777 rate, 0.761876 seconds, 60096 images\n",
            "Loaded: 1.339420 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 940: 0.670505, 0.736069 avg loss, 0.000781 rate, 0.869814 seconds, 60160 images\n",
            "Resizing\n",
            "608 x 608 \n",
            " try to allocate additional workspace_size = 4610.72 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.000039 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 941: 0.798196, 0.742282 avg loss, 0.000784 rate, 1.029516 seconds, 60224 images\n",
            "Loaded: 1.248483 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 942: 0.701200, 0.738174 avg loss, 0.000787 rate, 0.952013 seconds, 60288 images\n",
            "Loaded: 1.412554 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 943: 0.727388, 0.737095 avg loss, 0.000791 rate, 1.105165 seconds, 60352 images\n",
            "Loaded: 1.206980 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 944: 0.718936, 0.735279 avg loss, 0.000794 rate, 1.160602 seconds, 60416 images\n",
            "Loaded: 1.157011 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 945: 0.596782, 0.721430 avg loss, 0.000797 rate, 0.997081 seconds, 60480 images\n",
            "Loaded: 1.269880 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 946: 0.740318, 0.723318 avg loss, 0.000801 rate, 1.091295 seconds, 60544 images\n",
            "Loaded: 1.150756 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 947: 0.550651, 0.706052 avg loss, 0.000804 rate, 1.140769 seconds, 60608 images\n",
            "Loaded: 1.137089 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 948: 0.699531, 0.705400 avg loss, 0.000808 rate, 1.128173 seconds, 60672 images\n",
            "Loaded: 1.121655 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 949: 0.934191, 0.728279 avg loss, 0.000811 rate, 1.035889 seconds, 60736 images\n",
            "Loaded: 1.310342 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 950: 0.879554, 0.743406 avg loss, 0.000815 rate, 1.046298 seconds, 60800 images\n",
            "Resizing\n",
            "416 x 416 \n",
            " try to allocate additional workspace_size = 1302.33 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.000047 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 951: 0.719474, 0.741013 avg loss, 0.000818 rate, 0.461990 seconds, 60864 images\n",
            "Loaded: 1.424031 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 952: 0.748235, 0.741735 avg loss, 0.000821 rate, 0.440299 seconds, 60928 images\n",
            "Loaded: 1.390216 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 953: 0.691889, 0.736751 avg loss, 0.000825 rate, 0.437183 seconds, 60992 images\n",
            "Loaded: 1.432845 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 954: 0.665540, 0.729630 avg loss, 0.000828 rate, 0.429790 seconds, 61056 images\n",
            "Loaded: 1.467965 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 955: 0.682896, 0.724956 avg loss, 0.000832 rate, 0.422736 seconds, 61120 images\n",
            "Loaded: 1.444938 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 956: 0.658996, 0.718360 avg loss, 0.000835 rate, 0.448401 seconds, 61184 images\n",
            "Loaded: 1.390618 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 957: 1.036079, 0.750132 avg loss, 0.000839 rate, 0.399300 seconds, 61248 images\n",
            "Loaded: 1.471669 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 958: 0.788283, 0.753947 avg loss, 0.000842 rate, 0.446449 seconds, 61312 images\n",
            "Loaded: 1.403231 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 959: 0.663702, 0.744923 avg loss, 0.000846 rate, 0.413251 seconds, 61376 images\n",
            "Loaded: 1.513361 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 960: 0.637950, 0.734225 avg loss, 0.000849 rate, 0.426405 seconds, 61440 images\n",
            "Resizing\n",
            "544 x 544 \n",
            " try to allocate additional workspace_size = 4601.28 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.000046 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 961: 0.769961, 0.737799 avg loss, 0.000853 rate, 0.952261 seconds, 61504 images\n",
            "Loaded: 1.140318 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 962: 0.741423, 0.738161 avg loss, 0.000856 rate, 0.828793 seconds, 61568 images\n",
            "Loaded: 1.255255 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 963: 0.608391, 0.725184 avg loss, 0.000860 rate, 0.761676 seconds, 61632 images\n",
            "Loaded: 1.323089 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 964: 0.723285, 0.724994 avg loss, 0.000864 rate, 0.853385 seconds, 61696 images\n",
            "Loaded: 1.244177 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 965: 0.696042, 0.722099 avg loss, 0.000867 rate, 0.873455 seconds, 61760 images\n",
            "Loaded: 1.236661 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 966: 0.678500, 0.717739 avg loss, 0.000871 rate, 0.869812 seconds, 61824 images\n",
            "Loaded: 1.178130 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 967: 0.716477, 0.717613 avg loss, 0.000874 rate, 0.799859 seconds, 61888 images\n",
            "Loaded: 1.393890 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 968: 0.695206, 0.715372 avg loss, 0.000878 rate, 0.856448 seconds, 61952 images\n",
            "Loaded: 1.315730 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 969: 0.701059, 0.713941 avg loss, 0.000882 rate, 0.813681 seconds, 62016 images\n",
            "Loaded: 1.417969 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 970: 0.675798, 0.710127 avg loss, 0.000885 rate, 0.893492 seconds, 62080 images\n",
            "Resizing\n",
            "416 x 416 \n",
            " try to allocate additional workspace_size = 1302.33 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 1.457584 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 971: 0.678982, 0.707012 avg loss, 0.000889 rate, 0.433692 seconds, 62144 images\n",
            "Loaded: 1.447021 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 972: 0.787821, 0.715093 avg loss, 0.000893 rate, 0.457834 seconds, 62208 images\n",
            "Loaded: 1.368032 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 973: 0.641283, 0.707712 avg loss, 0.000896 rate, 0.407617 seconds, 62272 images\n",
            "Loaded: 1.488737 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 974: 0.537169, 0.690658 avg loss, 0.000900 rate, 0.428394 seconds, 62336 images\n",
            "Loaded: 1.611145 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 975: 0.697376, 0.691330 avg loss, 0.000904 rate, 0.446716 seconds, 62400 images\n",
            "Loaded: 1.402569 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 976: 0.675969, 0.689794 avg loss, 0.000907 rate, 0.451002 seconds, 62464 images\n",
            "Loaded: 1.439486 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 977: 0.623950, 0.683209 avg loss, 0.000911 rate, 0.415219 seconds, 62528 images\n",
            "Loaded: 1.398462 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 978: 0.694620, 0.684350 avg loss, 0.000915 rate, 0.423393 seconds, 62592 images\n",
            "Loaded: 1.438502 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 979: 0.634802, 0.679395 avg loss, 0.000919 rate, 0.393401 seconds, 62656 images\n",
            "Loaded: 1.411611 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 980: 0.783073, 0.689763 avg loss, 0.000922 rate, 0.494063 seconds, 62720 images\n",
            "Resizing\n",
            "544 x 544 \n",
            " try to allocate additional workspace_size = 4601.28 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.000044 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 981: 0.638436, 0.684631 avg loss, 0.000926 rate, 0.774916 seconds, 62784 images\n",
            "Loaded: 1.318628 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 982: 0.799418, 0.696109 avg loss, 0.000930 rate, 0.785267 seconds, 62848 images\n",
            "Loaded: 1.272662 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 983: 0.871687, 0.713667 avg loss, 0.000934 rate, 0.782649 seconds, 62912 images\n",
            "Loaded: 1.336019 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 984: 0.542497, 0.696550 avg loss, 0.000938 rate, 0.804575 seconds, 62976 images\n",
            "Loaded: 1.465263 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 985: 0.779844, 0.704879 avg loss, 0.000941 rate, 0.817817 seconds, 63040 images\n",
            "Loaded: 1.229436 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 986: 0.647330, 0.699124 avg loss, 0.000945 rate, 0.783348 seconds, 63104 images\n",
            "Loaded: 1.377704 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 987: 0.684976, 0.697710 avg loss, 0.000949 rate, 0.838064 seconds, 63168 images\n",
            "Loaded: 1.277410 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 988: 0.643700, 0.692309 avg loss, 0.000953 rate, 0.833906 seconds, 63232 images\n",
            "Loaded: 1.263885 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 989: 0.774011, 0.700479 avg loss, 0.000957 rate, 0.735200 seconds, 63296 images\n",
            "Loaded: 1.372333 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 990: 0.673689, 0.697800 avg loss, 0.000961 rate, 0.839834 seconds, 63360 images\n",
            "Resizing\n",
            "384 x 384 \n",
            " try to allocate additional workspace_size = 1302.33 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.000038 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 991: 0.767574, 0.704777 avg loss, 0.000964 rate, 0.419832 seconds, 63424 images\n",
            "Loaded: 1.381296 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 992: 0.831502, 0.717450 avg loss, 0.000968 rate, 0.395163 seconds, 63488 images\n",
            "Loaded: 1.452796 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 993: 0.565000, 0.702205 avg loss, 0.000972 rate, 0.375502 seconds, 63552 images\n",
            "Loaded: 1.416945 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 994: 0.622835, 0.694268 avg loss, 0.000976 rate, 0.380106 seconds, 63616 images\n",
            "Loaded: 1.565821 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 995: 0.539672, 0.678808 avg loss, 0.000980 rate, 0.405790 seconds, 63680 images\n",
            "Loaded: 1.580229 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 996: 0.575986, 0.668526 avg loss, 0.000984 rate, 0.376594 seconds, 63744 images\n",
            "Loaded: 1.474548 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 997: 0.617305, 0.663404 avg loss, 0.000988 rate, 0.370546 seconds, 63808 images\n",
            "Loaded: 1.436762 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 998: 0.643448, 0.661408 avg loss, 0.000992 rate, 0.369774 seconds, 63872 images\n",
            "Loaded: 1.417982 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 999: 0.653093, 0.660577 avg loss, 0.000996 rate, 0.404857 seconds, 63936 images\n",
            "Loaded: 1.364554 seconds\n",
            "\n",
            " (next mAP calculation at 1000 iterations) \n",
            " 1000: 0.613575, 0.655877 avg loss, 0.001000 rate, 0.380479 seconds, 64000 images\n",
            "Resizing to initial size: 416 x 416 \n",
            " try to allocate additional workspace_size = 1302.33 MB \n",
            " CUDA allocate done! \n",
            "\n",
            " calculation mAP (mean average precision)...\n",
            "368\n",
            " detections_count = 1058, unique_truth_count = 374  \n",
            "class_id = 0, name = banana, ap = 90.91%   \t (TP = 320, FP = 50) \n",
            "\n",
            " for thresh = 0.25, precision = 0.86, recall = 0.86, F1-score = 0.86 \n",
            " for thresh = 0.25, TP = 320, FP = 50, FN = 54, average IoU = 61.94 % \n",
            "\n",
            " IoU threshold = 50 %, used Area-Under-Curve for each unique Recall \n",
            " mean average precision (mAP@0.50) = 0.909093, or 90.91 % \n",
            "Total Detection Time: 96.000000 Seconds\n",
            "\n",
            "Set -points flag:\n",
            " `-points 101` for MS COCO \n",
            " `-points 11` for PascalVOC 2007 (uncomment `difficult` in voc.data) \n",
            " `-points 0` (AUC) for ImageNet, PascalVOC 2010-2012, your custom dataset\n",
            "\n",
            " mean_average_precision (mAP@0.5) = 0.909092 \n",
            "Saving weights to /content/gdrive/My Drive/darknet/backup/yolov3-tiny-280120_1000.weights\n",
            "Saving weights to /content/gdrive/My Drive/darknet/backup/yolov3-tiny-280120_last.weights\n",
            "Resizing\n",
            "352 x 352 \n",
            " try to allocate additional workspace_size = 1264.58 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 1.478695 seconds\n",
            "\n",
            " (next mAP calculation at 1100 iterations) \n",
            " Last accuracy mAP@0.5 = 90.91 % \n",
            " 1001: 0.567882, 0.647077 avg loss, 0.001000 rate, 0.364731 seconds, 64064 images\n",
            "Loaded: 1.387307 seconds\n",
            "\n",
            " (next mAP calculation at 1100 iterations) \n",
            " Last accuracy mAP@0.5 = 90.91 % \n",
            " 1002: 0.661046, 0.648474 avg loss, 0.001000 rate, 0.344642 seconds, 64128 images\n",
            "Loaded: 1.434732 seconds\n",
            "\n",
            " (next mAP calculation at 1100 iterations) \n",
            " Last accuracy mAP@0.5 = 90.91 % \n",
            " 1003: 0.681897, 0.651816 avg loss, 0.001000 rate, 0.321019 seconds, 64192 images\n",
            "Loaded: 1.674560 seconds\n",
            "\n",
            " (next mAP calculation at 1100 iterations) \n",
            " Last accuracy mAP@0.5 = 90.91 % \n",
            " 1004: 0.669277, 0.653562 avg loss, 0.001000 rate, 0.297453 seconds, 64256 images\n",
            "Loaded: 1.651183 seconds\n",
            "\n",
            " (next mAP calculation at 1100 iterations) \n",
            " Last accuracy mAP@0.5 = 90.91 % \n",
            " 1005: 0.640347, 0.652241 avg loss, 0.001000 rate, 0.344622 seconds, 64320 images\n",
            "Loaded: 1.453468 seconds\n",
            "\n",
            " (next mAP calculation at 1100 iterations) \n",
            " Last accuracy mAP@0.5 = 90.91 % \n",
            " 1006: 0.662815, 0.653298 avg loss, 0.001000 rate, 0.302028 seconds, 64384 images\n",
            "Loaded: 1.463646 seconds\n",
            "\n",
            " (next mAP calculation at 1100 iterations) \n",
            " Last accuracy mAP@0.5 = 90.91 % \n",
            " 1007: 0.643894, 0.652358 avg loss, 0.001000 rate, 0.283158 seconds, 64448 images\n",
            "Loaded: 1.394905 seconds\n",
            "\n",
            " (next mAP calculation at 1100 iterations) \n",
            " Last accuracy mAP@0.5 = 90.91 % \n",
            " 1008: 0.555047, 0.642627 avg loss, 0.001000 rate, 0.323090 seconds, 64512 images\n",
            "Loaded: 1.446646 seconds\n",
            "\n",
            " (next mAP calculation at 1100 iterations) \n",
            " Last accuracy mAP@0.5 = 90.91 % \n",
            " 1009: 0.597573, 0.638121 avg loss, 0.001000 rate, 0.318637 seconds, 64576 images\n",
            "Loaded: 1.409974 seconds\n",
            "\n",
            " (next mAP calculation at 1100 iterations) \n",
            " Last accuracy mAP@0.5 = 90.91 % \n",
            " 1010: 0.679820, 0.642291 avg loss, 0.001000 rate, 0.301311 seconds, 64640 images\n",
            "Resizing\n",
            "480 x 480 \n",
            " try to allocate additional workspace_size = 4592.89 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.000038 seconds\n",
            "\n",
            " (next mAP calculation at 1100 iterations) \n",
            " Last accuracy mAP@0.5 = 90.91 % \n",
            " 1011: 0.744425, 0.652505 avg loss, 0.001000 rate, 0.649386 seconds, 64704 images\n",
            "Loaded: 1.318573 seconds\n",
            "\n",
            " (next mAP calculation at 1100 iterations) \n",
            " Last accuracy mAP@0.5 = 90.91 % \n",
            " 1012: 0.597329, 0.646987 avg loss, 0.001000 rate, 0.657352 seconds, 64768 images\n",
            "Loaded: 1.373416 seconds\n",
            "\n",
            " (next mAP calculation at 1100 iterations) \n",
            " Last accuracy mAP@0.5 = 90.91 % \n",
            " 1013: 0.556271, 0.637916 avg loss, 0.001000 rate, 0.662942 seconds, 64832 images\n",
            "Loaded: 1.480706 seconds\n",
            "\n",
            " (next mAP calculation at 1100 iterations) \n",
            " Last accuracy mAP@0.5 = 90.91 % \n",
            " 1014: 0.729281, 0.647052 avg loss, 0.001000 rate, 0.668461 seconds, 64896 images\n",
            "Loaded: 1.390697 seconds\n",
            "\n",
            " (next mAP calculation at 1100 iterations) \n",
            " Last accuracy mAP@0.5 = 90.91 % \n",
            " 1015: 0.656968, 0.648044 avg loss, 0.001000 rate, 0.610851 seconds, 64960 images\n",
            "Loaded: 1.439525 seconds\n",
            "\n",
            " (next mAP calculation at 1100 iterations) \n",
            " Last accuracy mAP@0.5 = 90.91 % \n",
            " 1016: 0.622191, 0.645458 avg loss, 0.001000 rate, 0.643203 seconds, 65024 images\n",
            "Loaded: 1.440068 seconds\n",
            "\n",
            " (next mAP calculation at 1100 iterations) \n",
            " Last accuracy mAP@0.5 = 90.91 % \n",
            " 1017: 0.556886, 0.636601 avg loss, 0.001000 rate, 0.618456 seconds, 65088 images\n",
            "Loaded: 1.381188 seconds\n",
            "\n",
            " (next mAP calculation at 1100 iterations) \n",
            " Last accuracy mAP@0.5 = 90.91 % \n",
            " 1018: 0.603240, 0.633265 avg loss, 0.001000 rate, 0.603438 seconds, 65152 images\n",
            "Loaded: 1.365026 seconds\n",
            "\n",
            " (next mAP calculation at 1100 iterations) \n",
            " Last accuracy mAP@0.5 = 90.91 % \n",
            " 1019: 0.615833, 0.631522 avg loss, 0.001000 rate, 0.667270 seconds, 65216 images\n",
            "Loaded: 1.327231 seconds\n",
            "\n",
            " (next mAP calculation at 1100 iterations) \n",
            " Last accuracy mAP@0.5 = 90.91 % \n",
            " 1020: 0.588979, 0.627268 avg loss, 0.001000 rate, 0.598211 seconds, 65280 images\n",
            "Resizing\n",
            "544 x 544 \n",
            " try to allocate additional workspace_size = 4601.28 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.000055 seconds\n",
            "\n",
            " (next mAP calculation at 1100 iterations) \n",
            " Last accuracy mAP@0.5 = 90.91 % \n",
            " 1021: 0.645848, 0.629126 avg loss, 0.001000 rate, 0.925003 seconds, 65344 images\n",
            "Loaded: 1.274379 seconds\n",
            "\n",
            " (next mAP calculation at 1100 iterations) \n",
            " Last accuracy mAP@0.5 = 90.91 % \n",
            " 1022: 0.642882, 0.630501 avg loss, 0.001000 rate, 0.886263 seconds, 65408 images\n",
            "Loaded: 1.281732 seconds\n",
            "\n",
            " (next mAP calculation at 1100 iterations) \n",
            " Last accuracy mAP@0.5 = 90.91 % \n",
            " 1023: 0.782748, 0.645726 avg loss, 0.001000 rate, 0.855429 seconds, 65472 images\n",
            "Loaded: 1.271272 seconds\n",
            "\n",
            " (next mAP calculation at 1100 iterations) \n",
            " Last accuracy mAP@0.5 = 90.91 % \n",
            " 1024: 0.537397, 0.634893 avg loss, 0.001000 rate, 0.886048 seconds, 65536 images\n",
            "Loaded: 1.247680 seconds\n",
            "\n",
            " (next mAP calculation at 1100 iterations) \n",
            " Last accuracy mAP@0.5 = 90.91 % \n",
            " 1025: 0.736225, 0.645026 avg loss, 0.001000 rate, 0.852546 seconds, 65600 images\n",
            "Loaded: 1.283043 seconds\n",
            "\n",
            " (next mAP calculation at 1100 iterations) \n",
            " Last accuracy mAP@0.5 = 90.91 % \n",
            " 1026: 0.574306, 0.637954 avg loss, 0.001000 rate, 0.786692 seconds, 65664 images\n",
            "Loaded: 1.318634 seconds\n",
            "\n",
            " (next mAP calculation at 1100 iterations) \n",
            " Last accuracy mAP@0.5 = 90.91 % \n",
            " 1027: 0.710178, 0.645177 avg loss, 0.001000 rate, 0.870012 seconds, 65728 images\n",
            "Loaded: 1.225819 seconds\n",
            "\n",
            " (next mAP calculation at 1100 iterations) \n",
            " Last accuracy mAP@0.5 = 90.91 % \n",
            " 1028: 0.707211, 0.651380 avg loss, 0.001000 rate, 0.917377 seconds, 65792 images\n",
            "Loaded: 1.372858 seconds\n",
            "\n",
            " (next mAP calculation at 1100 iterations) \n",
            " Last accuracy mAP@0.5 = 90.91 % \n",
            " 1029: 0.571262, 0.643368 avg loss, 0.001000 rate, 0.878459 seconds, 65856 images\n",
            "Loaded: 1.310280 seconds\n",
            "\n",
            " (next mAP calculation at 1100 iterations) \n",
            " Last accuracy mAP@0.5 = 90.91 % \n",
            " 1030: 0.562312, 0.635263 avg loss, 0.001000 rate, 0.787481 seconds, 65920 images\n",
            "Resizing\n",
            "448 x 448 \n",
            " try to allocate additional workspace_size = 1233.65 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 1.359734 seconds\n",
            "\n",
            " (next mAP calculation at 1100 iterations) \n",
            " Last accuracy mAP@0.5 = 90.91 % \n",
            " 1031: 0.643355, 0.636072 avg loss, 0.001000 rate, 0.523183 seconds, 65984 images\n",
            "Loaded: 1.373642 seconds\n",
            "\n",
            " (next mAP calculation at 1100 iterations) \n",
            " Last accuracy mAP@0.5 = 90.91 % \n",
            " 1032: 0.579684, 0.630433 avg loss, 0.001000 rate, 0.519120 seconds, 66048 images\n",
            "Loaded: 1.389874 seconds\n",
            "\n",
            " (next mAP calculation at 1100 iterations) \n",
            " Last accuracy mAP@0.5 = 90.91 % \n",
            " 1033: 0.606872, 0.628077 avg loss, 0.001000 rate, 0.507803 seconds, 66112 images\n",
            "Loaded: 1.379771 seconds\n",
            "\n",
            " (next mAP calculation at 1100 iterations) \n",
            " Last accuracy mAP@0.5 = 90.91 % \n",
            " 1034: 0.753712, 0.640640 avg loss, 0.001000 rate, 0.452868 seconds, 66176 images\n",
            "Loaded: 1.527475 seconds\n",
            "\n",
            " (next mAP calculation at 1100 iterations) \n",
            " Last accuracy mAP@0.5 = 90.91 % \n",
            " 1035: 0.552914, 0.631868 avg loss, 0.001000 rate, 0.460297 seconds, 66240 images\n",
            "Loaded: 1.515513 seconds\n",
            "\n",
            " (next mAP calculation at 1100 iterations) \n",
            " Last accuracy mAP@0.5 = 90.91 % \n",
            " 1036: 0.674204, 0.636101 avg loss, 0.001000 rate, 0.548822 seconds, 66304 images\n",
            "Loaded: 1.357436 seconds\n",
            "\n",
            " (next mAP calculation at 1100 iterations) \n",
            " Last accuracy mAP@0.5 = 90.91 % \n",
            " 1037: 0.660002, 0.638492 avg loss, 0.001000 rate, 0.553649 seconds, 66368 images\n",
            "Loaded: 1.328497 seconds\n",
            "\n",
            " (next mAP calculation at 1100 iterations) \n",
            " Last accuracy mAP@0.5 = 90.91 % \n",
            " 1038: 0.605567, 0.635199 avg loss, 0.001000 rate, 0.552791 seconds, 66432 images\n",
            "Loaded: 1.309351 seconds\n",
            "\n",
            " (next mAP calculation at 1100 iterations) \n",
            " Last accuracy mAP@0.5 = 90.91 % \n",
            " 1039: 0.589309, 0.630610 avg loss, 0.001000 rate, 0.480360 seconds, 66496 images\n",
            "Loaded: 1.490170 seconds\n",
            "\n",
            " (next mAP calculation at 1100 iterations) \n",
            " Last accuracy mAP@0.5 = 90.91 % \n",
            " 1040: 0.571899, 0.624739 avg loss, 0.001000 rate, 0.487689 seconds, 66560 images\n",
            "Resizing\n",
            "576 x 576 \n",
            " try to allocate additional workspace_size = 4605.87 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.000042 seconds\n",
            "\n",
            " (next mAP calculation at 1100 iterations) \n",
            " Last accuracy mAP@0.5 = 90.91 % \n",
            " 1041: 0.776795, 0.639944 avg loss, 0.001000 rate, 0.847410 seconds, 66624 images\n",
            "Loaded: 1.299699 seconds\n",
            "\n",
            " (next mAP calculation at 1100 iterations) \n",
            " Last accuracy mAP@0.5 = 90.91 % \n",
            " 1042: 0.527302, 0.628680 avg loss, 0.001000 rate, 0.857355 seconds, 66688 images\n",
            "Loaded: 1.320147 seconds\n",
            "\n",
            " (next mAP calculation at 1100 iterations) \n",
            " Last accuracy mAP@0.5 = 90.91 % \n",
            " 1043: 0.607873, 0.626600 avg loss, 0.001000 rate, 0.855049 seconds, 66752 images\n",
            "Loaded: 1.367737 seconds\n",
            "\n",
            " (next mAP calculation at 1100 iterations) \n",
            " Last accuracy mAP@0.5 = 90.91 % \n",
            " 1044: 0.659086, 0.629848 avg loss, 0.001000 rate, 0.900266 seconds, 66816 images\n",
            "Loaded: 1.303590 seconds\n",
            "\n",
            " (next mAP calculation at 1100 iterations) \n",
            " Last accuracy mAP@0.5 = 90.91 % \n",
            " 1045: 0.779608, 0.644824 avg loss, 0.001000 rate, 0.932474 seconds, 66880 images\n",
            "Loaded: 1.304176 seconds\n",
            "\n",
            " (next mAP calculation at 1100 iterations) \n",
            " Last accuracy mAP@0.5 = 90.91 % \n",
            " 1046: 0.670937, 0.647435 avg loss, 0.001000 rate, 0.807797 seconds, 66944 images\n",
            "Loaded: 1.367418 seconds\n",
            "\n",
            " (next mAP calculation at 1100 iterations) \n",
            " Last accuracy mAP@0.5 = 90.91 % \n",
            " 1047: 0.613200, 0.644012 avg loss, 0.001000 rate, 0.833746 seconds, 67008 images\n",
            "Loaded: 1.431998 seconds\n",
            "\n",
            " (next mAP calculation at 1100 iterations) \n",
            " Last accuracy mAP@0.5 = 90.91 % \n",
            " 1048: 0.567123, 0.636323 avg loss, 0.001000 rate, 0.917213 seconds, 67072 images\n",
            "Loaded: 1.370334 seconds\n",
            "\n",
            " (next mAP calculation at 1100 iterations) \n",
            " Last accuracy mAP@0.5 = 90.91 % \n",
            " 1049: 0.631012, 0.635792 avg loss, 0.001000 rate, 0.962720 seconds, 67136 images\n",
            "Loaded: 1.246508 seconds\n",
            "\n",
            " (next mAP calculation at 1100 iterations) \n",
            " Last accuracy mAP@0.5 = 90.91 % \n",
            " 1050: 0.597385, 0.631951 avg loss, 0.001000 rate, 0.878360 seconds, 67200 images\n",
            "Resizing\n",
            "512 x 512 \n",
            " try to allocate additional workspace_size = 4596.96 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 1.373761 seconds\n",
            "\n",
            " (next mAP calculation at 1100 iterations) \n",
            " Last accuracy mAP@0.5 = 90.91 % \n",
            " 1051: 0.604984, 0.629255 avg loss, 0.001000 rate, 0.699049 seconds, 67264 images\n",
            "Loaded: 1.435902 seconds\n",
            "\n",
            " (next mAP calculation at 1100 iterations) \n",
            " Last accuracy mAP@0.5 = 90.91 % \n",
            " 1052: 0.577305, 0.624060 avg loss, 0.001000 rate, 0.725897 seconds, 67328 images\n",
            "Loaded: 1.431195 seconds\n",
            "\n",
            " (next mAP calculation at 1100 iterations) \n",
            " Last accuracy mAP@0.5 = 90.91 % \n",
            " 1053: 0.644783, 0.626132 avg loss, 0.001000 rate, 0.691760 seconds, 67392 images\n",
            "Loaded: 1.467809 seconds\n",
            "\n",
            " (next mAP calculation at 1100 iterations) \n",
            " Last accuracy mAP@0.5 = 90.91 % \n",
            " 1054: 0.605853, 0.624104 avg loss, 0.001000 rate, 0.732059 seconds, 67456 images\n",
            "Loaded: 1.302209 seconds\n",
            "\n",
            " (next mAP calculation at 1100 iterations) \n",
            " Last accuracy mAP@0.5 = 90.91 % \n",
            " 1055: 0.605970, 0.622291 avg loss, 0.001000 rate, 0.656306 seconds, 67520 images\n",
            "Loaded: 1.450598 seconds\n",
            "\n",
            " (next mAP calculation at 1100 iterations) \n",
            " Last accuracy mAP@0.5 = 90.91 % \n",
            " 1056: 0.578583, 0.617920 avg loss, 0.001000 rate, 0.640457 seconds, 67584 images\n",
            "Loaded: 1.348342 seconds\n",
            "\n",
            " (next mAP calculation at 1100 iterations) \n",
            " Last accuracy mAP@0.5 = 90.91 % \n",
            " 1057: 0.553939, 0.611522 avg loss, 0.001000 rate, 0.649301 seconds, 67648 images\n",
            "Loaded: 1.375002 seconds\n",
            "\n",
            " (next mAP calculation at 1100 iterations) \n",
            " Last accuracy mAP@0.5 = 90.91 % \n",
            " 1058: 0.607641, 0.611134 avg loss, 0.001000 rate, 0.693344 seconds, 67712 images\n",
            "Loaded: 1.434156 seconds\n",
            "\n",
            " (next mAP calculation at 1100 iterations) \n",
            " Last accuracy mAP@0.5 = 90.91 % \n",
            " 1059: 0.653076, 0.615328 avg loss, 0.001000 rate, 0.691649 seconds, 67776 images\n",
            "Loaded: 1.465878 seconds\n",
            "\n",
            " (next mAP calculation at 1100 iterations) \n",
            " Last accuracy mAP@0.5 = 90.91 % \n",
            " 1060: 0.527349, 0.606530 avg loss, 0.001000 rate, 0.659117 seconds, 67840 images\n",
            "Resizing\n",
            "352 x 352 \n",
            " try to allocate additional workspace_size = 1264.58 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.000034 seconds\n",
            "\n",
            " (next mAP calculation at 1100 iterations) \n",
            " Last accuracy mAP@0.5 = 90.91 % \n",
            " 1061: 0.602104, 0.606087 avg loss, 0.001000 rate, 0.361223 seconds, 67904 images\n",
            "Loaded: 1.381547 seconds\n",
            "\n",
            " (next mAP calculation at 1100 iterations) \n",
            " Last accuracy mAP@0.5 = 90.91 % \n",
            " 1062: 0.718297, 0.617308 avg loss, 0.001000 rate, 0.302275 seconds, 67968 images\n",
            "Loaded: 1.447847 seconds\n",
            "\n",
            " (next mAP calculation at 1100 iterations) \n",
            " Last accuracy mAP@0.5 = 90.91 % \n",
            " 1063: 0.548235, 0.610401 avg loss, 0.001000 rate, 0.300157 seconds, 68032 images\n",
            "Loaded: 1.544478 seconds\n",
            "\n",
            " (next mAP calculation at 1100 iterations) \n",
            " Last accuracy mAP@0.5 = 90.91 % \n",
            " 1064: 0.612485, 0.610609 avg loss, 0.001000 rate, 0.285243 seconds, 68096 images\n",
            "Loaded: 1.401166 seconds\n",
            "\n",
            " (next mAP calculation at 1100 iterations) \n",
            " Last accuracy mAP@0.5 = 90.91 % \n",
            " 1065: 0.598310, 0.609380 avg loss, 0.001000 rate, 0.292533 seconds, 68160 images\n",
            "Loaded: 1.498867 seconds\n",
            "\n",
            " (next mAP calculation at 1100 iterations) \n",
            " Last accuracy mAP@0.5 = 90.91 % \n",
            " 1066: 0.481096, 0.596551 avg loss, 0.001000 rate, 0.289961 seconds, 68224 images\n",
            "Loaded: 1.481887 seconds\n",
            "\n",
            " (next mAP calculation at 1100 iterations) \n",
            " Last accuracy mAP@0.5 = 90.91 % \n",
            " 1067: 0.558630, 0.592759 avg loss, 0.001000 rate, 0.306467 seconds, 68288 images\n",
            "Loaded: 1.447028 seconds\n",
            "\n",
            " (next mAP calculation at 1100 iterations) \n",
            " Last accuracy mAP@0.5 = 90.91 % \n",
            " 1068: 0.636611, 0.597144 avg loss, 0.001000 rate, 0.281384 seconds, 68352 images\n",
            "Loaded: 1.470131 seconds\n",
            "\n",
            " (next mAP calculation at 1100 iterations) \n",
            " Last accuracy mAP@0.5 = 90.91 % \n",
            " 1069: 0.544300, 0.591860 avg loss, 0.001000 rate, 0.290527 seconds, 68416 images\n",
            "Loaded: 1.420839 seconds\n",
            "\n",
            " (next mAP calculation at 1100 iterations) \n",
            " Last accuracy mAP@0.5 = 90.91 % \n",
            " 1070: 0.602786, 0.592952 avg loss, 0.001000 rate, 0.327950 seconds, 68480 images\n",
            "Resizing\n",
            "544 x 544 \n",
            " try to allocate additional workspace_size = 4601.28 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.000044 seconds\n",
            "\n",
            " (next mAP calculation at 1100 iterations) \n",
            " Last accuracy mAP@0.5 = 90.91 % \n",
            " 1071: 0.553803, 0.589038 avg loss, 0.001000 rate, 0.810861 seconds, 68544 images\n",
            "Loaded: 1.374904 seconds\n",
            "\n",
            " (next mAP calculation at 1100 iterations) \n",
            " Last accuracy mAP@0.5 = 90.91 % \n",
            " 1072: 0.547364, 0.584870 avg loss, 0.001000 rate, 0.748191 seconds, 68608 images\n",
            "Loaded: 1.382472 seconds\n",
            "\n",
            " (next mAP calculation at 1100 iterations) \n",
            " Last accuracy mAP@0.5 = 90.91 % \n",
            " 1073: 0.594350, 0.585818 avg loss, 0.001000 rate, 0.781279 seconds, 68672 images\n",
            "Loaded: 1.385530 seconds\n",
            "\n",
            " (next mAP calculation at 1100 iterations) \n",
            " Last accuracy mAP@0.5 = 90.91 % \n",
            " 1074: 0.619805, 0.589217 avg loss, 0.001000 rate, 0.838634 seconds, 68736 images\n",
            "Loaded: 1.195371 seconds\n",
            "\n",
            " (next mAP calculation at 1100 iterations) \n",
            " Last accuracy mAP@0.5 = 90.91 % \n",
            " 1075: 0.563259, 0.586621 avg loss, 0.001000 rate, 0.889980 seconds, 68800 images\n",
            "Loaded: 1.244131 seconds\n",
            "\n",
            " (next mAP calculation at 1100 iterations) \n",
            " Last accuracy mAP@0.5 = 90.91 % \n",
            " 1076: 0.551398, 0.583099 avg loss, 0.001000 rate, 0.764766 seconds, 68864 images\n",
            "Loaded: 1.329611 seconds\n",
            "\n",
            " (next mAP calculation at 1100 iterations) \n",
            " Last accuracy mAP@0.5 = 90.91 % \n",
            " 1077: 0.663876, 0.591177 avg loss, 0.001000 rate, 0.797072 seconds, 68928 images\n",
            "Loaded: 1.397131 seconds\n",
            "\n",
            " (next mAP calculation at 1100 iterations) \n",
            " Last accuracy mAP@0.5 = 90.91 % \n",
            " 1078: 0.612814, 0.593340 avg loss, 0.001000 rate, 0.819150 seconds, 68992 images\n",
            "Loaded: 1.384737 seconds\n",
            "\n",
            " (next mAP calculation at 1100 iterations) \n",
            " Last accuracy mAP@0.5 = 90.91 % \n",
            " 1079: 0.509008, 0.584907 avg loss, 0.001000 rate, 0.809465 seconds, 69056 images\n",
            "Loaded: 1.362282 seconds\n",
            "\n",
            " (next mAP calculation at 1100 iterations) \n",
            " Last accuracy mAP@0.5 = 90.91 % \n",
            " 1080: 0.662247, 0.592641 avg loss, 0.001000 rate, 0.905869 seconds, 69120 images\n",
            "Resizing\n",
            "384 x 384 \n",
            " try to allocate additional workspace_size = 1302.33 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.000035 seconds\n",
            "\n",
            " (next mAP calculation at 1100 iterations) \n",
            " Last accuracy mAP@0.5 = 90.91 % \n",
            " 1081: 0.526380, 0.586015 avg loss, 0.001000 rate, 0.435002 seconds, 69184 images\n",
            "Loaded: 1.337982 seconds\n",
            "\n",
            " (next mAP calculation at 1100 iterations) \n",
            " Last accuracy mAP@0.5 = 90.91 % \n",
            " 1082: 0.610194, 0.588433 avg loss, 0.001000 rate, 0.384774 seconds, 69248 images\n",
            "Loaded: 1.464286 seconds\n",
            "\n",
            " (next mAP calculation at 1100 iterations) \n",
            " Last accuracy mAP@0.5 = 90.91 % \n",
            " 1083: 0.417675, 0.571357 avg loss, 0.001000 rate, 0.352521 seconds, 69312 images\n",
            "Loaded: 1.452523 seconds\n",
            "\n",
            " (next mAP calculation at 1100 iterations) \n",
            " Last accuracy mAP@0.5 = 90.91 % \n",
            " 1084: 0.616403, 0.575862 avg loss, 0.001000 rate, 0.430779 seconds, 69376 images\n",
            "Loaded: 1.443970 seconds\n",
            "\n",
            " (next mAP calculation at 1100 iterations) \n",
            " Last accuracy mAP@0.5 = 90.91 % \n",
            " 1085: 0.595919, 0.577867 avg loss, 0.001000 rate, 0.404713 seconds, 69440 images\n",
            "Loaded: 1.549883 seconds\n",
            "\n",
            " (next mAP calculation at 1100 iterations) \n",
            " Last accuracy mAP@0.5 = 90.91 % \n",
            " 1086: 0.563132, 0.576394 avg loss, 0.001000 rate, 0.352582 seconds, 69504 images\n",
            "Loaded: 1.444839 seconds\n",
            "\n",
            " (next mAP calculation at 1100 iterations) \n",
            " Last accuracy mAP@0.5 = 90.91 % \n",
            " 1087: 0.711858, 0.589940 avg loss, 0.001000 rate, 0.383528 seconds, 69568 images\n",
            "Loaded: 1.495886 seconds\n",
            "\n",
            " (next mAP calculation at 1100 iterations) \n",
            " Last accuracy mAP@0.5 = 90.91 % \n",
            " 1088: 0.449696, 0.575916 avg loss, 0.001000 rate, 0.345306 seconds, 69632 images\n",
            "Loaded: 1.454963 seconds\n",
            "\n",
            " (next mAP calculation at 1100 iterations) \n",
            " Last accuracy mAP@0.5 = 90.91 % \n",
            " 1089: 0.586411, 0.576965 avg loss, 0.001000 rate, 0.363606 seconds, 69696 images\n",
            "Loaded: 1.478918 seconds\n",
            "\n",
            " (next mAP calculation at 1100 iterations) \n",
            " Last accuracy mAP@0.5 = 90.91 % \n",
            " 1090: 0.643018, 0.583571 avg loss, 0.001000 rate, 0.347748 seconds, 69760 images\n",
            "Resizing\n",
            "384 x 384 \n",
            " try to allocate additional workspace_size = 1302.33 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 1.518408 seconds\n",
            "\n",
            " (next mAP calculation at 1100 iterations) \n",
            " Last accuracy mAP@0.5 = 90.91 % \n",
            " 1091: 0.681105, 0.593324 avg loss, 0.001000 rate, 0.452589 seconds, 69824 images\n",
            "Loaded: 1.418268 seconds\n",
            "\n",
            " (next mAP calculation at 1100 iterations) \n",
            " Last accuracy mAP@0.5 = 90.91 % \n",
            " 1092: 0.724250, 0.606417 avg loss, 0.001000 rate, 0.352332 seconds, 69888 images\n",
            "Loaded: 1.454821 seconds\n",
            "\n",
            " (next mAP calculation at 1100 iterations) \n",
            " Last accuracy mAP@0.5 = 90.91 % \n",
            " 1093: 0.486423, 0.594417 avg loss, 0.001000 rate, 0.375027 seconds, 69952 images\n",
            "Loaded: 1.464327 seconds\n",
            "\n",
            " (next mAP calculation at 1100 iterations) \n",
            " Last accuracy mAP@0.5 = 90.91 % \n",
            " 1094: 0.603016, 0.595277 avg loss, 0.001000 rate, 0.353771 seconds, 70016 images\n",
            "Loaded: 1.477291 seconds\n",
            "\n",
            " (next mAP calculation at 1100 iterations) \n",
            " Last accuracy mAP@0.5 = 90.91 % \n",
            " 1095: 0.512619, 0.587011 avg loss, 0.001000 rate, 0.387574 seconds, 70080 images\n",
            "Loaded: 1.390522 seconds\n",
            "\n",
            " (next mAP calculation at 1100 iterations) \n",
            " Last accuracy mAP@0.5 = 90.91 % \n",
            " 1096: 0.600656, 0.588376 avg loss, 0.001000 rate, 0.353491 seconds, 70144 images\n",
            "Loaded: 1.577826 seconds\n",
            "\n",
            " (next mAP calculation at 1100 iterations) \n",
            " Last accuracy mAP@0.5 = 90.91 % \n",
            " 1097: 0.505488, 0.580087 avg loss, 0.001000 rate, 0.356771 seconds, 70208 images\n",
            "Loaded: 1.453480 seconds\n",
            "\n",
            " (next mAP calculation at 1100 iterations) \n",
            " Last accuracy mAP@0.5 = 90.91 % \n",
            " 1098: 0.511793, 0.573258 avg loss, 0.001000 rate, 0.374535 seconds, 70272 images\n",
            "Loaded: 1.449683 seconds\n",
            "\n",
            " (next mAP calculation at 1100 iterations) \n",
            " Last accuracy mAP@0.5 = 90.91 % \n",
            " 1099: 0.611500, 0.577082 avg loss, 0.001000 rate, 0.391571 seconds, 70336 images\n",
            "Loaded: 1.389424 seconds\n",
            "\n",
            " (next mAP calculation at 1100 iterations) \n",
            " Last accuracy mAP@0.5 = 90.91 % \n",
            " 1100: 0.568504, 0.576224 avg loss, 0.001000 rate, 0.364711 seconds, 70400 images\n",
            "Resizing to initial size: 416 x 416 \n",
            " try to allocate additional workspace_size = 1302.33 MB \n",
            " CUDA allocate done! \n",
            "\n",
            " calculation mAP (mean average precision)...\n",
            "368\n",
            " detections_count = 833, unique_truth_count = 374  \n",
            "class_id = 0, name = banana, ap = 96.12%   \t (TP = 345, FP = 29) \n",
            "\n",
            " for thresh = 0.25, precision = 0.92, recall = 0.92, F1-score = 0.92 \n",
            " for thresh = 0.25, TP = 345, FP = 29, FN = 29, average IoU = 67.88 % \n",
            "\n",
            " IoU threshold = 50 %, used Area-Under-Curve for each unique Recall \n",
            " mean average precision (mAP@0.50) = 0.961191, or 96.12 % \n",
            "Total Detection Time: 15.000000 Seconds\n",
            "\n",
            "Set -points flag:\n",
            " `-points 101` for MS COCO \n",
            " `-points 11` for PascalVOC 2007 (uncomment `difficult` in voc.data) \n",
            " `-points 0` (AUC) for ImageNet, PascalVOC 2010-2012, your custom dataset\n",
            "\n",
            " mean_average_precision (mAP@0.5) = 0.961191 \n",
            "Saving weights to /content/gdrive/My Drive/darknet/backup/yolov3-tiny-280120_last.weights\n",
            "Resizing\n",
            "544 x 544 \n",
            " try to allocate additional workspace_size = 4601.28 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.000211 seconds\n",
            "\n",
            " (next mAP calculation at 1200 iterations) \n",
            " Last accuracy mAP@0.5 = 96.12 % \n",
            " 1101: 0.644473, 0.583049 avg loss, 0.001000 rate, 0.848905 seconds, 70464 images\n",
            "Loaded: 1.308991 seconds\n",
            "\n",
            " (next mAP calculation at 1200 iterations) \n",
            " Last accuracy mAP@0.5 = 96.12 % \n",
            " 1102: 0.589543, 0.583698 avg loss, 0.001000 rate, 0.791544 seconds, 70528 images\n",
            "Loaded: 1.362670 seconds\n",
            "\n",
            " (next mAP calculation at 1200 iterations) \n",
            " Last accuracy mAP@0.5 = 96.12 % \n",
            " 1103: 0.628371, 0.588165 avg loss, 0.001000 rate, 0.786417 seconds, 70592 images\n",
            "Loaded: 1.491476 seconds\n",
            "\n",
            " (next mAP calculation at 1200 iterations) \n",
            " Last accuracy mAP@0.5 = 96.12 % \n",
            " 1104: 0.519279, 0.581277 avg loss, 0.001000 rate, 0.745545 seconds, 70656 images\n",
            "Loaded: 1.379117 seconds\n",
            "\n",
            " (next mAP calculation at 1200 iterations) \n",
            " Last accuracy mAP@0.5 = 96.12 % \n",
            " 1105: 0.591434, 0.582293 avg loss, 0.001000 rate, 0.840434 seconds, 70720 images\n",
            "Loaded: 1.238196 seconds\n",
            "\n",
            " (next mAP calculation at 1200 iterations) \n",
            " Last accuracy mAP@0.5 = 96.12 % \n",
            " 1106: 0.552781, 0.579341 avg loss, 0.001000 rate, 0.783417 seconds, 70784 images\n",
            "Loaded: 1.384585 seconds\n",
            "\n",
            " (next mAP calculation at 1200 iterations) \n",
            " Last accuracy mAP@0.5 = 96.12 % \n",
            " 1107: 0.507576, 0.572165 avg loss, 0.001000 rate, 0.848951 seconds, 70848 images\n",
            "Loaded: 1.325684 seconds\n",
            "\n",
            " (next mAP calculation at 1200 iterations) \n",
            " Last accuracy mAP@0.5 = 96.12 % \n",
            " 1108: 0.566937, 0.571642 avg loss, 0.001000 rate, 0.865770 seconds, 70912 images\n",
            "Loaded: 1.287835 seconds\n",
            "\n",
            " (next mAP calculation at 1200 iterations) \n",
            " Last accuracy mAP@0.5 = 96.12 % \n",
            " 1109: 0.521005, 0.566578 avg loss, 0.001000 rate, 0.799545 seconds, 70976 images\n",
            "Loaded: 1.300636 seconds\n",
            "\n",
            " (next mAP calculation at 1200 iterations) \n",
            " Last accuracy mAP@0.5 = 96.12 % \n",
            " 1110: 0.526572, 0.562578 avg loss, 0.001000 rate, 0.841371 seconds, 71040 images\n",
            "Resizing\n",
            "448 x 448 \n",
            " try to allocate additional workspace_size = 1233.65 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 1.492488 seconds\n",
            "\n",
            " (next mAP calculation at 1200 iterations) \n",
            " Last accuracy mAP@0.5 = 96.12 % \n",
            " 1111: 0.576828, 0.564003 avg loss, 0.001000 rate, 0.509020 seconds, 71104 images\n",
            "Loaded: 1.398628 seconds\n",
            "\n",
            " (next mAP calculation at 1200 iterations) \n",
            " Last accuracy mAP@0.5 = 96.12 % \n",
            " 1112: 0.576024, 0.565205 avg loss, 0.001000 rate, 0.485549 seconds, 71168 images\n",
            "Loaded: 1.391237 seconds\n",
            "\n",
            " (next mAP calculation at 1200 iterations) \n",
            " Last accuracy mAP@0.5 = 96.12 % \n",
            " 1113: 0.413960, 0.550080 avg loss, 0.001000 rate, 0.577293 seconds, 71232 images\n",
            "Loaded: 1.459053 seconds\n",
            "\n",
            " (next mAP calculation at 1200 iterations) \n",
            " Last accuracy mAP@0.5 = 96.12 % \n",
            " 1114: 0.613066, 0.556379 avg loss, 0.001000 rate, 0.475338 seconds, 71296 images\n",
            "Loaded: 1.453501 seconds\n",
            "\n",
            " (next mAP calculation at 1200 iterations) \n",
            " Last accuracy mAP@0.5 = 96.12 % \n",
            " 1115: 0.614551, 0.562196 avg loss, 0.001000 rate, 0.535006 seconds, 71360 images\n",
            "Loaded: 1.386399 seconds\n",
            "\n",
            " (next mAP calculation at 1200 iterations) \n",
            " Last accuracy mAP@0.5 = 96.12 % \n",
            " 1116: 0.418066, 0.547783 avg loss, 0.001000 rate, 0.501271 seconds, 71424 images\n",
            "Loaded: 1.491121 seconds\n",
            "\n",
            " (next mAP calculation at 1200 iterations) \n",
            " Last accuracy mAP@0.5 = 96.12 % \n",
            " 1117: 0.564066, 0.549411 avg loss, 0.001000 rate, 0.465532 seconds, 71488 images\n",
            "Loaded: 1.455843 seconds\n",
            "\n",
            " (next mAP calculation at 1200 iterations) \n",
            " Last accuracy mAP@0.5 = 96.12 % \n",
            " 1118: 0.593248, 0.553795 avg loss, 0.001000 rate, 0.549608 seconds, 71552 images\n",
            "Loaded: 1.393854 seconds\n",
            "\n",
            " (next mAP calculation at 1200 iterations) \n",
            " Last accuracy mAP@0.5 = 96.12 % \n",
            " 1119: 0.643235, 0.562739 avg loss, 0.001000 rate, 0.486087 seconds, 71616 images\n",
            "Loaded: 1.405120 seconds\n",
            "\n",
            " (next mAP calculation at 1200 iterations) \n",
            " Last accuracy mAP@0.5 = 96.12 % \n",
            " 1120: 0.488659, 0.555331 avg loss, 0.001000 rate, 0.484979 seconds, 71680 images\n",
            "Resizing\n",
            "384 x 384 \n",
            " try to allocate additional workspace_size = 1302.33 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.000034 seconds\n",
            "\n",
            " (next mAP calculation at 1200 iterations) \n",
            " Last accuracy mAP@0.5 = 96.12 % \n",
            " 1121: 0.500227, 0.549821 avg loss, 0.001000 rate, 0.414085 seconds, 71744 images\n",
            "Loaded: 1.429810 seconds\n",
            "\n",
            " (next mAP calculation at 1200 iterations) \n",
            " Last accuracy mAP@0.5 = 96.12 % \n",
            " 1122: 0.700162, 0.564855 avg loss, 0.001000 rate, 0.378134 seconds, 71808 images\n",
            "Loaded: 1.392634 seconds\n",
            "\n",
            " (next mAP calculation at 1200 iterations) \n",
            " Last accuracy mAP@0.5 = 96.12 % \n",
            " 1123: 0.542084, 0.562578 avg loss, 0.001000 rate, 0.356478 seconds, 71872 images\n",
            "Loaded: 1.540460 seconds\n",
            "\n",
            " (next mAP calculation at 1200 iterations) \n",
            " Last accuracy mAP@0.5 = 96.12 % \n",
            " 1124: 0.488790, 0.555199 avg loss, 0.001000 rate, 0.392252 seconds, 71936 images\n",
            "Loaded: 1.498860 seconds\n",
            "\n",
            " (next mAP calculation at 1200 iterations) \n",
            " Last accuracy mAP@0.5 = 96.12 % \n",
            " 1125: 0.673275, 0.567007 avg loss, 0.001000 rate, 0.373916 seconds, 72000 images\n",
            "Loaded: 1.439409 seconds\n",
            "\n",
            " (next mAP calculation at 1200 iterations) \n",
            " Last accuracy mAP@0.5 = 96.12 % \n",
            " 1126: 0.694732, 0.579779 avg loss, 0.001000 rate, 0.358545 seconds, 72064 images\n",
            "Loaded: 1.422071 seconds\n",
            "\n",
            " (next mAP calculation at 1200 iterations) \n",
            " Last accuracy mAP@0.5 = 96.12 % \n",
            " 1127: 0.494137, 0.571215 avg loss, 0.001000 rate, 0.374785 seconds, 72128 images\n",
            "Loaded: 1.483975 seconds\n",
            "\n",
            " (next mAP calculation at 1200 iterations) \n",
            " Last accuracy mAP@0.5 = 96.12 % \n",
            " 1128: 0.557791, 0.569872 avg loss, 0.001000 rate, 0.370606 seconds, 72192 images\n",
            "Loaded: 1.480837 seconds\n",
            "\n",
            " (next mAP calculation at 1200 iterations) \n",
            " Last accuracy mAP@0.5 = 96.12 % \n",
            " 1129: 0.587448, 0.571630 avg loss, 0.001000 rate, 0.349476 seconds, 72256 images\n",
            "Loaded: 1.534654 seconds\n",
            "\n",
            " (next mAP calculation at 1200 iterations) \n",
            " Last accuracy mAP@0.5 = 96.12 % \n",
            " 1130: 0.489345, 0.563401 avg loss, 0.001000 rate, 0.351410 seconds, 72320 images\n",
            "Resizing\n",
            "608 x 608 \n",
            " try to allocate additional workspace_size = 4610.72 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.000035 seconds\n",
            "\n",
            " (next mAP calculation at 1200 iterations) \n",
            " Last accuracy mAP@0.5 = 96.12 % \n",
            " 1131: 0.647176, 0.571779 avg loss, 0.001000 rate, 1.217240 seconds, 72384 images\n",
            "Loaded: 0.950660 seconds\n",
            "\n",
            " (next mAP calculation at 1200 iterations) \n",
            " Last accuracy mAP@0.5 = 96.12 % \n",
            " 1132: 0.630355, 0.577637 avg loss, 0.001000 rate, 1.100399 seconds, 72448 images\n",
            "Loaded: 1.102150 seconds\n",
            "\n",
            " (next mAP calculation at 1200 iterations) \n",
            " Last accuracy mAP@0.5 = 96.12 % \n",
            " 1133: 0.632076, 0.583080 avg loss, 0.001000 rate, 1.016485 seconds, 72512 images\n",
            "Loaded: 1.282890 seconds\n",
            "\n",
            " (next mAP calculation at 1200 iterations) \n",
            " Last accuracy mAP@0.5 = 96.12 % \n",
            " 1134: 0.478132, 0.572586 avg loss, 0.001000 rate, 1.089813 seconds, 72576 images\n",
            "Loaded: 1.179065 seconds\n",
            "\n",
            " (next mAP calculation at 1200 iterations) \n",
            " Last accuracy mAP@0.5 = 96.12 % \n",
            " 1135: 0.603925, 0.575720 avg loss, 0.001000 rate, 1.145634 seconds, 72640 images\n",
            "Loaded: 1.124134 seconds\n",
            "\n",
            " (next mAP calculation at 1200 iterations) \n",
            " Last accuracy mAP@0.5 = 96.12 % \n",
            " 1136: 0.647531, 0.582901 avg loss, 0.001000 rate, 0.985939 seconds, 72704 images\n",
            "Loaded: 1.316035 seconds\n",
            "\n",
            " (next mAP calculation at 1200 iterations) \n",
            " Last accuracy mAP@0.5 = 96.12 % \n",
            " 1137: 0.609975, 0.585608 avg loss, 0.001000 rate, 1.126570 seconds, 72768 images\n",
            "Loaded: 1.137062 seconds\n",
            "\n",
            " (next mAP calculation at 1200 iterations) \n",
            " Last accuracy mAP@0.5 = 96.12 % \n",
            " 1138: 0.795371, 0.606584 avg loss, 0.001000 rate, 1.039513 seconds, 72832 images\n",
            "Loaded: 1.265637 seconds\n",
            "\n",
            " (next mAP calculation at 1200 iterations) \n",
            " Last accuracy mAP@0.5 = 96.12 % \n",
            " 1139: 0.681138, 0.614040 avg loss, 0.001000 rate, 1.120146 seconds, 72896 images\n",
            "Loaded: 1.317677 seconds\n",
            "\n",
            " (next mAP calculation at 1200 iterations) \n",
            " Last accuracy mAP@0.5 = 96.12 % \n",
            " 1140: 0.513665, 0.604002 avg loss, 0.001000 rate, 1.055628 seconds, 72960 images\n",
            "Resizing\n",
            "544 x 544 \n",
            " try to allocate additional workspace_size = 4601.28 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 1.452333 seconds\n",
            "\n",
            " (next mAP calculation at 1200 iterations) \n",
            " Last accuracy mAP@0.5 = 96.12 % \n",
            " 1141: 0.587093, 0.602311 avg loss, 0.001000 rate, 0.900597 seconds, 73024 images\n",
            "Loaded: 1.254692 seconds\n",
            "\n",
            " (next mAP calculation at 1200 iterations) \n",
            " Last accuracy mAP@0.5 = 96.12 % \n",
            " 1142: 0.482508, 0.590331 avg loss, 0.001000 rate, 0.776631 seconds, 73088 images\n",
            "Loaded: 1.352844 seconds\n",
            "\n",
            " (next mAP calculation at 1200 iterations) \n",
            " Last accuracy mAP@0.5 = 96.12 % \n",
            " 1143: 0.610569, 0.592355 avg loss, 0.001000 rate, 0.885243 seconds, 73152 images\n",
            "Loaded: 1.260804 seconds\n",
            "\n",
            " (next mAP calculation at 1200 iterations) \n",
            " Last accuracy mAP@0.5 = 96.12 % \n",
            " 1144: 0.523271, 0.585446 avg loss, 0.001000 rate, 0.778783 seconds, 73216 images\n",
            "Loaded: 1.371617 seconds\n",
            "\n",
            " (next mAP calculation at 1200 iterations) \n",
            " Last accuracy mAP@0.5 = 96.12 % \n",
            " 1145: 0.514018, 0.578304 avg loss, 0.001000 rate, 0.799094 seconds, 73280 images\n",
            "Loaded: 1.362000 seconds\n",
            "\n",
            " (next mAP calculation at 1200 iterations) \n",
            " Last accuracy mAP@0.5 = 96.12 % \n",
            " 1146: 0.610245, 0.581498 avg loss, 0.001000 rate, 0.836809 seconds, 73344 images\n",
            "Loaded: 1.250950 seconds\n",
            "\n",
            " (next mAP calculation at 1200 iterations) \n",
            " Last accuracy mAP@0.5 = 96.12 % \n",
            " 1147: 0.485758, 0.571924 avg loss, 0.001000 rate, 0.830053 seconds, 73408 images\n",
            "Loaded: 1.242531 seconds\n",
            "\n",
            " (next mAP calculation at 1200 iterations) \n",
            " Last accuracy mAP@0.5 = 96.12 % \n",
            " 1148: 0.508711, 0.565603 avg loss, 0.001000 rate, 0.841571 seconds, 73472 images\n",
            "Loaded: 1.431158 seconds\n",
            "\n",
            " (next mAP calculation at 1200 iterations) \n",
            " Last accuracy mAP@0.5 = 96.12 % \n",
            " 1149: 0.449189, 0.553961 avg loss, 0.001000 rate, 0.911245 seconds, 73536 images\n",
            "Loaded: 1.348952 seconds\n",
            "\n",
            " (next mAP calculation at 1200 iterations) \n",
            " Last accuracy mAP@0.5 = 96.12 % \n",
            " 1150: 0.583101, 0.556875 avg loss, 0.001000 rate, 0.849827 seconds, 73600 images\n",
            "Resizing\n",
            "416 x 416 \n",
            " try to allocate additional workspace_size = 1302.33 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.000039 seconds\n",
            "\n",
            " (next mAP calculation at 1200 iterations) \n",
            " Last accuracy mAP@0.5 = 96.12 % \n",
            " 1151: 0.495381, 0.550726 avg loss, 0.001000 rate, 0.548735 seconds, 73664 images\n",
            "Loaded: 1.315764 seconds\n",
            "\n",
            " (next mAP calculation at 1200 iterations) \n",
            " Last accuracy mAP@0.5 = 96.12 % \n",
            " 1152: 0.443764, 0.540030 avg loss, 0.001000 rate, 0.413638 seconds, 73728 images\n",
            "Loaded: 1.430499 seconds\n",
            "\n",
            " (next mAP calculation at 1200 iterations) \n",
            " Last accuracy mAP@0.5 = 96.12 % \n",
            " 1153: 0.682339, 0.554260 avg loss, 0.001000 rate, 0.450096 seconds, 73792 images\n",
            "Loaded: 1.455830 seconds\n",
            "\n",
            " (next mAP calculation at 1200 iterations) \n",
            " Last accuracy mAP@0.5 = 96.12 % \n",
            " 1154: 0.532591, 0.552094 avg loss, 0.001000 rate, 0.476789 seconds, 73856 images\n",
            "Loaded: 1.497529 seconds\n",
            "\n",
            " (next mAP calculation at 1200 iterations) \n",
            " Last accuracy mAP@0.5 = 96.12 % \n",
            " 1155: 0.613449, 0.558229 avg loss, 0.001000 rate, 0.437110 seconds, 73920 images\n",
            "Loaded: 1.404433 seconds\n",
            "\n",
            " (next mAP calculation at 1200 iterations) \n",
            " Last accuracy mAP@0.5 = 96.12 % \n",
            " 1156: 0.448303, 0.547236 avg loss, 0.001000 rate, 0.435014 seconds, 73984 images\n",
            "Loaded: 1.416484 seconds\n",
            "\n",
            " (next mAP calculation at 1200 iterations) \n",
            " Last accuracy mAP@0.5 = 96.12 % \n",
            " 1157: 0.515812, 0.544094 avg loss, 0.001000 rate, 0.424748 seconds, 74048 images\n",
            "Loaded: 1.435571 seconds\n",
            "\n",
            " (next mAP calculation at 1200 iterations) \n",
            " Last accuracy mAP@0.5 = 96.12 % \n",
            " 1158: 0.512636, 0.540948 avg loss, 0.001000 rate, 0.469931 seconds, 74112 images\n",
            "Loaded: 1.381945 seconds\n",
            "\n",
            " (next mAP calculation at 1200 iterations) \n",
            " Last accuracy mAP@0.5 = 96.12 % \n",
            " 1159: 0.559554, 0.542809 avg loss, 0.001000 rate, 0.434389 seconds, 74176 images\n",
            "Loaded: 1.464484 seconds\n",
            "\n",
            " (next mAP calculation at 1200 iterations) \n",
            " Last accuracy mAP@0.5 = 96.12 % \n",
            " 1160: 0.479569, 0.536485 avg loss, 0.001000 rate, 0.430648 seconds, 74240 images\n",
            "Resizing\n",
            "480 x 480 \n",
            " try to allocate additional workspace_size = 4592.89 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.000040 seconds\n",
            "\n",
            " (next mAP calculation at 1200 iterations) \n",
            " Last accuracy mAP@0.5 = 96.12 % \n",
            " 1161: 0.441549, 0.526991 avg loss, 0.001000 rate, 0.628487 seconds, 74304 images\n",
            "Loaded: 1.309111 seconds\n",
            "\n",
            " (next mAP calculation at 1200 iterations) \n",
            " Last accuracy mAP@0.5 = 96.12 % \n",
            " 1162: 0.511636, 0.525456 avg loss, 0.001000 rate, 0.656908 seconds, 74368 images\n",
            "Loaded: 1.360427 seconds\n",
            "\n",
            " (next mAP calculation at 1200 iterations) \n",
            " Last accuracy mAP@0.5 = 96.12 % \n",
            " 1163: 0.625252, 0.535435 avg loss, 0.001000 rate, 0.585363 seconds, 74432 images\n",
            "Loaded: 1.476718 seconds\n",
            "\n",
            " (next mAP calculation at 1200 iterations) \n",
            " Last accuracy mAP@0.5 = 96.12 % \n",
            " 1164: 0.518788, 0.533771 avg loss, 0.001000 rate, 0.605424 seconds, 74496 images\n",
            "Loaded: 1.462633 seconds\n",
            "\n",
            " (next mAP calculation at 1200 iterations) \n",
            " Last accuracy mAP@0.5 = 96.12 % \n",
            " 1165: 0.478670, 0.528261 avg loss, 0.001000 rate, 0.661894 seconds, 74560 images\n",
            "Loaded: 1.251074 seconds\n",
            "\n",
            " (next mAP calculation at 1200 iterations) \n",
            " Last accuracy mAP@0.5 = 96.12 % \n",
            " 1166: 0.621588, 0.537593 avg loss, 0.001000 rate, 0.679381 seconds, 74624 images\n",
            "Loaded: 1.337981 seconds\n",
            "\n",
            " (next mAP calculation at 1200 iterations) \n",
            " Last accuracy mAP@0.5 = 96.12 % \n",
            " 1167: 0.461618, 0.529996 avg loss, 0.001000 rate, 0.576076 seconds, 74688 images\n",
            "Loaded: 1.433701 seconds\n",
            "\n",
            " (next mAP calculation at 1200 iterations) \n",
            " Last accuracy mAP@0.5 = 96.12 % \n",
            " 1168: 0.445014, 0.521497 avg loss, 0.001000 rate, 0.596374 seconds, 74752 images\n",
            "Loaded: 1.370597 seconds\n",
            "\n",
            " (next mAP calculation at 1200 iterations) \n",
            " Last accuracy mAP@0.5 = 96.12 % \n",
            " 1169: 0.413829, 0.510731 avg loss, 0.001000 rate, 0.567226 seconds, 74816 images\n",
            "Loaded: 1.435094 seconds\n",
            "\n",
            " (next mAP calculation at 1200 iterations) \n",
            " Last accuracy mAP@0.5 = 96.12 % \n",
            " 1170: 0.524179, 0.512075 avg loss, 0.001000 rate, 0.659744 seconds, 74880 images\n",
            "Resizing\n",
            "384 x 384 \n",
            " try to allocate additional workspace_size = 1302.33 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 1.347104 seconds\n",
            "\n",
            " (next mAP calculation at 1200 iterations) \n",
            " Last accuracy mAP@0.5 = 96.12 % \n",
            " 1171: 0.449955, 0.505863 avg loss, 0.001000 rate, 0.360431 seconds, 74944 images\n",
            "Loaded: 1.446299 seconds\n",
            "\n",
            " (next mAP calculation at 1200 iterations) \n",
            " Last accuracy mAP@0.5 = 96.12 % \n",
            " 1172: 0.405907, 0.495868 avg loss, 0.001000 rate, 0.359045 seconds, 75008 images\n",
            "Loaded: 1.530040 seconds\n",
            "\n",
            " (next mAP calculation at 1200 iterations) \n",
            " Last accuracy mAP@0.5 = 96.12 % \n",
            " 1173: 0.612713, 0.507552 avg loss, 0.001000 rate, 0.370836 seconds, 75072 images\n",
            "Loaded: 1.462272 seconds\n",
            "\n",
            " (next mAP calculation at 1200 iterations) \n",
            " Last accuracy mAP@0.5 = 96.12 % \n",
            " 1174: 0.463455, 0.503142 avg loss, 0.001000 rate, 0.397234 seconds, 75136 images\n",
            "Loaded: 1.510957 seconds\n",
            "\n",
            " (next mAP calculation at 1200 iterations) \n",
            " Last accuracy mAP@0.5 = 96.12 % \n",
            " 1175: 0.479607, 0.500789 avg loss, 0.001000 rate, 0.420562 seconds, 75200 images\n",
            "Loaded: 1.412746 seconds\n",
            "\n",
            " (next mAP calculation at 1200 iterations) \n",
            " Last accuracy mAP@0.5 = 96.12 % \n",
            " 1176: 0.554240, 0.506134 avg loss, 0.001000 rate, 0.395041 seconds, 75264 images\n",
            "Loaded: 1.505621 seconds\n",
            "\n",
            " (next mAP calculation at 1200 iterations) \n",
            " Last accuracy mAP@0.5 = 96.12 % \n",
            " 1177: 0.619236, 0.517444 avg loss, 0.001000 rate, 0.411669 seconds, 75328 images\n",
            "Loaded: 1.353831 seconds\n",
            "\n",
            " (next mAP calculation at 1200 iterations) \n",
            " Last accuracy mAP@0.5 = 96.12 % \n",
            " 1178: 0.532325, 0.518932 avg loss, 0.001000 rate, 0.346682 seconds, 75392 images\n",
            "Loaded: 1.500250 seconds\n",
            "\n",
            " (next mAP calculation at 1200 iterations) \n",
            " Last accuracy mAP@0.5 = 96.12 % \n",
            " 1179: 0.487155, 0.515754 avg loss, 0.001000 rate, 0.380518 seconds, 75456 images\n",
            "Loaded: 1.410521 seconds\n",
            "\n",
            " (next mAP calculation at 1200 iterations) \n",
            " Last accuracy mAP@0.5 = 96.12 % \n",
            " 1180: 0.509176, 0.515097 avg loss, 0.001000 rate, 0.364341 seconds, 75520 images\n",
            "Resizing\n",
            "352 x 352 \n",
            " try to allocate additional workspace_size = 1264.58 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 1.362615 seconds\n",
            "\n",
            " (next mAP calculation at 1200 iterations) \n",
            " Last accuracy mAP@0.5 = 96.12 % \n",
            " 1181: 0.734920, 0.537079 avg loss, 0.001000 rate, 0.336449 seconds, 75584 images\n",
            "Loaded: 1.528376 seconds\n",
            "\n",
            " (next mAP calculation at 1200 iterations) \n",
            " Last accuracy mAP@0.5 = 96.12 % \n",
            " 1182: 0.511718, 0.534543 avg loss, 0.001000 rate, 0.302270 seconds, 75648 images\n",
            "Loaded: 1.399219 seconds\n",
            "\n",
            " (next mAP calculation at 1200 iterations) \n",
            " Last accuracy mAP@0.5 = 96.12 % \n",
            " 1183: 0.520767, 0.533165 avg loss, 0.001000 rate, 0.305136 seconds, 75712 images\n",
            "Loaded: 1.448082 seconds\n",
            "\n",
            " (next mAP calculation at 1200 iterations) \n",
            " Last accuracy mAP@0.5 = 96.12 % \n",
            " 1184: 0.479568, 0.527806 avg loss, 0.001000 rate, 0.274959 seconds, 75776 images\n",
            "Loaded: 1.475246 seconds\n",
            "\n",
            " (next mAP calculation at 1200 iterations) \n",
            " Last accuracy mAP@0.5 = 96.12 % \n",
            " 1185: 0.473470, 0.522372 avg loss, 0.001000 rate, 0.294374 seconds, 75840 images\n",
            "Loaded: 1.454011 seconds\n",
            "\n",
            " (next mAP calculation at 1200 iterations) \n",
            " Last accuracy mAP@0.5 = 96.12 % \n",
            " 1186: 0.519307, 0.522065 avg loss, 0.001000 rate, 0.303896 seconds, 75904 images\n",
            "Loaded: 1.478243 seconds\n",
            "\n",
            " (next mAP calculation at 1200 iterations) \n",
            " Last accuracy mAP@0.5 = 96.12 % \n",
            " 1187: 0.502585, 0.520117 avg loss, 0.001000 rate, 0.322209 seconds, 75968 images\n",
            "Loaded: 1.448846 seconds\n",
            "\n",
            " (next mAP calculation at 1200 iterations) \n",
            " Last accuracy mAP@0.5 = 96.12 % \n",
            " 1188: 0.596972, 0.527803 avg loss, 0.001000 rate, 0.300611 seconds, 76032 images\n",
            "Loaded: 1.466806 seconds\n",
            "\n",
            " (next mAP calculation at 1200 iterations) \n",
            " Last accuracy mAP@0.5 = 96.12 % \n",
            " 1189: 0.529074, 0.527930 avg loss, 0.001000 rate, 0.299697 seconds, 76096 images\n",
            "Loaded: 1.447338 seconds\n",
            "\n",
            " (next mAP calculation at 1200 iterations) \n",
            " Last accuracy mAP@0.5 = 96.12 % \n",
            " 1190: 0.661580, 0.541295 avg loss, 0.001000 rate, 0.312495 seconds, 76160 images\n",
            "Resizing\n",
            "576 x 576 \n",
            " try to allocate additional workspace_size = 4605.87 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.000032 seconds\n",
            "\n",
            " (next mAP calculation at 1200 iterations) \n",
            " Last accuracy mAP@0.5 = 96.12 % \n",
            " 1191: 0.530637, 0.540229 avg loss, 0.001000 rate, 0.924668 seconds, 76224 images\n",
            "Loaded: 1.412023 seconds\n",
            "\n",
            " (next mAP calculation at 1200 iterations) \n",
            " Last accuracy mAP@0.5 = 96.12 % \n",
            " 1192: 0.574664, 0.543673 avg loss, 0.001000 rate, 0.884488 seconds, 76288 images\n",
            "Loaded: 1.392399 seconds\n",
            "\n",
            " (next mAP calculation at 1200 iterations) \n",
            " Last accuracy mAP@0.5 = 96.12 % \n",
            " 1193: 0.544616, 0.543767 avg loss, 0.001000 rate, 0.835264 seconds, 76352 images\n",
            "Loaded: 1.320897 seconds\n",
            "\n",
            " (next mAP calculation at 1200 iterations) \n",
            " Last accuracy mAP@0.5 = 96.12 % \n",
            " 1194: 0.729041, 0.562294 avg loss, 0.001000 rate, 0.845790 seconds, 76416 images\n",
            "Loaded: 1.311189 seconds\n",
            "\n",
            " (next mAP calculation at 1200 iterations) \n",
            " Last accuracy mAP@0.5 = 96.12 % \n",
            " 1195: 0.565270, 0.562592 avg loss, 0.001000 rate, 0.876200 seconds, 76480 images\n",
            "Loaded: 1.337281 seconds\n",
            "\n",
            " (next mAP calculation at 1200 iterations) \n",
            " Last accuracy mAP@0.5 = 96.12 % \n",
            " 1196: 0.466260, 0.552959 avg loss, 0.001000 rate, 0.870215 seconds, 76544 images\n",
            "Loaded: 1.348885 seconds\n",
            "\n",
            " (next mAP calculation at 1200 iterations) \n",
            " Last accuracy mAP@0.5 = 96.12 % \n",
            " 1197: 0.520549, 0.549718 avg loss, 0.001000 rate, 0.907884 seconds, 76608 images\n",
            "Loaded: 1.311021 seconds\n",
            "\n",
            " (next mAP calculation at 1200 iterations) \n",
            " Last accuracy mAP@0.5 = 96.12 % \n",
            " 1198: 0.445163, 0.539262 avg loss, 0.001000 rate, 0.979322 seconds, 76672 images\n",
            "Loaded: 1.220213 seconds\n",
            "\n",
            " (next mAP calculation at 1200 iterations) \n",
            " Last accuracy mAP@0.5 = 96.12 % \n",
            " 1199: 0.533290, 0.538665 avg loss, 0.001000 rate, 0.847721 seconds, 76736 images\n",
            "Loaded: 1.301995 seconds\n",
            "\n",
            " (next mAP calculation at 1200 iterations) \n",
            " Last accuracy mAP@0.5 = 96.12 % \n",
            " 1200: 0.438324, 0.528631 avg loss, 0.001000 rate, 0.865464 seconds, 76800 images\n",
            "Resizing to initial size: 416 x 416 \n",
            " try to allocate additional workspace_size = 1302.33 MB \n",
            " CUDA allocate done! \n",
            "\n",
            " calculation mAP (mean average precision)...\n",
            "368\n",
            " detections_count = 767, unique_truth_count = 374  \n",
            "class_id = 0, name = banana, ap = 98.05%   \t (TP = 354, FP = 17) \n",
            "\n",
            " for thresh = 0.25, precision = 0.95, recall = 0.95, F1-score = 0.95 \n",
            " for thresh = 0.25, TP = 354, FP = 17, FN = 20, average IoU = 71.38 % \n",
            "\n",
            " IoU threshold = 50 %, used Area-Under-Curve for each unique Recall \n",
            " mean average precision (mAP@0.50) = 0.980461, or 98.05 % \n",
            "Total Detection Time: 16.000000 Seconds\n",
            "\n",
            "Set -points flag:\n",
            " `-points 101` for MS COCO \n",
            " `-points 11` for PascalVOC 2007 (uncomment `difficult` in voc.data) \n",
            " `-points 0` (AUC) for ImageNet, PascalVOC 2010-2012, your custom dataset\n",
            "\n",
            " mean_average_precision (mAP@0.5) = 0.980461 \n",
            "Saving weights to /content/gdrive/My Drive/darknet/backup/yolov3-tiny-280120_last.weights\n",
            "Resizing\n",
            "576 x 576 \n",
            " try to allocate additional workspace_size = 4605.87 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.000038 seconds\n",
            "\n",
            " (next mAP calculation at 1300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.05 % \n",
            " 1201: 0.538123, 0.529580 avg loss, 0.001000 rate, 0.964271 seconds, 76864 images\n",
            "Loaded: 1.280041 seconds\n",
            "\n",
            " (next mAP calculation at 1300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.05 % \n",
            " 1202: 0.571667, 0.533789 avg loss, 0.001000 rate, 0.968228 seconds, 76928 images\n",
            "Loaded: 1.218176 seconds\n",
            "\n",
            " (next mAP calculation at 1300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.05 % \n",
            " 1203: 0.476451, 0.528055 avg loss, 0.001000 rate, 0.842877 seconds, 76992 images\n",
            "Loaded: 1.303874 seconds\n",
            "\n",
            " (next mAP calculation at 1300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.05 % \n",
            " 1204: 0.542360, 0.529485 avg loss, 0.001000 rate, 0.892446 seconds, 77056 images\n",
            "Loaded: 1.407314 seconds\n",
            "\n",
            " (next mAP calculation at 1300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.05 % \n",
            " 1205: 0.545759, 0.531113 avg loss, 0.001000 rate, 0.932557 seconds, 77120 images\n",
            "Loaded: 1.224763 seconds\n",
            "\n",
            " (next mAP calculation at 1300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.05 % \n",
            " 1206: 0.522313, 0.530233 avg loss, 0.001000 rate, 0.928693 seconds, 77184 images\n",
            "Loaded: 1.231521 seconds\n",
            "\n",
            " (next mAP calculation at 1300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.05 % \n",
            " 1207: 0.534939, 0.530703 avg loss, 0.001000 rate, 0.952236 seconds, 77248 images\n",
            "Loaded: 1.292956 seconds\n",
            "\n",
            " (next mAP calculation at 1300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.05 % \n",
            " 1208: 0.481637, 0.525797 avg loss, 0.001000 rate, 0.993248 seconds, 77312 images\n",
            "Loaded: 1.221305 seconds\n",
            "\n",
            " (next mAP calculation at 1300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.05 % \n",
            " 1209: 0.538086, 0.527026 avg loss, 0.001000 rate, 0.929052 seconds, 77376 images\n",
            "Loaded: 1.423938 seconds\n",
            "\n",
            " (next mAP calculation at 1300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.05 % \n",
            " 1210: 0.453545, 0.519678 avg loss, 0.001000 rate, 0.890770 seconds, 77440 images\n",
            "Resizing\n",
            "448 x 448 \n",
            " try to allocate additional workspace_size = 1233.65 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 1.510093 seconds\n",
            "\n",
            " (next mAP calculation at 1300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.05 % \n",
            " 1211: 0.562444, 0.523954 avg loss, 0.001000 rate, 0.540092 seconds, 77504 images\n",
            "Loaded: 1.433044 seconds\n",
            "\n",
            " (next mAP calculation at 1300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.05 % \n",
            " 1212: 0.478640, 0.519423 avg loss, 0.001000 rate, 0.509044 seconds, 77568 images\n",
            "Loaded: 1.431381 seconds\n",
            "\n",
            " (next mAP calculation at 1300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.05 % \n",
            " 1213: 0.534052, 0.520886 avg loss, 0.001000 rate, 0.553166 seconds, 77632 images\n",
            "Loaded: 1.404433 seconds\n",
            "\n",
            " (next mAP calculation at 1300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.05 % \n",
            " 1214: 0.426535, 0.511451 avg loss, 0.001000 rate, 0.493405 seconds, 77696 images\n",
            "Loaded: 1.452253 seconds\n",
            "\n",
            " (next mAP calculation at 1300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.05 % \n",
            " 1215: 0.454874, 0.505793 avg loss, 0.001000 rate, 0.494109 seconds, 77760 images\n",
            "Loaded: 1.413151 seconds\n",
            "\n",
            " (next mAP calculation at 1300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.05 % \n",
            " 1216: 0.489180, 0.504132 avg loss, 0.001000 rate, 0.545310 seconds, 77824 images\n",
            "Loaded: 1.401674 seconds\n",
            "\n",
            " (next mAP calculation at 1300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.05 % \n",
            " 1217: 0.530104, 0.506729 avg loss, 0.001000 rate, 0.473522 seconds, 77888 images\n",
            "Loaded: 1.511437 seconds\n",
            "\n",
            " (next mAP calculation at 1300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.05 % \n",
            " 1218: 0.464047, 0.502461 avg loss, 0.001000 rate, 0.492207 seconds, 77952 images\n",
            "Loaded: 1.506749 seconds\n",
            "\n",
            " (next mAP calculation at 1300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.05 % \n",
            " 1219: 0.476477, 0.499862 avg loss, 0.001000 rate, 0.534487 seconds, 78016 images\n",
            "Loaded: 1.573428 seconds\n",
            "\n",
            " (next mAP calculation at 1300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.05 % \n",
            " 1220: 0.456761, 0.495552 avg loss, 0.001000 rate, 0.472387 seconds, 78080 images\n",
            "Resizing\n",
            "480 x 480 \n",
            " try to allocate additional workspace_size = 4592.89 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.031708 seconds\n",
            "\n",
            " (next mAP calculation at 1300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.05 % \n",
            " 1221: 0.518399, 0.497837 avg loss, 0.001000 rate, 0.721216 seconds, 78144 images\n",
            "Loaded: 1.267653 seconds\n",
            "\n",
            " (next mAP calculation at 1300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.05 % \n",
            " 1222: 0.477444, 0.495798 avg loss, 0.001000 rate, 0.678871 seconds, 78208 images\n",
            "Loaded: 1.293160 seconds\n",
            "\n",
            " (next mAP calculation at 1300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.05 % \n",
            " 1223: 0.560959, 0.502314 avg loss, 0.001000 rate, 0.613313 seconds, 78272 images\n",
            "Loaded: 1.437042 seconds\n",
            "\n",
            " (next mAP calculation at 1300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.05 % \n",
            " 1224: 0.513568, 0.503439 avg loss, 0.001000 rate, 0.690114 seconds, 78336 images\n",
            "Loaded: 1.380095 seconds\n",
            "\n",
            " (next mAP calculation at 1300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.05 % \n",
            " 1225: 0.401079, 0.493203 avg loss, 0.001000 rate, 0.588889 seconds, 78400 images\n",
            "Loaded: 1.570543 seconds\n",
            "\n",
            " (next mAP calculation at 1300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.05 % \n",
            " 1226: 0.456436, 0.489526 avg loss, 0.001000 rate, 0.668656 seconds, 78464 images\n",
            "Loaded: 1.340452 seconds\n",
            "\n",
            " (next mAP calculation at 1300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.05 % \n",
            " 1227: 0.455661, 0.486140 avg loss, 0.001000 rate, 0.687987 seconds, 78528 images\n",
            "Loaded: 1.341567 seconds\n",
            "\n",
            " (next mAP calculation at 1300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.05 % \n",
            " 1228: 0.435543, 0.481080 avg loss, 0.001000 rate, 0.683085 seconds, 78592 images\n",
            "Loaded: 1.291767 seconds\n",
            "\n",
            " (next mAP calculation at 1300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.05 % \n",
            " 1229: 0.583317, 0.491304 avg loss, 0.001000 rate, 0.704348 seconds, 78656 images\n",
            "Loaded: 1.336227 seconds\n",
            "\n",
            " (next mAP calculation at 1300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.05 % \n",
            " 1230: 0.474088, 0.489582 avg loss, 0.001000 rate, 0.631164 seconds, 78720 images\n",
            "Resizing\n",
            "576 x 576 \n",
            " try to allocate additional workspace_size = 4605.87 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.000038 seconds\n",
            "\n",
            " (next mAP calculation at 1300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.05 % \n",
            " 1231: 0.442957, 0.484920 avg loss, 0.001000 rate, 0.930484 seconds, 78784 images\n",
            "Loaded: 1.292878 seconds\n",
            "\n",
            " (next mAP calculation at 1300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.05 % \n",
            " 1232: 0.631116, 0.499539 avg loss, 0.001000 rate, 0.950635 seconds, 78848 images\n",
            "Loaded: 1.261398 seconds\n",
            "\n",
            " (next mAP calculation at 1300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.05 % \n",
            " 1233: 0.409120, 0.490497 avg loss, 0.001000 rate, 0.826913 seconds, 78912 images\n",
            "Loaded: 1.387350 seconds\n",
            "\n",
            " (next mAP calculation at 1300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.05 % \n",
            " 1234: 0.545428, 0.495990 avg loss, 0.001000 rate, 0.885189 seconds, 78976 images\n",
            "Loaded: 1.391199 seconds\n",
            "\n",
            " (next mAP calculation at 1300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.05 % \n",
            " 1235: 0.496563, 0.496048 avg loss, 0.001000 rate, 0.899237 seconds, 79040 images\n",
            "Loaded: 1.237413 seconds\n",
            "\n",
            " (next mAP calculation at 1300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.05 % \n",
            " 1236: 0.465907, 0.493034 avg loss, 0.001000 rate, 0.834163 seconds, 79104 images\n",
            "Loaded: 1.410998 seconds\n",
            "\n",
            " (next mAP calculation at 1300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.05 % \n",
            " 1237: 0.458607, 0.489591 avg loss, 0.001000 rate, 0.837075 seconds, 79168 images\n",
            "Loaded: 1.370097 seconds\n",
            "\n",
            " (next mAP calculation at 1300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.05 % \n",
            " 1238: 0.511546, 0.491786 avg loss, 0.001000 rate, 0.900439 seconds, 79232 images\n",
            "Loaded: 1.265363 seconds\n",
            "\n",
            " (next mAP calculation at 1300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.05 % \n",
            " 1239: 0.478920, 0.490500 avg loss, 0.001000 rate, 1.002729 seconds, 79296 images\n",
            "Loaded: 1.191573 seconds\n",
            "\n",
            " (next mAP calculation at 1300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.05 % \n",
            " 1240: 0.494214, 0.490871 avg loss, 0.001000 rate, 0.904853 seconds, 79360 images\n",
            "Resizing\n",
            "576 x 576 \n",
            " try to allocate additional workspace_size = 4605.87 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 1.449697 seconds\n",
            "\n",
            " (next mAP calculation at 1300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.05 % \n",
            " 1241: 0.569570, 0.498741 avg loss, 0.001000 rate, 0.976978 seconds, 79424 images\n",
            "Loaded: 1.250665 seconds\n",
            "\n",
            " (next mAP calculation at 1300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.05 % \n",
            " 1242: 0.500932, 0.498960 avg loss, 0.001000 rate, 0.931260 seconds, 79488 images\n",
            "Loaded: 1.251896 seconds\n",
            "\n",
            " (next mAP calculation at 1300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.05 % \n",
            " 1243: 0.383333, 0.487397 avg loss, 0.001000 rate, 0.953991 seconds, 79552 images\n",
            "Loaded: 1.454717 seconds\n",
            "\n",
            " (next mAP calculation at 1300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.05 % \n",
            " 1244: 0.492123, 0.487870 avg loss, 0.001000 rate, 0.895869 seconds, 79616 images\n",
            "Loaded: 1.466123 seconds\n",
            "\n",
            " (next mAP calculation at 1300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.05 % \n",
            " 1245: 0.731436, 0.512227 avg loss, 0.001000 rate, 0.851470 seconds, 79680 images\n",
            "Loaded: 1.352017 seconds\n",
            "\n",
            " (next mAP calculation at 1300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.05 % \n",
            " 1246: 0.447326, 0.505737 avg loss, 0.001000 rate, 0.851408 seconds, 79744 images\n",
            "Loaded: 1.326880 seconds\n",
            "\n",
            " (next mAP calculation at 1300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.05 % \n",
            " 1247: 0.654312, 0.520594 avg loss, 0.001000 rate, 0.967906 seconds, 79808 images\n",
            "Loaded: 1.197681 seconds\n",
            "\n",
            " (next mAP calculation at 1300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.05 % \n",
            " 1248: 0.570673, 0.525602 avg loss, 0.001000 rate, 0.956138 seconds, 79872 images\n",
            "Loaded: 1.250375 seconds\n",
            "\n",
            " (next mAP calculation at 1300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.05 % \n",
            " 1249: 0.509575, 0.523999 avg loss, 0.001000 rate, 0.844355 seconds, 79936 images\n",
            "Loaded: 1.446247 seconds\n",
            "\n",
            " (next mAP calculation at 1300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.05 % \n",
            " 1250: 0.587159, 0.530315 avg loss, 0.001000 rate, 0.895298 seconds, 80000 images\n",
            "Resizing\n",
            "352 x 352 \n",
            " try to allocate additional workspace_size = 1264.58 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.000115 seconds\n",
            "\n",
            " (next mAP calculation at 1300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.05 % \n",
            " 1251: 0.471100, 0.524394 avg loss, 0.001000 rate, 0.359672 seconds, 80064 images\n",
            "Loaded: 1.410157 seconds\n",
            "\n",
            " (next mAP calculation at 1300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.05 % \n",
            " 1252: 0.470689, 0.519023 avg loss, 0.001000 rate, 0.313443 seconds, 80128 images\n",
            "Loaded: 1.420359 seconds\n",
            "\n",
            " (next mAP calculation at 1300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.05 % \n",
            " 1253: 0.473696, 0.514491 avg loss, 0.001000 rate, 0.281179 seconds, 80192 images\n",
            "Loaded: 1.464837 seconds\n",
            "\n",
            " (next mAP calculation at 1300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.05 % \n",
            " 1254: 0.447837, 0.507825 avg loss, 0.001000 rate, 0.283969 seconds, 80256 images\n",
            "Loaded: 1.494608 seconds\n",
            "\n",
            " (next mAP calculation at 1300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.05 % \n",
            " 1255: 0.433399, 0.500383 avg loss, 0.001000 rate, 0.304628 seconds, 80320 images\n",
            "Loaded: 1.425678 seconds\n",
            "\n",
            " (next mAP calculation at 1300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.05 % \n",
            " 1256: 0.412626, 0.491607 avg loss, 0.001000 rate, 0.298505 seconds, 80384 images\n",
            "Loaded: 1.443660 seconds\n",
            "\n",
            " (next mAP calculation at 1300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.05 % \n",
            " 1257: 0.672591, 0.509706 avg loss, 0.001000 rate, 0.291853 seconds, 80448 images\n",
            "Loaded: 1.445009 seconds\n",
            "\n",
            " (next mAP calculation at 1300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.05 % \n",
            " 1258: 0.583747, 0.517110 avg loss, 0.001000 rate, 0.284189 seconds, 80512 images\n",
            "Loaded: 1.440559 seconds\n",
            "\n",
            " (next mAP calculation at 1300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.05 % \n",
            " 1259: 0.468241, 0.512223 avg loss, 0.001000 rate, 0.287457 seconds, 80576 images\n",
            "Loaded: 1.429964 seconds\n",
            "\n",
            " (next mAP calculation at 1300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.05 % \n",
            " 1260: 0.517018, 0.512702 avg loss, 0.001000 rate, 0.291047 seconds, 80640 images\n",
            "Resizing\n",
            "576 x 576 \n",
            " try to allocate additional workspace_size = 4605.87 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.000036 seconds\n",
            "\n",
            " (next mAP calculation at 1300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.05 % \n",
            " 1261: 0.514374, 0.512870 avg loss, 0.001000 rate, 0.939566 seconds, 80704 images\n",
            "Loaded: 1.293856 seconds\n",
            "\n",
            " (next mAP calculation at 1300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.05 % \n",
            " 1262: 0.461402, 0.507723 avg loss, 0.001000 rate, 0.974095 seconds, 80768 images\n",
            "Loaded: 1.236772 seconds\n",
            "\n",
            " (next mAP calculation at 1300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.05 % \n",
            " 1263: 0.447909, 0.501741 avg loss, 0.001000 rate, 0.931784 seconds, 80832 images\n",
            "Loaded: 1.335502 seconds\n",
            "\n",
            " (next mAP calculation at 1300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.05 % \n",
            " 1264: 0.654394, 0.517007 avg loss, 0.001000 rate, 0.872605 seconds, 80896 images\n",
            "Loaded: 1.376964 seconds\n",
            "\n",
            " (next mAP calculation at 1300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.05 % \n",
            " 1265: 0.559831, 0.521289 avg loss, 0.001000 rate, 0.890204 seconds, 80960 images\n",
            "Loaded: 1.328609 seconds\n",
            "\n",
            " (next mAP calculation at 1300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.05 % \n",
            " 1266: 0.544274, 0.523588 avg loss, 0.001000 rate, 0.881292 seconds, 81024 images\n",
            "Loaded: 1.300458 seconds\n",
            "\n",
            " (next mAP calculation at 1300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.05 % \n",
            " 1267: 0.527214, 0.523950 avg loss, 0.001000 rate, 0.923156 seconds, 81088 images\n",
            "Loaded: 1.274106 seconds\n",
            "\n",
            " (next mAP calculation at 1300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.05 % \n",
            " 1268: 0.395332, 0.511088 avg loss, 0.001000 rate, 0.867840 seconds, 81152 images\n",
            "Loaded: 1.358676 seconds\n",
            "\n",
            " (next mAP calculation at 1300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.05 % \n",
            " 1269: 0.472933, 0.507273 avg loss, 0.001000 rate, 0.974062 seconds, 81216 images\n",
            "Loaded: 1.362153 seconds\n",
            "\n",
            " (next mAP calculation at 1300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.05 % \n",
            " 1270: 0.606501, 0.517196 avg loss, 0.001000 rate, 0.843729 seconds, 81280 images\n",
            "Resizing\n",
            "544 x 544 \n",
            " try to allocate additional workspace_size = 4601.28 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 1.394854 seconds\n",
            "\n",
            " (next mAP calculation at 1300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.05 % \n",
            " 1271: 0.618116, 0.527288 avg loss, 0.001000 rate, 0.929537 seconds, 81344 images\n",
            "Loaded: 1.207687 seconds\n",
            "\n",
            " (next mAP calculation at 1300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.05 % \n",
            " 1272: 0.568137, 0.531373 avg loss, 0.001000 rate, 0.765271 seconds, 81408 images\n",
            "Loaded: 1.337369 seconds\n",
            "\n",
            " (next mAP calculation at 1300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.05 % \n",
            " 1273: 0.456830, 0.523918 avg loss, 0.001000 rate, 0.786005 seconds, 81472 images\n",
            "Loaded: 1.320335 seconds\n",
            "\n",
            " (next mAP calculation at 1300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.05 % \n",
            " 1274: 0.429428, 0.514469 avg loss, 0.001000 rate, 0.791064 seconds, 81536 images\n",
            "Loaded: 1.473270 seconds\n",
            "\n",
            " (next mAP calculation at 1300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.05 % \n",
            " 1275: 0.465733, 0.509596 avg loss, 0.001000 rate, 0.837809 seconds, 81600 images\n",
            "Loaded: 1.289539 seconds\n",
            "\n",
            " (next mAP calculation at 1300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.05 % \n",
            " 1276: 0.547447, 0.513381 avg loss, 0.001000 rate, 0.810393 seconds, 81664 images\n",
            "Loaded: 1.276651 seconds\n",
            "\n",
            " (next mAP calculation at 1300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.05 % \n",
            " 1277: 0.688959, 0.530939 avg loss, 0.001000 rate, 0.876298 seconds, 81728 images\n",
            "Loaded: 1.247055 seconds\n",
            "\n",
            " (next mAP calculation at 1300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.05 % \n",
            " 1278: 0.445011, 0.522346 avg loss, 0.001000 rate, 0.878081 seconds, 81792 images\n",
            "Loaded: 1.216537 seconds\n",
            "\n",
            " (next mAP calculation at 1300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.05 % \n",
            " 1279: 0.561763, 0.526288 avg loss, 0.001000 rate, 0.773850 seconds, 81856 images\n",
            "Loaded: 1.380365 seconds\n",
            "\n",
            " (next mAP calculation at 1300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.05 % \n",
            " 1280: 0.493885, 0.523047 avg loss, 0.001000 rate, 0.853916 seconds, 81920 images\n",
            "Resizing\n",
            "416 x 416 \n",
            " try to allocate additional workspace_size = 1302.33 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 1.432210 seconds\n",
            "\n",
            " (next mAP calculation at 1300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.05 % \n",
            " 1281: 0.417222, 0.512465 avg loss, 0.001000 rate, 0.428262 seconds, 81984 images\n",
            "Loaded: 1.494310 seconds\n",
            "\n",
            " (next mAP calculation at 1300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.05 % \n",
            " 1282: 0.461870, 0.507405 avg loss, 0.001000 rate, 0.455534 seconds, 82048 images\n",
            "Loaded: 1.391937 seconds\n",
            "\n",
            " (next mAP calculation at 1300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.05 % \n",
            " 1283: 0.496009, 0.506266 avg loss, 0.001000 rate, 0.406582 seconds, 82112 images\n",
            "Loaded: 1.477098 seconds\n",
            "\n",
            " (next mAP calculation at 1300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.05 % \n",
            " 1284: 0.696678, 0.525307 avg loss, 0.001000 rate, 0.473811 seconds, 82176 images\n",
            "Loaded: 1.413123 seconds\n",
            "\n",
            " (next mAP calculation at 1300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.05 % \n",
            " 1285: 0.445681, 0.517344 avg loss, 0.001000 rate, 0.491738 seconds, 82240 images\n",
            "Loaded: 1.492978 seconds\n",
            "\n",
            " (next mAP calculation at 1300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.05 % \n",
            " 1286: 0.565250, 0.522135 avg loss, 0.001000 rate, 0.456116 seconds, 82304 images\n",
            "Loaded: 1.406027 seconds\n",
            "\n",
            " (next mAP calculation at 1300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.05 % \n",
            " 1287: 0.462682, 0.516190 avg loss, 0.001000 rate, 0.446358 seconds, 82368 images\n",
            "Loaded: 1.401466 seconds\n",
            "\n",
            " (next mAP calculation at 1300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.05 % \n",
            " 1288: 0.509259, 0.515496 avg loss, 0.001000 rate, 0.426954 seconds, 82432 images\n",
            "Loaded: 1.497971 seconds\n",
            "\n",
            " (next mAP calculation at 1300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.05 % \n",
            " 1289: 0.502422, 0.514189 avg loss, 0.001000 rate, 0.400965 seconds, 82496 images\n",
            "Loaded: 1.465706 seconds\n",
            "\n",
            " (next mAP calculation at 1300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.05 % \n",
            " 1290: 0.470506, 0.509821 avg loss, 0.001000 rate, 0.460146 seconds, 82560 images\n",
            "Resizing\n",
            "448 x 448 \n",
            " try to allocate additional workspace_size = 1233.65 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.000035 seconds\n",
            "\n",
            " (next mAP calculation at 1300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.05 % \n",
            " 1291: 0.430311, 0.501870 avg loss, 0.001000 rate, 0.523420 seconds, 82624 images\n",
            "Loaded: 1.423038 seconds\n",
            "\n",
            " (next mAP calculation at 1300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.05 % \n",
            " 1292: 0.560621, 0.507745 avg loss, 0.001000 rate, 0.512536 seconds, 82688 images\n",
            "Loaded: 1.469799 seconds\n",
            "\n",
            " (next mAP calculation at 1300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.05 % \n",
            " 1293: 0.435390, 0.500509 avg loss, 0.001000 rate, 0.493916 seconds, 82752 images\n",
            "Loaded: 1.430344 seconds\n",
            "\n",
            " (next mAP calculation at 1300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.05 % \n",
            " 1294: 0.414318, 0.491890 avg loss, 0.001000 rate, 0.493093 seconds, 82816 images\n",
            "Loaded: 1.457277 seconds\n",
            "\n",
            " (next mAP calculation at 1300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.05 % \n",
            " 1295: 0.603897, 0.503091 avg loss, 0.001000 rate, 0.524502 seconds, 82880 images\n",
            "Loaded: 1.538488 seconds\n",
            "\n",
            " (next mAP calculation at 1300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.05 % \n",
            " 1296: 0.446406, 0.497422 avg loss, 0.001000 rate, 0.468597 seconds, 82944 images\n",
            "Loaded: 1.643444 seconds\n",
            "\n",
            " (next mAP calculation at 1300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.05 % \n",
            " 1297: 0.437775, 0.491458 avg loss, 0.001000 rate, 0.469019 seconds, 83008 images\n",
            "Loaded: 1.395432 seconds\n",
            "\n",
            " (next mAP calculation at 1300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.05 % \n",
            " 1298: 0.507825, 0.493094 avg loss, 0.001000 rate, 0.544872 seconds, 83072 images\n",
            "Loaded: 1.442046 seconds\n",
            "\n",
            " (next mAP calculation at 1300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.05 % \n",
            " 1299: 0.410018, 0.484787 avg loss, 0.001000 rate, 0.472371 seconds, 83136 images\n",
            "Loaded: 1.473812 seconds\n",
            "\n",
            " (next mAP calculation at 1300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.05 % \n",
            " 1300: 0.521041, 0.488412 avg loss, 0.001000 rate, 0.500769 seconds, 83200 images\n",
            "Resizing to initial size: 416 x 416 \n",
            " try to allocate additional workspace_size = 1302.33 MB \n",
            " CUDA allocate done! \n",
            "\n",
            " calculation mAP (mean average precision)...\n",
            "368\n",
            " detections_count = 783, unique_truth_count = 374  \n",
            "class_id = 0, name = banana, ap = 98.08%   \t (TP = 361, FP = 21) \n",
            "\n",
            " for thresh = 0.25, precision = 0.95, recall = 0.97, F1-score = 0.96 \n",
            " for thresh = 0.25, TP = 361, FP = 21, FN = 13, average IoU = 70.25 % \n",
            "\n",
            " IoU threshold = 50 %, used Area-Under-Curve for each unique Recall \n",
            " mean average precision (mAP@0.50) = 0.980751, or 98.08 % \n",
            "Total Detection Time: 16.000000 Seconds\n",
            "\n",
            "Set -points flag:\n",
            " `-points 101` for MS COCO \n",
            " `-points 11` for PascalVOC 2007 (uncomment `difficult` in voc.data) \n",
            " `-points 0` (AUC) for ImageNet, PascalVOC 2010-2012, your custom dataset\n",
            "\n",
            " mean_average_precision (mAP@0.5) = 0.980751 \n",
            "Saving weights to /content/gdrive/My Drive/darknet/backup/yolov3-tiny-280120_last.weights\n",
            "Resizing\n",
            "384 x 384 \n",
            " try to allocate additional workspace_size = 1302.33 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.000035 seconds\n",
            "\n",
            " (next mAP calculation at 1400 iterations) \n",
            " Last accuracy mAP@0.5 = 98.08 % \n",
            " 1301: 0.446805, 0.484252 avg loss, 0.001000 rate, 0.410815 seconds, 83264 images\n",
            "Loaded: 1.444429 seconds\n",
            "\n",
            " (next mAP calculation at 1400 iterations) \n",
            " Last accuracy mAP@0.5 = 98.08 % \n",
            " 1302: 0.433168, 0.479143 avg loss, 0.001000 rate, 0.364445 seconds, 83328 images\n",
            "Loaded: 2.044037 seconds\n",
            "\n",
            " (next mAP calculation at 1400 iterations) \n",
            " Last accuracy mAP@0.5 = 98.08 % \n",
            " 1303: 0.712130, 0.502442 avg loss, 0.001000 rate, 0.441949 seconds, 83392 images\n",
            "Loaded: 1.458305 seconds\n",
            "\n",
            " (next mAP calculation at 1400 iterations) \n",
            " Last accuracy mAP@0.5 = 98.08 % \n",
            " 1304: 0.417324, 0.493930 avg loss, 0.001000 rate, 0.389081 seconds, 83456 images\n",
            "Loaded: 1.481548 seconds\n",
            "\n",
            " (next mAP calculation at 1400 iterations) \n",
            " Last accuracy mAP@0.5 = 98.08 % \n",
            " 1305: 0.458160, 0.490353 avg loss, 0.001000 rate, 0.336015 seconds, 83520 images\n",
            "Loaded: 1.441853 seconds\n",
            "\n",
            " (next mAP calculation at 1400 iterations) \n",
            " Last accuracy mAP@0.5 = 98.08 % \n",
            " 1306: 0.486427, 0.489960 avg loss, 0.001000 rate, 0.364778 seconds, 83584 images\n",
            "Loaded: 1.417679 seconds\n",
            "\n",
            " (next mAP calculation at 1400 iterations) \n",
            " Last accuracy mAP@0.5 = 98.08 % \n",
            " 1307: 0.410159, 0.481980 avg loss, 0.001000 rate, 0.380014 seconds, 83648 images\n",
            "Loaded: 1.409186 seconds\n",
            "\n",
            " (next mAP calculation at 1400 iterations) \n",
            " Last accuracy mAP@0.5 = 98.08 % \n",
            " 1308: 0.427053, 0.476488 avg loss, 0.001000 rate, 0.351089 seconds, 83712 images\n",
            "Loaded: 1.426366 seconds\n",
            "\n",
            " (next mAP calculation at 1400 iterations) \n",
            " Last accuracy mAP@0.5 = 98.08 % \n",
            " 1309: 0.808233, 0.509662 avg loss, 0.001000 rate, 0.387659 seconds, 83776 images\n",
            "Loaded: 1.457711 seconds\n",
            "\n",
            " (next mAP calculation at 1400 iterations) \n",
            " Last accuracy mAP@0.5 = 98.08 % \n",
            " 1310: 0.465631, 0.505259 avg loss, 0.001000 rate, 0.382916 seconds, 83840 images\n",
            "Resizing\n",
            "320 x 320 \n",
            " try to allocate additional workspace_size = 868.75 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 1.483859 seconds\n",
            "\n",
            " (next mAP calculation at 1400 iterations) \n",
            " Last accuracy mAP@0.5 = 98.08 % \n",
            " 1311: 0.488996, 0.503633 avg loss, 0.001000 rate, 0.339494 seconds, 83904 images\n",
            "Loaded: 1.398322 seconds\n",
            "\n",
            " (next mAP calculation at 1400 iterations) \n",
            " Last accuracy mAP@0.5 = 98.08 % \n",
            " 1312: 0.466129, 0.499882 avg loss, 0.001000 rate, 0.251971 seconds, 83968 images\n",
            "Loaded: 1.442479 seconds\n",
            "\n",
            " (next mAP calculation at 1400 iterations) \n",
            " Last accuracy mAP@0.5 = 98.08 % \n",
            " 1313: 0.518101, 0.501704 avg loss, 0.001000 rate, 0.255274 seconds, 84032 images\n",
            "Loaded: 1.429271 seconds\n",
            "\n",
            " (next mAP calculation at 1400 iterations) \n",
            " Last accuracy mAP@0.5 = 98.08 % \n",
            " 1314: 0.533062, 0.504840 avg loss, 0.001000 rate, 0.255121 seconds, 84096 images\n",
            "Loaded: 1.562046 seconds\n",
            "\n",
            " (next mAP calculation at 1400 iterations) \n",
            " Last accuracy mAP@0.5 = 98.08 % \n",
            " 1315: 0.461237, 0.500480 avg loss, 0.001000 rate, 0.270819 seconds, 84160 images\n",
            "Loaded: 1.519111 seconds\n",
            "\n",
            " (next mAP calculation at 1400 iterations) \n",
            " Last accuracy mAP@0.5 = 98.08 % \n",
            " 1316: 0.482644, 0.498696 avg loss, 0.001000 rate, 0.299050 seconds, 84224 images\n",
            "Loaded: 1.499978 seconds\n",
            "\n",
            " (next mAP calculation at 1400 iterations) \n",
            " Last accuracy mAP@0.5 = 98.08 % \n",
            " 1317: 0.459708, 0.494797 avg loss, 0.001000 rate, 0.238643 seconds, 84288 images\n",
            "Loaded: 1.673791 seconds\n",
            "\n",
            " (next mAP calculation at 1400 iterations) \n",
            " Last accuracy mAP@0.5 = 98.08 % \n",
            " 1318: 0.513007, 0.496618 avg loss, 0.001000 rate, 0.264730 seconds, 84352 images\n",
            "Loaded: 1.412156 seconds\n",
            "\n",
            " (next mAP calculation at 1400 iterations) \n",
            " Last accuracy mAP@0.5 = 98.08 % \n",
            " 1319: 0.429919, 0.489948 avg loss, 0.001000 rate, 0.250241 seconds, 84416 images\n",
            "Loaded: 1.479723 seconds\n",
            "\n",
            " (next mAP calculation at 1400 iterations) \n",
            " Last accuracy mAP@0.5 = 98.08 % \n",
            " 1320: 0.479898, 0.488943 avg loss, 0.001000 rate, 0.238149 seconds, 84480 images\n",
            "Resizing\n",
            "352 x 352 \n",
            " try to allocate additional workspace_size = 1264.58 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.232091 seconds\n",
            "\n",
            " (next mAP calculation at 1400 iterations) \n",
            " Last accuracy mAP@0.5 = 98.08 % \n",
            " 1321: 0.449304, 0.484979 avg loss, 0.001000 rate, 0.323376 seconds, 84544 images\n",
            "Loaded: 1.377179 seconds\n",
            "\n",
            " (next mAP calculation at 1400 iterations) \n",
            " Last accuracy mAP@0.5 = 98.08 % \n",
            " 1322: 0.469946, 0.483476 avg loss, 0.001000 rate, 0.291818 seconds, 84608 images\n",
            "Loaded: 1.454261 seconds\n",
            "\n",
            " (next mAP calculation at 1400 iterations) \n",
            " Last accuracy mAP@0.5 = 98.08 % \n",
            " 1323: 0.418692, 0.476997 avg loss, 0.001000 rate, 0.302927 seconds, 84672 images\n",
            "Loaded: 1.573180 seconds\n",
            "\n",
            " (next mAP calculation at 1400 iterations) \n",
            " Last accuracy mAP@0.5 = 98.08 % \n",
            " 1324: 0.497096, 0.479007 avg loss, 0.001000 rate, 0.284603 seconds, 84736 images\n",
            "Loaded: 1.397184 seconds\n",
            "\n",
            " (next mAP calculation at 1400 iterations) \n",
            " Last accuracy mAP@0.5 = 98.08 % \n",
            " 1325: 0.802010, 0.511308 avg loss, 0.001000 rate, 0.298729 seconds, 84800 images\n",
            "Loaded: 1.468278 seconds\n",
            "\n",
            " (next mAP calculation at 1400 iterations) \n",
            " Last accuracy mAP@0.5 = 98.08 % \n",
            " 1326: 0.401180, 0.500295 avg loss, 0.001000 rate, 0.300233 seconds, 84864 images\n",
            "Loaded: 1.467475 seconds\n",
            "\n",
            " (next mAP calculation at 1400 iterations) \n",
            " Last accuracy mAP@0.5 = 98.08 % \n",
            " 1327: 0.500605, 0.500326 avg loss, 0.001000 rate, 0.287640 seconds, 84928 images\n",
            "Loaded: 1.504323 seconds\n",
            "\n",
            " (next mAP calculation at 1400 iterations) \n",
            " Last accuracy mAP@0.5 = 98.08 % \n",
            " 1328: 0.528939, 0.503187 avg loss, 0.001000 rate, 0.328827 seconds, 84992 images\n",
            "Loaded: 1.439613 seconds\n",
            "\n",
            " (next mAP calculation at 1400 iterations) \n",
            " Last accuracy mAP@0.5 = 98.08 % \n",
            " 1329: 0.353109, 0.488179 avg loss, 0.001000 rate, 0.298918 seconds, 85056 images\n",
            "Loaded: 1.534798 seconds\n",
            "\n",
            " (next mAP calculation at 1400 iterations) \n",
            " Last accuracy mAP@0.5 = 98.08 % \n",
            " 1330: 0.428147, 0.482176 avg loss, 0.001000 rate, 0.274076 seconds, 85120 images\n",
            "Resizing\n",
            "512 x 512 \n",
            " try to allocate additional workspace_size = 4596.96 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.000038 seconds\n",
            "\n",
            " (next mAP calculation at 1400 iterations) \n",
            " Last accuracy mAP@0.5 = 98.08 % \n",
            " 1331: 0.385532, 0.472512 avg loss, 0.001000 rate, 0.792151 seconds, 85184 images\n",
            "Loaded: 1.329334 seconds\n",
            "\n",
            " (next mAP calculation at 1400 iterations) \n",
            " Last accuracy mAP@0.5 = 98.08 % \n",
            " 1332: 0.410233, 0.466284 avg loss, 0.001000 rate, 0.729643 seconds, 85248 images\n",
            "Loaded: 1.294109 seconds\n",
            "\n",
            " (next mAP calculation at 1400 iterations) \n",
            " Last accuracy mAP@0.5 = 98.08 % \n",
            " 1333: 0.566580, 0.476313 avg loss, 0.001000 rate, 0.699937 seconds, 85312 images\n",
            "Loaded: 1.476756 seconds\n",
            "\n",
            " (next mAP calculation at 1400 iterations) \n",
            " Last accuracy mAP@0.5 = 98.08 % \n",
            " 1334: 0.437266, 0.472409 avg loss, 0.001000 rate, 0.733016 seconds, 85376 images\n",
            "Loaded: 1.405614 seconds\n",
            "\n",
            " (next mAP calculation at 1400 iterations) \n",
            " Last accuracy mAP@0.5 = 98.08 % \n",
            " 1335: 0.351440, 0.460312 avg loss, 0.001000 rate, 0.697364 seconds, 85440 images\n",
            "Loaded: 1.368350 seconds\n",
            "\n",
            " (next mAP calculation at 1400 iterations) \n",
            " Last accuracy mAP@0.5 = 98.08 % \n",
            " 1336: 0.377907, 0.452071 avg loss, 0.001000 rate, 0.678406 seconds, 85504 images\n",
            "Loaded: 1.341450 seconds\n",
            "\n",
            " (next mAP calculation at 1400 iterations) \n",
            " Last accuracy mAP@0.5 = 98.08 % \n",
            " 1337: 0.383020, 0.445166 avg loss, 0.001000 rate, 0.675898 seconds, 85568 images\n",
            "Loaded: 1.360238 seconds\n",
            "\n",
            " (next mAP calculation at 1400 iterations) \n",
            " Last accuracy mAP@0.5 = 98.08 % \n",
            " 1338: 0.474706, 0.448120 avg loss, 0.001000 rate, 0.749568 seconds, 85632 images\n",
            "Loaded: 1.399240 seconds\n",
            "\n",
            " (next mAP calculation at 1400 iterations) \n",
            " Last accuracy mAP@0.5 = 98.08 % \n",
            " 1339: 0.397107, 0.443019 avg loss, 0.001000 rate, 0.653457 seconds, 85696 images\n",
            "Loaded: 1.413288 seconds\n",
            "\n",
            " (next mAP calculation at 1400 iterations) \n",
            " Last accuracy mAP@0.5 = 98.08 % \n",
            " 1340: 0.505228, 0.449240 avg loss, 0.001000 rate, 0.657662 seconds, 85760 images\n",
            "Resizing\n",
            "576 x 576 \n",
            " try to allocate additional workspace_size = 4605.87 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.000036 seconds\n",
            "\n",
            " (next mAP calculation at 1400 iterations) \n",
            " Last accuracy mAP@0.5 = 98.08 % \n",
            " 1341: 0.525571, 0.456873 avg loss, 0.001000 rate, 0.970747 seconds, 85824 images\n",
            "Loaded: 1.349565 seconds\n",
            "\n",
            " (next mAP calculation at 1400 iterations) \n",
            " Last accuracy mAP@0.5 = 98.08 % \n",
            " 1342: 0.391067, 0.450292 avg loss, 0.001000 rate, 0.865033 seconds, 85888 images\n",
            "Loaded: 1.602316 seconds\n",
            "\n",
            " (next mAP calculation at 1400 iterations) \n",
            " Last accuracy mAP@0.5 = 98.08 % \n",
            " 1343: 0.466288, 0.451892 avg loss, 0.001000 rate, 0.970047 seconds, 85952 images\n",
            "Loaded: 1.238765 seconds\n",
            "\n",
            " (next mAP calculation at 1400 iterations) \n",
            " Last accuracy mAP@0.5 = 98.08 % \n",
            " 1344: 0.456243, 0.452327 avg loss, 0.001000 rate, 1.017311 seconds, 86016 images\n",
            "Loaded: 1.249089 seconds\n",
            "\n",
            " (next mAP calculation at 1400 iterations) \n",
            " Last accuracy mAP@0.5 = 98.08 % \n",
            " 1345: 0.521602, 0.459254 avg loss, 0.001000 rate, 0.899267 seconds, 86080 images\n",
            "Loaded: 1.981087 seconds\n",
            "\n",
            " (next mAP calculation at 1400 iterations) \n",
            " Last accuracy mAP@0.5 = 98.08 % \n",
            " 1346: 0.464462, 0.459775 avg loss, 0.001000 rate, 1.068657 seconds, 86144 images\n",
            "Loaded: 1.338436 seconds\n",
            "\n",
            " (next mAP calculation at 1400 iterations) \n",
            " Last accuracy mAP@0.5 = 98.08 % \n",
            " 1347: 0.474219, 0.461220 avg loss, 0.001000 rate, 0.996911 seconds, 86208 images\n",
            "Loaded: 1.439217 seconds\n",
            "\n",
            " (next mAP calculation at 1400 iterations) \n",
            " Last accuracy mAP@0.5 = 98.08 % \n",
            " 1348: 0.412523, 0.456350 avg loss, 0.001000 rate, 0.971932 seconds, 86272 images\n",
            "Loaded: 1.296063 seconds\n",
            "\n",
            " (next mAP calculation at 1400 iterations) \n",
            " Last accuracy mAP@0.5 = 98.08 % \n",
            " 1349: 0.497227, 0.460438 avg loss, 0.001000 rate, 0.987641 seconds, 86336 images\n",
            "Loaded: 1.261283 seconds\n",
            "\n",
            " (next mAP calculation at 1400 iterations) \n",
            " Last accuracy mAP@0.5 = 98.08 % \n",
            " 1350: 0.470457, 0.461440 avg loss, 0.001000 rate, 0.870060 seconds, 86400 images\n",
            "Resizing\n",
            "416 x 416 \n",
            " try to allocate additional workspace_size = 1302.33 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 1.496050 seconds\n",
            "\n",
            " (next mAP calculation at 1400 iterations) \n",
            " Last accuracy mAP@0.5 = 98.08 % \n",
            " 1351: 0.535790, 0.468875 avg loss, 0.001000 rate, 0.504076 seconds, 86464 images\n",
            "Loaded: 1.395431 seconds\n",
            "\n",
            " (next mAP calculation at 1400 iterations) \n",
            " Last accuracy mAP@0.5 = 98.08 % \n",
            " 1352: 0.404540, 0.462441 avg loss, 0.001000 rate, 0.424649 seconds, 86528 images\n",
            "Loaded: 1.509664 seconds\n",
            "\n",
            " (next mAP calculation at 1400 iterations) \n",
            " Last accuracy mAP@0.5 = 98.08 % \n",
            " 1353: 0.386313, 0.454828 avg loss, 0.001000 rate, 0.448835 seconds, 86592 images\n",
            "Loaded: 1.453219 seconds\n",
            "\n",
            " (next mAP calculation at 1400 iterations) \n",
            " Last accuracy mAP@0.5 = 98.08 % \n",
            " 1354: 0.440926, 0.453438 avg loss, 0.001000 rate, 0.438538 seconds, 86656 images\n",
            "Loaded: 1.483708 seconds\n",
            "\n",
            " (next mAP calculation at 1400 iterations) \n",
            " Last accuracy mAP@0.5 = 98.08 % \n",
            " 1355: 0.545470, 0.462641 avg loss, 0.001000 rate, 0.426569 seconds, 86720 images\n",
            "Loaded: 1.475841 seconds\n",
            "\n",
            " (next mAP calculation at 1400 iterations) \n",
            " Last accuracy mAP@0.5 = 98.08 % \n",
            " 1356: 0.457877, 0.462165 avg loss, 0.001000 rate, 0.453097 seconds, 86784 images\n",
            "Loaded: 1.392340 seconds\n",
            "\n",
            " (next mAP calculation at 1400 iterations) \n",
            " Last accuracy mAP@0.5 = 98.08 % \n",
            " 1357: 0.325356, 0.448484 avg loss, 0.001000 rate, 0.434499 seconds, 86848 images\n",
            "Loaded: 1.504005 seconds\n",
            "\n",
            " (next mAP calculation at 1400 iterations) \n",
            " Last accuracy mAP@0.5 = 98.08 % \n",
            " 1358: 0.398213, 0.443457 avg loss, 0.001000 rate, 0.426765 seconds, 86912 images\n",
            "Loaded: 1.498772 seconds\n",
            "\n",
            " (next mAP calculation at 1400 iterations) \n",
            " Last accuracy mAP@0.5 = 98.08 % \n",
            " 1359: 0.391412, 0.438252 avg loss, 0.001000 rate, 0.412378 seconds, 86976 images\n",
            "Loaded: 1.454655 seconds\n",
            "\n",
            " (next mAP calculation at 1400 iterations) \n",
            " Last accuracy mAP@0.5 = 98.08 % \n",
            " 1360: 0.497448, 0.444172 avg loss, 0.001000 rate, 0.450012 seconds, 87040 images\n",
            "Resizing\n",
            "512 x 512 \n",
            " try to allocate additional workspace_size = 4596.96 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.000038 seconds\n",
            "\n",
            " (next mAP calculation at 1400 iterations) \n",
            " Last accuracy mAP@0.5 = 98.08 % \n",
            " 1361: 0.410721, 0.440827 avg loss, 0.001000 rate, 0.664843 seconds, 87104 images\n",
            "Loaded: 1.396019 seconds\n",
            "\n",
            " (next mAP calculation at 1400 iterations) \n",
            " Last accuracy mAP@0.5 = 98.08 % \n",
            " 1362: 0.436072, 0.440351 avg loss, 0.001000 rate, 0.649912 seconds, 87168 images\n",
            "Loaded: 1.351682 seconds\n",
            "\n",
            " (next mAP calculation at 1400 iterations) \n",
            " Last accuracy mAP@0.5 = 98.08 % \n",
            " 1363: 0.554650, 0.451781 avg loss, 0.001000 rate, 0.631700 seconds, 87232 images\n",
            "Loaded: 1.475313 seconds\n",
            "\n",
            " (next mAP calculation at 1400 iterations) \n",
            " Last accuracy mAP@0.5 = 98.08 % \n",
            " 1364: 0.494884, 0.456091 avg loss, 0.001000 rate, 0.695678 seconds, 87296 images\n",
            "Loaded: 1.436507 seconds\n",
            "\n",
            " (next mAP calculation at 1400 iterations) \n",
            " Last accuracy mAP@0.5 = 98.08 % \n",
            " 1365: 0.480034, 0.458486 avg loss, 0.001000 rate, 0.678798 seconds, 87360 images\n",
            "Loaded: 1.377230 seconds\n",
            "\n",
            " (next mAP calculation at 1400 iterations) \n",
            " Last accuracy mAP@0.5 = 98.08 % \n",
            " 1366: 0.490811, 0.461718 avg loss, 0.001000 rate, 0.760385 seconds, 87424 images\n",
            "Loaded: 1.419266 seconds\n",
            "\n",
            " (next mAP calculation at 1400 iterations) \n",
            " Last accuracy mAP@0.5 = 98.08 % \n",
            " 1367: 0.409907, 0.456537 avg loss, 0.001000 rate, 0.659081 seconds, 87488 images\n",
            "Loaded: 1.442610 seconds\n",
            "\n",
            " (next mAP calculation at 1400 iterations) \n",
            " Last accuracy mAP@0.5 = 98.08 % \n",
            " 1368: 0.382117, 0.449095 avg loss, 0.001000 rate, 0.662196 seconds, 87552 images\n",
            "Loaded: 1.498463 seconds\n",
            "\n",
            " (next mAP calculation at 1400 iterations) \n",
            " Last accuracy mAP@0.5 = 98.08 % \n",
            " 1369: 0.533553, 0.457541 avg loss, 0.001000 rate, 0.622860 seconds, 87616 images\n",
            "Loaded: 1.441752 seconds\n",
            "\n",
            " (next mAP calculation at 1400 iterations) \n",
            " Last accuracy mAP@0.5 = 98.08 % \n",
            " 1370: 0.431573, 0.454944 avg loss, 0.001000 rate, 0.751060 seconds, 87680 images\n",
            "Resizing\n",
            "352 x 352 \n",
            " try to allocate additional workspace_size = 1264.58 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.070526 seconds\n",
            "\n",
            " (next mAP calculation at 1400 iterations) \n",
            " Last accuracy mAP@0.5 = 98.08 % \n",
            " 1371: 0.458478, 0.455297 avg loss, 0.001000 rate, 0.387869 seconds, 87744 images\n",
            "Loaded: 1.330899 seconds\n",
            "\n",
            " (next mAP calculation at 1400 iterations) \n",
            " Last accuracy mAP@0.5 = 98.08 % \n",
            " 1372: 0.396621, 0.449430 avg loss, 0.001000 rate, 0.326821 seconds, 87808 images\n",
            "Loaded: 1.438875 seconds\n",
            "\n",
            " (next mAP calculation at 1400 iterations) \n",
            " Last accuracy mAP@0.5 = 98.08 % \n",
            " 1373: 0.379443, 0.442431 avg loss, 0.001000 rate, 0.323370 seconds, 87872 images\n",
            "Loaded: 1.721851 seconds\n",
            "\n",
            " (next mAP calculation at 1400 iterations) \n",
            " Last accuracy mAP@0.5 = 98.08 % \n",
            " 1374: 0.475085, 0.445697 avg loss, 0.001000 rate, 0.291843 seconds, 87936 images\n",
            "Loaded: 1.495733 seconds\n",
            "\n",
            " (next mAP calculation at 1400 iterations) \n",
            " Last accuracy mAP@0.5 = 98.08 % \n",
            " 1375: 0.461820, 0.447309 avg loss, 0.001000 rate, 0.284799 seconds, 88000 images\n",
            "Loaded: 1.498212 seconds\n",
            "\n",
            " (next mAP calculation at 1400 iterations) \n",
            " Last accuracy mAP@0.5 = 98.08 % \n",
            " 1376: 0.611709, 0.463749 avg loss, 0.001000 rate, 0.291291 seconds, 88064 images\n",
            "Loaded: 1.506991 seconds\n",
            "\n",
            " (next mAP calculation at 1400 iterations) \n",
            " Last accuracy mAP@0.5 = 98.08 % \n",
            " 1377: 0.484696, 0.465844 avg loss, 0.001000 rate, 0.306605 seconds, 88128 images\n",
            "Loaded: 1.439571 seconds\n",
            "\n",
            " (next mAP calculation at 1400 iterations) \n",
            " Last accuracy mAP@0.5 = 98.08 % \n",
            " 1378: 0.405096, 0.459769 avg loss, 0.001000 rate, 0.326756 seconds, 88192 images\n",
            "Loaded: 1.532144 seconds\n",
            "\n",
            " (next mAP calculation at 1400 iterations) \n",
            " Last accuracy mAP@0.5 = 98.08 % \n",
            " 1379: 0.499686, 0.463761 avg loss, 0.001000 rate, 0.320192 seconds, 88256 images\n",
            "Loaded: 1.472468 seconds\n",
            "\n",
            " (next mAP calculation at 1400 iterations) \n",
            " Last accuracy mAP@0.5 = 98.08 % \n",
            " 1380: 0.506731, 0.468058 avg loss, 0.001000 rate, 0.316972 seconds, 88320 images\n",
            "Resizing\n",
            "608 x 608 \n",
            " try to allocate additional workspace_size = 4610.72 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.000038 seconds\n",
            "\n",
            " (next mAP calculation at 1400 iterations) \n",
            " Last accuracy mAP@0.5 = 98.08 % \n",
            " 1381: 0.597468, 0.480999 avg loss, 0.001000 rate, 1.139080 seconds, 88384 images\n",
            "Loaded: 1.102394 seconds\n",
            "\n",
            " (next mAP calculation at 1400 iterations) \n",
            " Last accuracy mAP@0.5 = 98.08 % \n",
            " 1382: 0.558822, 0.488781 avg loss, 0.001000 rate, 0.991661 seconds, 88448 images\n",
            "Loaded: 1.479385 seconds\n",
            "\n",
            " (next mAP calculation at 1400 iterations) \n",
            " Last accuracy mAP@0.5 = 98.08 % \n",
            " 1383: 0.513867, 0.491290 avg loss, 0.001000 rate, 1.113275 seconds, 88512 images\n",
            "Loaded: 1.134945 seconds\n",
            "\n",
            " (next mAP calculation at 1400 iterations) \n",
            " Last accuracy mAP@0.5 = 98.08 % \n",
            " 1384: 0.451209, 0.487282 avg loss, 0.001000 rate, 0.999474 seconds, 88576 images\n",
            "Loaded: 1.238667 seconds\n",
            "\n",
            " (next mAP calculation at 1400 iterations) \n",
            " Last accuracy mAP@0.5 = 98.08 % \n",
            " 1385: 0.498461, 0.488400 avg loss, 0.001000 rate, 1.139580 seconds, 88640 images\n",
            "Loaded: 1.152282 seconds\n",
            "\n",
            " (next mAP calculation at 1400 iterations) \n",
            " Last accuracy mAP@0.5 = 98.08 % \n",
            " 1386: 0.592251, 0.498785 avg loss, 0.001000 rate, 1.024504 seconds, 88704 images\n",
            "Loaded: 1.225008 seconds\n",
            "\n",
            " (next mAP calculation at 1400 iterations) \n",
            " Last accuracy mAP@0.5 = 98.08 % \n",
            " 1387: 0.465433, 0.495449 avg loss, 0.001000 rate, 1.061782 seconds, 88768 images\n",
            "Loaded: 1.199209 seconds\n",
            "\n",
            " (next mAP calculation at 1400 iterations) \n",
            " Last accuracy mAP@0.5 = 98.08 % \n",
            " 1388: 0.540866, 0.499991 avg loss, 0.001000 rate, 1.003187 seconds, 88832 images\n",
            "Loaded: 1.292565 seconds\n",
            "\n",
            " (next mAP calculation at 1400 iterations) \n",
            " Last accuracy mAP@0.5 = 98.08 % \n",
            " 1389: 0.467959, 0.496788 avg loss, 0.001000 rate, 1.034562 seconds, 88896 images\n",
            "Loaded: 1.219447 seconds\n",
            "\n",
            " (next mAP calculation at 1400 iterations) \n",
            " Last accuracy mAP@0.5 = 98.08 % \n",
            " 1390: 0.493589, 0.496468 avg loss, 0.001000 rate, 1.020016 seconds, 88960 images\n",
            "Resizing\n",
            "384 x 384 \n",
            " try to allocate additional workspace_size = 1302.33 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.000037 seconds\n",
            "\n",
            " (next mAP calculation at 1400 iterations) \n",
            " Last accuracy mAP@0.5 = 98.08 % \n",
            " 1391: 0.499569, 0.496778 avg loss, 0.001000 rate, 0.506078 seconds, 89024 images\n",
            "Loaded: 1.548809 seconds\n",
            "\n",
            " (next mAP calculation at 1400 iterations) \n",
            " Last accuracy mAP@0.5 = 98.08 % \n",
            " 1392: 0.384574, 0.485558 avg loss, 0.001000 rate, 0.358776 seconds, 89088 images\n",
            "Loaded: 1.622400 seconds\n",
            "\n",
            " (next mAP calculation at 1400 iterations) \n",
            " Last accuracy mAP@0.5 = 98.08 % \n",
            " 1393: 0.488231, 0.485825 avg loss, 0.001000 rate, 0.357359 seconds, 89152 images\n",
            "Loaded: 1.494210 seconds\n",
            "\n",
            " (next mAP calculation at 1400 iterations) \n",
            " Last accuracy mAP@0.5 = 98.08 % \n",
            " 1394: 0.487297, 0.485972 avg loss, 0.001000 rate, 0.349150 seconds, 89216 images\n",
            "Loaded: 1.431898 seconds\n",
            "\n",
            " (next mAP calculation at 1400 iterations) \n",
            " Last accuracy mAP@0.5 = 98.08 % \n",
            " 1395: 0.447224, 0.482097 avg loss, 0.001000 rate, 0.361204 seconds, 89280 images\n",
            "Loaded: 1.425578 seconds\n",
            "\n",
            " (next mAP calculation at 1400 iterations) \n",
            " Last accuracy mAP@0.5 = 98.08 % \n",
            " 1396: 0.490558, 0.482943 avg loss, 0.001000 rate, 0.340506 seconds, 89344 images\n",
            "Loaded: 1.388152 seconds\n",
            "\n",
            " (next mAP calculation at 1400 iterations) \n",
            " Last accuracy mAP@0.5 = 98.08 % \n",
            " 1397: 0.426230, 0.477272 avg loss, 0.001000 rate, 0.346177 seconds, 89408 images\n",
            "Loaded: 1.487928 seconds\n",
            "\n",
            " (next mAP calculation at 1400 iterations) \n",
            " Last accuracy mAP@0.5 = 98.08 % \n",
            " 1398: 0.406604, 0.470205 avg loss, 0.001000 rate, 0.420111 seconds, 89472 images\n",
            "Loaded: 1.334535 seconds\n",
            "\n",
            " (next mAP calculation at 1400 iterations) \n",
            " Last accuracy mAP@0.5 = 98.08 % \n",
            " 1399: 0.516483, 0.474833 avg loss, 0.001000 rate, 0.369275 seconds, 89536 images\n",
            "Loaded: 1.563729 seconds\n",
            "\n",
            " (next mAP calculation at 1400 iterations) \n",
            " Last accuracy mAP@0.5 = 98.08 % \n",
            " 1400: 0.502650, 0.477615 avg loss, 0.001000 rate, 0.343258 seconds, 89600 images\n",
            "Resizing to initial size: 416 x 416 \n",
            " try to allocate additional workspace_size = 1302.33 MB \n",
            " CUDA allocate done! \n",
            "\n",
            " calculation mAP (mean average precision)...\n",
            "368\n",
            " detections_count = 690, unique_truth_count = 374  \n",
            "class_id = 0, name = banana, ap = 97.72%   \t (TP = 348, FP = 13) \n",
            "\n",
            " for thresh = 0.25, precision = 0.96, recall = 0.93, F1-score = 0.95 \n",
            " for thresh = 0.25, TP = 348, FP = 13, FN = 26, average IoU = 73.55 % \n",
            "\n",
            " IoU threshold = 50 %, used Area-Under-Curve for each unique Recall \n",
            " mean average precision (mAP@0.50) = 0.977242, or 97.72 % \n",
            "Total Detection Time: 14.000000 Seconds\n",
            "\n",
            "Set -points flag:\n",
            " `-points 101` for MS COCO \n",
            " `-points 11` for PascalVOC 2007 (uncomment `difficult` in voc.data) \n",
            " `-points 0` (AUC) for ImageNet, PascalVOC 2010-2012, your custom dataset\n",
            "\n",
            " mean_average_precision (mAP@0.5) = 0.977242 \n",
            "Saving weights to /content/gdrive/My Drive/darknet/backup/yolov3-tiny-280120_last.weights\n",
            "Resizing\n",
            "352 x 352 \n",
            " try to allocate additional workspace_size = 1264.58 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 1.382395 seconds\n",
            "\n",
            " (next mAP calculation at 1500 iterations) \n",
            " Last accuracy mAP@0.5 = 97.72 % \n",
            " 1401: 0.469761, 0.476829 avg loss, 0.001000 rate, 0.426020 seconds, 89664 images\n",
            "Loaded: 1.406301 seconds\n",
            "\n",
            " (next mAP calculation at 1500 iterations) \n",
            " Last accuracy mAP@0.5 = 97.72 % \n",
            " 1402: 0.605515, 0.489698 avg loss, 0.001000 rate, 0.275655 seconds, 89728 images\n",
            "Loaded: 1.458870 seconds\n",
            "\n",
            " (next mAP calculation at 1500 iterations) \n",
            " Last accuracy mAP@0.5 = 97.72 % \n",
            " 1403: 0.445741, 0.485302 avg loss, 0.001000 rate, 0.298330 seconds, 89792 images\n",
            "Loaded: 1.464713 seconds\n",
            "\n",
            " (next mAP calculation at 1500 iterations) \n",
            " Last accuracy mAP@0.5 = 97.72 % \n",
            " 1404: 0.453292, 0.482101 avg loss, 0.001000 rate, 0.318020 seconds, 89856 images\n",
            "Loaded: 1.494784 seconds\n",
            "\n",
            " (next mAP calculation at 1500 iterations) \n",
            " Last accuracy mAP@0.5 = 97.72 % \n",
            " 1405: 0.427712, 0.476662 avg loss, 0.001000 rate, 0.310486 seconds, 89920 images\n",
            "Loaded: 1.399219 seconds\n",
            "\n",
            " (next mAP calculation at 1500 iterations) \n",
            " Last accuracy mAP@0.5 = 97.72 % \n",
            " 1406: 0.462732, 0.475269 avg loss, 0.001000 rate, 0.285110 seconds, 89984 images\n",
            "Loaded: 1.445264 seconds\n",
            "\n",
            " (next mAP calculation at 1500 iterations) \n",
            " Last accuracy mAP@0.5 = 97.72 % \n",
            " 1407: 0.404250, 0.468167 avg loss, 0.001000 rate, 0.308606 seconds, 90048 images\n",
            "Loaded: 1.443115 seconds\n",
            "\n",
            " (next mAP calculation at 1500 iterations) \n",
            " Last accuracy mAP@0.5 = 97.72 % \n",
            " 1408: 0.502011, 0.471552 avg loss, 0.001000 rate, 0.280737 seconds, 90112 images\n",
            "Loaded: 1.411116 seconds\n",
            "\n",
            " (next mAP calculation at 1500 iterations) \n",
            " Last accuracy mAP@0.5 = 97.72 % \n",
            " 1409: 0.455718, 0.469968 avg loss, 0.001000 rate, 0.317464 seconds, 90176 images\n",
            "Loaded: 1.413934 seconds\n",
            "\n",
            " (next mAP calculation at 1500 iterations) \n",
            " Last accuracy mAP@0.5 = 97.72 % \n",
            " 1410: 0.460238, 0.468995 avg loss, 0.001000 rate, 0.327566 seconds, 90240 images\n",
            "Resizing\n",
            "448 x 448 \n",
            " try to allocate additional workspace_size = 1233.65 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.000038 seconds\n",
            "\n",
            " (next mAP calculation at 1500 iterations) \n",
            " Last accuracy mAP@0.5 = 97.72 % \n",
            " 1411: 0.491824, 0.471278 avg loss, 0.001000 rate, 0.533088 seconds, 90304 images\n",
            "Loaded: 1.486394 seconds\n",
            "\n",
            " (next mAP calculation at 1500 iterations) \n",
            " Last accuracy mAP@0.5 = 97.72 % \n",
            " 1412: 0.467783, 0.470929 avg loss, 0.001000 rate, 0.468161 seconds, 90368 images\n",
            "Loaded: 1.543445 seconds\n",
            "\n",
            " (next mAP calculation at 1500 iterations) \n",
            " Last accuracy mAP@0.5 = 97.72 % \n",
            " 1413: 0.482114, 0.472047 avg loss, 0.001000 rate, 0.533805 seconds, 90432 images\n",
            "Loaded: 1.479643 seconds\n",
            "\n",
            " (next mAP calculation at 1500 iterations) \n",
            " Last accuracy mAP@0.5 = 97.72 % \n",
            " 1414: 0.496995, 0.474542 avg loss, 0.001000 rate, 0.570969 seconds, 90496 images\n",
            "Loaded: 1.314101 seconds\n",
            "\n",
            " (next mAP calculation at 1500 iterations) \n",
            " Last accuracy mAP@0.5 = 97.72 % \n",
            " 1415: 0.404996, 0.467587 avg loss, 0.001000 rate, 0.529438 seconds, 90560 images\n",
            "Loaded: 1.407300 seconds\n",
            "\n",
            " (next mAP calculation at 1500 iterations) \n",
            " Last accuracy mAP@0.5 = 97.72 % \n",
            " 1416: 0.441240, 0.464953 avg loss, 0.001000 rate, 0.484558 seconds, 90624 images\n",
            "Loaded: 1.431864 seconds\n",
            "\n",
            " (next mAP calculation at 1500 iterations) \n",
            " Last accuracy mAP@0.5 = 97.72 % \n",
            " 1417: 0.475791, 0.466036 avg loss, 0.001000 rate, 0.506951 seconds, 90688 images\n",
            "Loaded: 1.346778 seconds\n",
            "\n",
            " (next mAP calculation at 1500 iterations) \n",
            " Last accuracy mAP@0.5 = 97.72 % \n",
            " 1418: 0.468691, 0.466302 avg loss, 0.001000 rate, 0.498139 seconds, 90752 images\n",
            "Loaded: 1.390086 seconds\n",
            "\n",
            " (next mAP calculation at 1500 iterations) \n",
            " Last accuracy mAP@0.5 = 97.72 % \n",
            " 1419: 0.393214, 0.458993 avg loss, 0.001000 rate, 0.493375 seconds, 90816 images\n",
            "Loaded: 1.555626 seconds\n",
            "\n",
            " (next mAP calculation at 1500 iterations) \n",
            " Last accuracy mAP@0.5 = 97.72 % \n",
            " 1420: 0.386095, 0.451703 avg loss, 0.001000 rate, 0.514710 seconds, 90880 images\n",
            "Resizing\n",
            "576 x 576 \n",
            " try to allocate additional workspace_size = 4605.87 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.000041 seconds\n",
            "\n",
            " (next mAP calculation at 1500 iterations) \n",
            " Last accuracy mAP@0.5 = 97.72 % \n",
            " 1421: 0.384963, 0.445029 avg loss, 0.001000 rate, 0.934772 seconds, 90944 images\n",
            "Loaded: 1.281361 seconds\n",
            "\n",
            " (next mAP calculation at 1500 iterations) \n",
            " Last accuracy mAP@0.5 = 97.72 % \n",
            " 1422: 0.549752, 0.455502 avg loss, 0.001000 rate, 0.902915 seconds, 91008 images\n",
            "Loaded: 1.304825 seconds\n",
            "\n",
            " (next mAP calculation at 1500 iterations) \n",
            " Last accuracy mAP@0.5 = 97.72 % \n",
            " 1423: 0.513178, 0.461269 avg loss, 0.001000 rate, 0.902452 seconds, 91072 images\n",
            "Loaded: 1.240168 seconds\n",
            "\n",
            " (next mAP calculation at 1500 iterations) \n",
            " Last accuracy mAP@0.5 = 97.72 % \n",
            " 1424: 0.483446, 0.463487 avg loss, 0.001000 rate, 0.931206 seconds, 91136 images\n",
            "Loaded: 1.273606 seconds\n",
            "\n",
            " (next mAP calculation at 1500 iterations) \n",
            " Last accuracy mAP@0.5 = 97.72 % \n",
            " 1425: 0.558965, 0.473035 avg loss, 0.001000 rate, 0.910763 seconds, 91200 images\n",
            "Loaded: 1.251223 seconds\n",
            "\n",
            " (next mAP calculation at 1500 iterations) \n",
            " Last accuracy mAP@0.5 = 97.72 % \n",
            " 1426: 0.587303, 0.484461 avg loss, 0.001000 rate, 0.825019 seconds, 91264 images\n",
            "Loaded: 1.317863 seconds\n",
            "\n",
            " (next mAP calculation at 1500 iterations) \n",
            " Last accuracy mAP@0.5 = 97.72 % \n",
            " 1427: 0.442237, 0.480239 avg loss, 0.001000 rate, 0.892445 seconds, 91328 images\n",
            "Loaded: 1.368897 seconds\n",
            "\n",
            " (next mAP calculation at 1500 iterations) \n",
            " Last accuracy mAP@0.5 = 97.72 % \n",
            " 1428: 0.392097, 0.471425 avg loss, 0.001000 rate, 1.029726 seconds, 91392 images\n",
            "Loaded: 1.178487 seconds\n",
            "\n",
            " (next mAP calculation at 1500 iterations) \n",
            " Last accuracy mAP@0.5 = 97.72 % \n",
            " 1429: 0.385842, 0.462866 avg loss, 0.001000 rate, 0.814525 seconds, 91456 images\n",
            "Loaded: 1.292460 seconds\n",
            "\n",
            " (next mAP calculation at 1500 iterations) \n",
            " Last accuracy mAP@0.5 = 97.72 % \n",
            " 1430: 0.470119, 0.463592 avg loss, 0.001000 rate, 0.902049 seconds, 91520 images\n",
            "Resizing\n",
            "384 x 384 \n",
            " try to allocate additional workspace_size = 1302.33 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.000046 seconds\n",
            "\n",
            " (next mAP calculation at 1500 iterations) \n",
            " Last accuracy mAP@0.5 = 97.72 % \n",
            " 1431: 0.414723, 0.458705 avg loss, 0.001000 rate, 0.407656 seconds, 91584 images\n",
            "Loaded: 1.336397 seconds\n",
            "\n",
            " (next mAP calculation at 1500 iterations) \n",
            " Last accuracy mAP@0.5 = 97.72 % \n",
            " 1432: 0.492180, 0.462052 avg loss, 0.001000 rate, 0.400176 seconds, 91648 images\n",
            "Loaded: 1.396347 seconds\n",
            "\n",
            " (next mAP calculation at 1500 iterations) \n",
            " Last accuracy mAP@0.5 = 97.72 % \n",
            " 1433: 0.443035, 0.460151 avg loss, 0.001000 rate, 0.385186 seconds, 91712 images\n",
            "Loaded: 1.406614 seconds\n",
            "\n",
            " (next mAP calculation at 1500 iterations) \n",
            " Last accuracy mAP@0.5 = 97.72 % \n",
            " 1434: 0.451878, 0.459323 avg loss, 0.001000 rate, 0.372906 seconds, 91776 images\n",
            "Loaded: 1.436855 seconds\n",
            "\n",
            " (next mAP calculation at 1500 iterations) \n",
            " Last accuracy mAP@0.5 = 97.72 % \n",
            " 1435: 0.480352, 0.461426 avg loss, 0.001000 rate, 0.394243 seconds, 91840 images\n",
            "Loaded: 1.370674 seconds\n",
            "\n",
            " (next mAP calculation at 1500 iterations) \n",
            " Last accuracy mAP@0.5 = 97.72 % \n",
            " 1436: 0.480541, 0.463338 avg loss, 0.001000 rate, 0.357885 seconds, 91904 images\n",
            "Loaded: 1.416272 seconds\n",
            "\n",
            " (next mAP calculation at 1500 iterations) \n",
            " Last accuracy mAP@0.5 = 97.72 % \n",
            " 1437: 0.478997, 0.464904 avg loss, 0.001000 rate, 0.367526 seconds, 91968 images\n",
            "Loaded: 1.598800 seconds\n",
            "\n",
            " (next mAP calculation at 1500 iterations) \n",
            " Last accuracy mAP@0.5 = 97.72 % \n",
            " 1438: 0.399194, 0.458333 avg loss, 0.001000 rate, 0.372681 seconds, 92032 images\n",
            "Loaded: 1.496062 seconds\n",
            "\n",
            " (next mAP calculation at 1500 iterations) \n",
            " Last accuracy mAP@0.5 = 97.72 % \n",
            " 1439: 0.553654, 0.467865 avg loss, 0.001000 rate, 0.388379 seconds, 92096 images\n",
            "Loaded: 1.526793 seconds\n",
            "\n",
            " (next mAP calculation at 1500 iterations) \n",
            " Last accuracy mAP@0.5 = 97.72 % \n",
            " 1440: 0.419573, 0.463036 avg loss, 0.001000 rate, 0.378134 seconds, 92160 images\n",
            "Resizing\n",
            "480 x 480 \n",
            " try to allocate additional workspace_size = 4592.89 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.000044 seconds\n",
            "\n",
            " (next mAP calculation at 1500 iterations) \n",
            " Last accuracy mAP@0.5 = 97.72 % \n",
            " 1441: 0.424629, 0.459195 avg loss, 0.001000 rate, 0.626777 seconds, 92224 images\n",
            "Loaded: 1.371146 seconds\n",
            "\n",
            " (next mAP calculation at 1500 iterations) \n",
            " Last accuracy mAP@0.5 = 97.72 % \n",
            " 1442: 0.351828, 0.448458 avg loss, 0.001000 rate, 0.619213 seconds, 92288 images\n",
            "Loaded: 1.420871 seconds\n",
            "\n",
            " (next mAP calculation at 1500 iterations) \n",
            " Last accuracy mAP@0.5 = 97.72 % \n",
            " 1443: 0.493126, 0.452925 avg loss, 0.001000 rate, 0.702642 seconds, 92352 images\n",
            "Loaded: 1.318133 seconds\n",
            "\n",
            " (next mAP calculation at 1500 iterations) \n",
            " Last accuracy mAP@0.5 = 97.72 % \n",
            " 1444: 0.516960, 0.459329 avg loss, 0.001000 rate, 0.645740 seconds, 92416 images\n",
            "Loaded: 1.302552 seconds\n",
            "\n",
            " (next mAP calculation at 1500 iterations) \n",
            " Last accuracy mAP@0.5 = 97.72 % \n",
            " 1445: 0.372300, 0.450626 avg loss, 0.001000 rate, 0.648789 seconds, 92480 images\n",
            "Loaded: 1.301618 seconds\n",
            "\n",
            " (next mAP calculation at 1500 iterations) \n",
            " Last accuracy mAP@0.5 = 97.72 % \n",
            " 1446: 0.351157, 0.440679 avg loss, 0.001000 rate, 0.589194 seconds, 92544 images\n",
            "Loaded: 1.377387 seconds\n",
            "\n",
            " (next mAP calculation at 1500 iterations) \n",
            " Last accuracy mAP@0.5 = 97.72 % \n",
            " 1447: 0.437680, 0.440379 avg loss, 0.001000 rate, 0.566988 seconds, 92608 images\n",
            "Loaded: 1.382895 seconds\n",
            "\n",
            " (next mAP calculation at 1500 iterations) \n",
            " Last accuracy mAP@0.5 = 97.72 % \n",
            " 1448: 0.508341, 0.447175 avg loss, 0.001000 rate, 0.620340 seconds, 92672 images\n",
            "Loaded: 1.383970 seconds\n",
            "\n",
            " (next mAP calculation at 1500 iterations) \n",
            " Last accuracy mAP@0.5 = 97.72 % \n",
            " 1449: 0.521731, 0.454631 avg loss, 0.001000 rate, 0.600378 seconds, 92736 images\n",
            "Loaded: 1.348041 seconds\n",
            "\n",
            " (next mAP calculation at 1500 iterations) \n",
            " Last accuracy mAP@0.5 = 97.72 % \n",
            " 1450: 0.398425, 0.449010 avg loss, 0.001000 rate, 0.722748 seconds, 92800 images\n",
            "Resizing\n",
            "480 x 480 \n",
            " try to allocate additional workspace_size = 4592.89 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 1.444015 seconds\n",
            "\n",
            " (next mAP calculation at 1500 iterations) \n",
            " Last accuracy mAP@0.5 = 97.72 % \n",
            " 1451: 0.499576, 0.454067 avg loss, 0.001000 rate, 0.719368 seconds, 92864 images\n",
            "Loaded: 1.226298 seconds\n",
            "\n",
            " (next mAP calculation at 1500 iterations) \n",
            " Last accuracy mAP@0.5 = 97.72 % \n",
            " 1452: 0.401337, 0.448794 avg loss, 0.001000 rate, 0.631630 seconds, 92928 images\n",
            "Loaded: 1.330846 seconds\n",
            "\n",
            " (next mAP calculation at 1500 iterations) \n",
            " Last accuracy mAP@0.5 = 97.72 % \n",
            " 1453: 0.521186, 0.456033 avg loss, 0.001000 rate, 0.599565 seconds, 92992 images\n",
            "Loaded: 1.510475 seconds\n",
            "\n",
            " (next mAP calculation at 1500 iterations) \n",
            " Last accuracy mAP@0.5 = 97.72 % \n",
            " 1454: 0.452719, 0.455701 avg loss, 0.001000 rate, 0.602627 seconds, 93056 images\n",
            "Loaded: 1.286029 seconds\n",
            "\n",
            " (next mAP calculation at 1500 iterations) \n",
            " Last accuracy mAP@0.5 = 97.72 % \n",
            " 1455: 0.444050, 0.454536 avg loss, 0.001000 rate, 0.658563 seconds, 93120 images\n",
            "Loaded: 1.362442 seconds\n",
            "\n",
            " (next mAP calculation at 1500 iterations) \n",
            " Last accuracy mAP@0.5 = 97.72 % \n",
            " 1456: 0.508580, 0.459941 avg loss, 0.001000 rate, 0.662328 seconds, 93184 images\n",
            "Loaded: 1.364901 seconds\n",
            "\n",
            " (next mAP calculation at 1500 iterations) \n",
            " Last accuracy mAP@0.5 = 97.72 % \n",
            " 1457: 0.514239, 0.465370 avg loss, 0.001000 rate, 0.599734 seconds, 93248 images\n",
            "Loaded: 1.358127 seconds\n",
            "\n",
            " (next mAP calculation at 1500 iterations) \n",
            " Last accuracy mAP@0.5 = 97.72 % \n",
            " 1458: 0.484242, 0.467258 avg loss, 0.001000 rate, 0.655837 seconds, 93312 images\n",
            "Loaded: 1.412906 seconds\n",
            "\n",
            " (next mAP calculation at 1500 iterations) \n",
            " Last accuracy mAP@0.5 = 97.72 % \n",
            " 1459: 0.411166, 0.461648 avg loss, 0.001000 rate, 0.660155 seconds, 93376 images\n",
            "Loaded: 1.301009 seconds\n",
            "\n",
            " (next mAP calculation at 1500 iterations) \n",
            " Last accuracy mAP@0.5 = 97.72 % \n",
            " 1460: 0.425198, 0.458003 avg loss, 0.001000 rate, 0.641464 seconds, 93440 images\n",
            "Resizing\n",
            "576 x 576 \n",
            " try to allocate additional workspace_size = 4605.87 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.000033 seconds\n",
            "\n",
            " (next mAP calculation at 1500 iterations) \n",
            " Last accuracy mAP@0.5 = 97.72 % \n",
            " 1461: 0.390850, 0.451288 avg loss, 0.001000 rate, 0.878568 seconds, 93504 images\n",
            "Loaded: 1.316380 seconds\n",
            "\n",
            " (next mAP calculation at 1500 iterations) \n",
            " Last accuracy mAP@0.5 = 97.72 % \n",
            " 1462: 0.436000, 0.449759 avg loss, 0.001000 rate, 0.876443 seconds, 93568 images\n",
            "Loaded: 1.403478 seconds\n",
            "\n",
            " (next mAP calculation at 1500 iterations) \n",
            " Last accuracy mAP@0.5 = 97.72 % \n",
            " 1463: 0.535323, 0.458316 avg loss, 0.001000 rate, 0.990988 seconds, 93632 images\n",
            "Loaded: 1.323400 seconds\n",
            "\n",
            " (next mAP calculation at 1500 iterations) \n",
            " Last accuracy mAP@0.5 = 97.72 % \n",
            " 1464: 0.545518, 0.467036 avg loss, 0.001000 rate, 0.890470 seconds, 93696 images\n",
            "Loaded: 1.298565 seconds\n",
            "\n",
            " (next mAP calculation at 1500 iterations) \n",
            " Last accuracy mAP@0.5 = 97.72 % \n",
            " 1465: 0.424097, 0.462742 avg loss, 0.001000 rate, 0.955500 seconds, 93760 images\n",
            "Loaded: 1.252664 seconds\n",
            "\n",
            " (next mAP calculation at 1500 iterations) \n",
            " Last accuracy mAP@0.5 = 97.72 % \n",
            " 1466: 0.475611, 0.464029 avg loss, 0.001000 rate, 0.875305 seconds, 93824 images\n",
            "Loaded: 1.298391 seconds\n",
            "\n",
            " (next mAP calculation at 1500 iterations) \n",
            " Last accuracy mAP@0.5 = 97.72 % \n",
            " 1467: 0.500860, 0.467712 avg loss, 0.001000 rate, 0.875182 seconds, 93888 images\n",
            "Loaded: 1.393861 seconds\n",
            "\n",
            " (next mAP calculation at 1500 iterations) \n",
            " Last accuracy mAP@0.5 = 97.72 % \n",
            " 1468: 0.415366, 0.462477 avg loss, 0.001000 rate, 1.021656 seconds, 93952 images\n",
            "Loaded: 1.311858 seconds\n",
            "\n",
            " (next mAP calculation at 1500 iterations) \n",
            " Last accuracy mAP@0.5 = 97.72 % \n",
            " 1469: 0.443040, 0.460534 avg loss, 0.001000 rate, 0.948340 seconds, 94016 images\n",
            "Loaded: 1.263416 seconds\n",
            "\n",
            " (next mAP calculation at 1500 iterations) \n",
            " Last accuracy mAP@0.5 = 97.72 % \n",
            " 1470: 0.460405, 0.460521 avg loss, 0.001000 rate, 0.834707 seconds, 94080 images\n",
            "Resizing\n",
            "352 x 352 \n",
            " try to allocate additional workspace_size = 1264.58 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.119233 seconds\n",
            "\n",
            " (next mAP calculation at 1500 iterations) \n",
            " Last accuracy mAP@0.5 = 97.72 % \n",
            " 1471: 0.398389, 0.454308 avg loss, 0.001000 rate, 0.406257 seconds, 94144 images\n",
            "Loaded: 1.387125 seconds\n",
            "\n",
            " (next mAP calculation at 1500 iterations) \n",
            " Last accuracy mAP@0.5 = 97.72 % \n",
            " 1472: 0.512427, 0.460120 avg loss, 0.001000 rate, 0.296269 seconds, 94208 images\n",
            "Loaded: 1.518396 seconds\n",
            "\n",
            " (next mAP calculation at 1500 iterations) \n",
            " Last accuracy mAP@0.5 = 97.72 % \n",
            " 1473: 0.377915, 0.451899 avg loss, 0.001000 rate, 0.332919 seconds, 94272 images\n",
            "Loaded: 1.537626 seconds\n",
            "\n",
            " (next mAP calculation at 1500 iterations) \n",
            " Last accuracy mAP@0.5 = 97.72 % \n",
            " 1474: 0.413503, 0.448060 avg loss, 0.001000 rate, 0.288162 seconds, 94336 images\n",
            "Loaded: 1.447707 seconds\n",
            "\n",
            " (next mAP calculation at 1500 iterations) \n",
            " Last accuracy mAP@0.5 = 97.72 % \n",
            " 1475: 0.470936, 0.450347 avg loss, 0.001000 rate, 0.283517 seconds, 94400 images\n",
            "Loaded: 1.520657 seconds\n",
            "\n",
            " (next mAP calculation at 1500 iterations) \n",
            " Last accuracy mAP@0.5 = 97.72 % \n",
            " 1476: 0.463541, 0.451667 avg loss, 0.001000 rate, 0.303926 seconds, 94464 images\n",
            "Loaded: 1.462560 seconds\n",
            "\n",
            " (next mAP calculation at 1500 iterations) \n",
            " Last accuracy mAP@0.5 = 97.72 % \n",
            " 1477: 0.486244, 0.455124 avg loss, 0.001000 rate, 0.304481 seconds, 94528 images\n",
            "Loaded: 1.452364 seconds\n",
            "\n",
            " (next mAP calculation at 1500 iterations) \n",
            " Last accuracy mAP@0.5 = 97.72 % \n",
            " 1478: 0.477341, 0.457346 avg loss, 0.001000 rate, 0.290855 seconds, 94592 images\n",
            "Loaded: 1.417137 seconds\n",
            "\n",
            " (next mAP calculation at 1500 iterations) \n",
            " Last accuracy mAP@0.5 = 97.72 % \n",
            " 1479: 0.469581, 0.458569 avg loss, 0.001000 rate, 0.299370 seconds, 94656 images\n",
            "Loaded: 1.618442 seconds\n",
            "\n",
            " (next mAP calculation at 1500 iterations) \n",
            " Last accuracy mAP@0.5 = 97.72 % \n",
            " 1480: 0.461383, 0.458851 avg loss, 0.001000 rate, 0.323865 seconds, 94720 images\n",
            "Resizing\n",
            "448 x 448 \n",
            " try to allocate additional workspace_size = 1233.65 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.000035 seconds\n",
            "\n",
            " (next mAP calculation at 1500 iterations) \n",
            " Last accuracy mAP@0.5 = 97.72 % \n",
            " 1481: 0.356969, 0.448663 avg loss, 0.001000 rate, 0.495166 seconds, 94784 images\n",
            "Loaded: 1.393427 seconds\n",
            "\n",
            " (next mAP calculation at 1500 iterations) \n",
            " Last accuracy mAP@0.5 = 97.72 % \n",
            " 1482: 0.307305, 0.434527 avg loss, 0.001000 rate, 0.592354 seconds, 94848 images\n",
            "Loaded: 1.235039 seconds\n",
            "\n",
            " (next mAP calculation at 1500 iterations) \n",
            " Last accuracy mAP@0.5 = 97.72 % \n",
            " 1483: 0.465417, 0.437616 avg loss, 0.001000 rate, 0.471100 seconds, 94912 images\n",
            "Loaded: 1.423408 seconds\n",
            "\n",
            " (next mAP calculation at 1500 iterations) \n",
            " Last accuracy mAP@0.5 = 97.72 % \n",
            " 1484: 0.311369, 0.424991 avg loss, 0.001000 rate, 0.461303 seconds, 94976 images\n",
            "Loaded: 1.458627 seconds\n",
            "\n",
            " (next mAP calculation at 1500 iterations) \n",
            " Last accuracy mAP@0.5 = 97.72 % \n",
            " 1485: 0.399618, 0.422454 avg loss, 0.001000 rate, 0.476715 seconds, 95040 images\n",
            "Loaded: 1.434236 seconds\n",
            "\n",
            " (next mAP calculation at 1500 iterations) \n",
            " Last accuracy mAP@0.5 = 97.72 % \n",
            " 1486: 0.362826, 0.416491 avg loss, 0.001000 rate, 0.553499 seconds, 95104 images\n",
            "Loaded: 1.363398 seconds\n",
            "\n",
            " (next mAP calculation at 1500 iterations) \n",
            " Last accuracy mAP@0.5 = 97.72 % \n",
            " 1487: 0.311631, 0.406005 avg loss, 0.001000 rate, 0.491087 seconds, 95168 images\n",
            "Loaded: 1.436266 seconds\n",
            "\n",
            " (next mAP calculation at 1500 iterations) \n",
            " Last accuracy mAP@0.5 = 97.72 % \n",
            " 1488: 0.460904, 0.411495 avg loss, 0.001000 rate, 0.497897 seconds, 95232 images\n",
            "Loaded: 1.477320 seconds\n",
            "\n",
            " (next mAP calculation at 1500 iterations) \n",
            " Last accuracy mAP@0.5 = 97.72 % \n",
            " 1489: 0.422144, 0.412560 avg loss, 0.001000 rate, 0.610194 seconds, 95296 images\n",
            "Loaded: 1.492775 seconds\n",
            "\n",
            " (next mAP calculation at 1500 iterations) \n",
            " Last accuracy mAP@0.5 = 97.72 % \n",
            " 1490: 0.379196, 0.409224 avg loss, 0.001000 rate, 0.468810 seconds, 95360 images\n",
            "Resizing\n",
            "448 x 448 \n",
            " try to allocate additional workspace_size = 1233.65 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 1.506304 seconds\n",
            "\n",
            " (next mAP calculation at 1500 iterations) \n",
            " Last accuracy mAP@0.5 = 97.72 % \n",
            " 1491: 0.438674, 0.412169 avg loss, 0.001000 rate, 0.625168 seconds, 95424 images\n",
            "Loaded: 1.264800 seconds\n",
            "\n",
            " (next mAP calculation at 1500 iterations) \n",
            " Last accuracy mAP@0.5 = 97.72 % \n",
            " 1492: 0.455164, 0.416468 avg loss, 0.001000 rate, 0.482051 seconds, 95488 images\n",
            "Loaded: 1.422837 seconds\n",
            "\n",
            " (next mAP calculation at 1500 iterations) \n",
            " Last accuracy mAP@0.5 = 97.72 % \n",
            " 1493: 0.390325, 0.413854 avg loss, 0.001000 rate, 0.490108 seconds, 95552 images\n",
            "Loaded: 1.424345 seconds\n",
            "\n",
            " (next mAP calculation at 1500 iterations) \n",
            " Last accuracy mAP@0.5 = 97.72 % \n",
            " 1494: 0.411607, 0.413629 avg loss, 0.001000 rate, 0.462678 seconds, 95616 images\n",
            "Loaded: 1.490991 seconds\n",
            "\n",
            " (next mAP calculation at 1500 iterations) \n",
            " Last accuracy mAP@0.5 = 97.72 % \n",
            " 1495: 0.463963, 0.418662 avg loss, 0.001000 rate, 0.479147 seconds, 95680 images\n",
            "Loaded: 1.499870 seconds\n",
            "\n",
            " (next mAP calculation at 1500 iterations) \n",
            " Last accuracy mAP@0.5 = 97.72 % \n",
            " 1496: 0.453124, 0.422109 avg loss, 0.001000 rate, 0.464570 seconds, 95744 images\n",
            "Loaded: 1.386000 seconds\n",
            "\n",
            " (next mAP calculation at 1500 iterations) \n",
            " Last accuracy mAP@0.5 = 97.72 % \n",
            " 1497: 0.380029, 0.417901 avg loss, 0.001000 rate, 0.458710 seconds, 95808 images\n",
            "Loaded: 1.420995 seconds\n",
            "\n",
            " (next mAP calculation at 1500 iterations) \n",
            " Last accuracy mAP@0.5 = 97.72 % \n",
            " 1498: 0.347301, 0.410841 avg loss, 0.001000 rate, 0.466334 seconds, 95872 images\n",
            "Loaded: 1.380532 seconds\n",
            "\n",
            " (next mAP calculation at 1500 iterations) \n",
            " Last accuracy mAP@0.5 = 97.72 % \n",
            " 1499: 0.459251, 0.415682 avg loss, 0.001000 rate, 0.510257 seconds, 95936 images\n",
            "Loaded: 1.432985 seconds\n",
            "\n",
            " (next mAP calculation at 1500 iterations) \n",
            " Last accuracy mAP@0.5 = 97.72 % \n",
            " 1500: 0.384586, 0.412572 avg loss, 0.001000 rate, 0.553346 seconds, 96000 images\n",
            "Resizing to initial size: 416 x 416 \n",
            " try to allocate additional workspace_size = 1302.33 MB \n",
            " CUDA allocate done! \n",
            "\n",
            " calculation mAP (mean average precision)...\n",
            "368\n",
            " detections_count = 661, unique_truth_count = 374  \n",
            "class_id = 0, name = banana, ap = 98.46%   \t (TP = 362, FP = 10) \n",
            "\n",
            " for thresh = 0.25, precision = 0.97, recall = 0.97, F1-score = 0.97 \n",
            " for thresh = 0.25, TP = 362, FP = 10, FN = 12, average IoU = 75.43 % \n",
            "\n",
            " IoU threshold = 50 %, used Area-Under-Curve for each unique Recall \n",
            " mean average precision (mAP@0.50) = 0.984621, or 98.46 % \n",
            "Total Detection Time: 16.000000 Seconds\n",
            "\n",
            "Set -points flag:\n",
            " `-points 101` for MS COCO \n",
            " `-points 11` for PascalVOC 2007 (uncomment `difficult` in voc.data) \n",
            " `-points 0` (AUC) for ImageNet, PascalVOC 2010-2012, your custom dataset\n",
            "\n",
            " mean_average_precision (mAP@0.5) = 0.984621 \n",
            "Saving weights to /content/gdrive/My Drive/darknet/backup/yolov3-tiny-280120_last.weights\n",
            "Resizing\n",
            "384 x 384 \n",
            " try to allocate additional workspace_size = 1302.33 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 1.425455 seconds\n",
            "\n",
            " (next mAP calculation at 1600 iterations) \n",
            " Last accuracy mAP@0.5 = 98.46 % \n",
            " 1501: 0.413338, 0.412649 avg loss, 0.001000 rate, 0.439185 seconds, 96064 images\n",
            "Loaded: 1.414247 seconds\n",
            "\n",
            " (next mAP calculation at 1600 iterations) \n",
            " Last accuracy mAP@0.5 = 98.46 % \n",
            " 1502: 0.392873, 0.410671 avg loss, 0.001000 rate, 0.365776 seconds, 96128 images\n",
            "Loaded: 1.491356 seconds\n",
            "\n",
            " (next mAP calculation at 1600 iterations) \n",
            " Last accuracy mAP@0.5 = 98.46 % \n",
            " 1503: 0.364784, 0.406083 avg loss, 0.001000 rate, 0.367808 seconds, 96192 images\n",
            "Loaded: 1.456848 seconds\n",
            "\n",
            " (next mAP calculation at 1600 iterations) \n",
            " Last accuracy mAP@0.5 = 98.46 % \n",
            " 1504: 0.494286, 0.414903 avg loss, 0.001000 rate, 0.359081 seconds, 96256 images\n",
            "Loaded: 1.443245 seconds\n",
            "\n",
            " (next mAP calculation at 1600 iterations) \n",
            " Last accuracy mAP@0.5 = 98.46 % \n",
            " 1505: 0.555306, 0.428943 avg loss, 0.001000 rate, 0.376224 seconds, 96320 images\n",
            "Loaded: 1.466674 seconds\n",
            "\n",
            " (next mAP calculation at 1600 iterations) \n",
            " Last accuracy mAP@0.5 = 98.46 % \n",
            " 1506: 0.451506, 0.431199 avg loss, 0.001000 rate, 0.385961 seconds, 96384 images\n",
            "Loaded: 1.422833 seconds\n",
            "\n",
            " (next mAP calculation at 1600 iterations) \n",
            " Last accuracy mAP@0.5 = 98.46 % \n",
            " 1507: 0.348899, 0.422969 avg loss, 0.001000 rate, 0.361535 seconds, 96448 images\n",
            "Loaded: 1.431524 seconds\n",
            "\n",
            " (next mAP calculation at 1600 iterations) \n",
            " Last accuracy mAP@0.5 = 98.46 % \n",
            " 1508: 0.356346, 0.416307 avg loss, 0.001000 rate, 0.352451 seconds, 96512 images\n",
            "Loaded: 1.446046 seconds\n",
            "\n",
            " (next mAP calculation at 1600 iterations) \n",
            " Last accuracy mAP@0.5 = 98.46 % \n",
            " 1509: 0.439044, 0.418581 avg loss, 0.001000 rate, 0.363243 seconds, 96576 images\n",
            "Loaded: 1.476669 seconds\n",
            "\n",
            " (next mAP calculation at 1600 iterations) \n",
            " Last accuracy mAP@0.5 = 98.46 % \n",
            " 1510: 0.414510, 0.418174 avg loss, 0.001000 rate, 0.399823 seconds, 96640 images\n",
            "Resizing\n",
            "352 x 352 \n",
            " try to allocate additional workspace_size = 1264.58 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 1.694936 seconds\n",
            "\n",
            " (next mAP calculation at 1600 iterations) \n",
            " Last accuracy mAP@0.5 = 98.46 % \n",
            " 1511: 0.390614, 0.415418 avg loss, 0.001000 rate, 0.392805 seconds, 96704 images\n",
            "Loaded: 1.375831 seconds\n",
            "\n",
            " (next mAP calculation at 1600 iterations) \n",
            " Last accuracy mAP@0.5 = 98.46 % \n",
            " 1512: 0.327226, 0.406598 avg loss, 0.001000 rate, 0.294262 seconds, 96768 images\n",
            "Loaded: 1.458173 seconds\n",
            "\n",
            " (next mAP calculation at 1600 iterations) \n",
            " Last accuracy mAP@0.5 = 98.46 % \n",
            " 1513: 0.324185, 0.398357 avg loss, 0.001000 rate, 0.294692 seconds, 96832 images\n",
            "Loaded: 1.493263 seconds\n",
            "\n",
            " (next mAP calculation at 1600 iterations) \n",
            " Last accuracy mAP@0.5 = 98.46 % \n",
            " 1514: 0.439332, 0.402455 avg loss, 0.001000 rate, 0.287896 seconds, 96896 images\n",
            "Loaded: 1.451061 seconds\n",
            "\n",
            " (next mAP calculation at 1600 iterations) \n",
            " Last accuracy mAP@0.5 = 98.46 % \n",
            " 1515: 0.409974, 0.403206 avg loss, 0.001000 rate, 0.299623 seconds, 96960 images\n",
            "Loaded: 1.465952 seconds\n",
            "\n",
            " (next mAP calculation at 1600 iterations) \n",
            " Last accuracy mAP@0.5 = 98.46 % \n",
            " 1516: 0.293003, 0.392186 avg loss, 0.001000 rate, 0.283227 seconds, 97024 images\n",
            "Loaded: 1.568996 seconds\n",
            "\n",
            " (next mAP calculation at 1600 iterations) \n",
            " Last accuracy mAP@0.5 = 98.46 % \n",
            " 1517: 0.403823, 0.393350 avg loss, 0.001000 rate, 0.297259 seconds, 97088 images\n",
            "Loaded: 1.554654 seconds\n",
            "\n",
            " (next mAP calculation at 1600 iterations) \n",
            " Last accuracy mAP@0.5 = 98.46 % \n",
            " 1518: 0.374017, 0.391417 avg loss, 0.001000 rate, 0.277781 seconds, 97152 images\n",
            "Loaded: 1.399589 seconds\n",
            "\n",
            " (next mAP calculation at 1600 iterations) \n",
            " Last accuracy mAP@0.5 = 98.46 % \n",
            " 1519: 0.393062, 0.391581 avg loss, 0.001000 rate, 0.305691 seconds, 97216 images\n",
            "Loaded: 1.449109 seconds\n",
            "\n",
            " (next mAP calculation at 1600 iterations) \n",
            " Last accuracy mAP@0.5 = 98.46 % \n",
            " 1520: 0.488141, 0.401237 avg loss, 0.001000 rate, 0.292856 seconds, 97280 images\n",
            "Resizing\n",
            "480 x 480 \n",
            " try to allocate additional workspace_size = 4592.89 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.000040 seconds\n",
            "\n",
            " (next mAP calculation at 1600 iterations) \n",
            " Last accuracy mAP@0.5 = 98.46 % \n",
            " 1521: 0.358030, 0.396916 avg loss, 0.001000 rate, 0.606989 seconds, 97344 images\n",
            "Loaded: 1.278087 seconds\n",
            "\n",
            " (next mAP calculation at 1600 iterations) \n",
            " Last accuracy mAP@0.5 = 98.46 % \n",
            " 1522: 0.367712, 0.393996 avg loss, 0.001000 rate, 0.619263 seconds, 97408 images\n",
            "Loaded: 1.356277 seconds\n",
            "\n",
            " (next mAP calculation at 1600 iterations) \n",
            " Last accuracy mAP@0.5 = 98.46 % \n",
            " 1523: 0.412198, 0.395816 avg loss, 0.001000 rate, 0.598771 seconds, 97472 images\n",
            "Loaded: 1.350448 seconds\n",
            "\n",
            " (next mAP calculation at 1600 iterations) \n",
            " Last accuracy mAP@0.5 = 98.46 % \n",
            " 1524: 0.398656, 0.396100 avg loss, 0.001000 rate, 0.600775 seconds, 97536 images\n",
            "Loaded: 1.352107 seconds\n",
            "\n",
            " (next mAP calculation at 1600 iterations) \n",
            " Last accuracy mAP@0.5 = 98.46 % \n",
            " 1525: 0.491306, 0.405621 avg loss, 0.001000 rate, 0.609409 seconds, 97600 images\n",
            "Loaded: 1.392856 seconds\n",
            "\n",
            " (next mAP calculation at 1600 iterations) \n",
            " Last accuracy mAP@0.5 = 98.46 % \n",
            " 1526: 0.431724, 0.408231 avg loss, 0.001000 rate, 0.729663 seconds, 97664 images\n",
            "Loaded: 1.180202 seconds\n",
            "\n",
            " (next mAP calculation at 1600 iterations) \n",
            " Last accuracy mAP@0.5 = 98.46 % \n",
            " 1527: 0.455590, 0.412967 avg loss, 0.001000 rate, 0.623733 seconds, 97728 images\n",
            "Loaded: 1.393362 seconds\n",
            "\n",
            " (next mAP calculation at 1600 iterations) \n",
            " Last accuracy mAP@0.5 = 98.46 % \n",
            " 1528: 0.366343, 0.408305 avg loss, 0.001000 rate, 0.586037 seconds, 97792 images\n",
            "Loaded: 1.366413 seconds\n",
            "\n",
            " (next mAP calculation at 1600 iterations) \n",
            " Last accuracy mAP@0.5 = 98.46 % \n",
            " 1529: 0.490321, 0.416506 avg loss, 0.001000 rate, 0.654933 seconds, 97856 images\n",
            "Loaded: 1.321753 seconds\n",
            "\n",
            " (next mAP calculation at 1600 iterations) \n",
            " Last accuracy mAP@0.5 = 98.46 % \n",
            " 1530: 0.328076, 0.407663 avg loss, 0.001000 rate, 0.633684 seconds, 97920 images\n",
            "Resizing\n",
            "512 x 512 \n",
            " try to allocate additional workspace_size = 4596.96 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.000040 seconds\n",
            "\n",
            " (next mAP calculation at 1600 iterations) \n",
            " Last accuracy mAP@0.5 = 98.46 % \n",
            " 1531: 0.376974, 0.404594 avg loss, 0.001000 rate, 0.697555 seconds, 97984 images\n",
            "Loaded: 1.404824 seconds\n",
            "\n",
            " (next mAP calculation at 1600 iterations) \n",
            " Last accuracy mAP@0.5 = 98.46 % \n",
            " 1532: 0.390931, 0.403228 avg loss, 0.001000 rate, 0.667635 seconds, 98048 images\n",
            "Loaded: 1.404185 seconds\n",
            "\n",
            " (next mAP calculation at 1600 iterations) \n",
            " Last accuracy mAP@0.5 = 98.46 % \n",
            " 1533: 0.443515, 0.407257 avg loss, 0.001000 rate, 0.646138 seconds, 98112 images\n",
            "Loaded: 1.464727 seconds\n",
            "\n",
            " (next mAP calculation at 1600 iterations) \n",
            " Last accuracy mAP@0.5 = 98.46 % \n",
            " 1534: 0.362357, 0.402767 avg loss, 0.001000 rate, 0.636575 seconds, 98176 images\n",
            "Loaded: 1.392629 seconds\n",
            "\n",
            " (next mAP calculation at 1600 iterations) \n",
            " Last accuracy mAP@0.5 = 98.46 % \n",
            " 1535: 0.427157, 0.405206 avg loss, 0.001000 rate, 0.647937 seconds, 98240 images\n",
            "Loaded: 1.560553 seconds\n",
            "\n",
            " (next mAP calculation at 1600 iterations) \n",
            " Last accuracy mAP@0.5 = 98.46 % \n",
            " 1536: 0.380111, 0.402696 avg loss, 0.001000 rate, 0.732399 seconds, 98304 images\n",
            "Loaded: 1.299047 seconds\n",
            "\n",
            " (next mAP calculation at 1600 iterations) \n",
            " Last accuracy mAP@0.5 = 98.46 % \n",
            " 1537: 0.430027, 0.405429 avg loss, 0.001000 rate, 0.693759 seconds, 98368 images\n",
            "Loaded: 1.436054 seconds\n",
            "\n",
            " (next mAP calculation at 1600 iterations) \n",
            " Last accuracy mAP@0.5 = 98.46 % \n",
            " 1538: 0.555400, 0.420426 avg loss, 0.001000 rate, 0.680446 seconds, 98432 images\n",
            "Loaded: 1.417799 seconds\n",
            "\n",
            " (next mAP calculation at 1600 iterations) \n",
            " Last accuracy mAP@0.5 = 98.46 % \n",
            " 1539: 0.477241, 0.426108 avg loss, 0.001000 rate, 0.639890 seconds, 98496 images\n",
            "Loaded: 1.409670 seconds\n",
            "\n",
            " (next mAP calculation at 1600 iterations) \n",
            " Last accuracy mAP@0.5 = 98.46 % \n",
            " 1540: 0.400754, 0.423572 avg loss, 0.001000 rate, 0.659128 seconds, 98560 images\n",
            "Resizing\n",
            "576 x 576 \n",
            " try to allocate additional workspace_size = 4605.87 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.000046 seconds\n",
            "\n",
            " (next mAP calculation at 1600 iterations) \n",
            " Last accuracy mAP@0.5 = 98.46 % \n",
            " 1541: 0.441177, 0.425333 avg loss, 0.001000 rate, 0.910369 seconds, 98624 images\n",
            "Loaded: 1.303735 seconds\n",
            "\n",
            " (next mAP calculation at 1600 iterations) \n",
            " Last accuracy mAP@0.5 = 98.46 % \n",
            " 1542: 0.321637, 0.414963 avg loss, 0.001000 rate, 1.011209 seconds, 98688 images\n",
            "Loaded: 1.137185 seconds\n",
            "\n",
            " (next mAP calculation at 1600 iterations) \n",
            " Last accuracy mAP@0.5 = 98.46 % \n",
            " 1543: 0.436124, 0.417079 avg loss, 0.001000 rate, 0.910681 seconds, 98752 images\n",
            "Loaded: 1.233228 seconds\n",
            "\n",
            " (next mAP calculation at 1600 iterations) \n",
            " Last accuracy mAP@0.5 = 98.46 % \n",
            " 1544: 0.445966, 0.419968 avg loss, 0.001000 rate, 0.900572 seconds, 98816 images\n",
            "Loaded: 1.279981 seconds\n",
            "\n",
            " (next mAP calculation at 1600 iterations) \n",
            " Last accuracy mAP@0.5 = 98.46 % \n",
            " 1545: 0.394136, 0.417385 avg loss, 0.001000 rate, 0.923233 seconds, 98880 images\n",
            "Loaded: 1.196728 seconds\n",
            "\n",
            " (next mAP calculation at 1600 iterations) \n",
            " Last accuracy mAP@0.5 = 98.46 % \n",
            " 1546: 0.356555, 0.411302 avg loss, 0.001000 rate, 0.882677 seconds, 98944 images\n",
            "Loaded: 1.282013 seconds\n",
            "\n",
            " (next mAP calculation at 1600 iterations) \n",
            " Last accuracy mAP@0.5 = 98.46 % \n",
            " 1547: 0.359907, 0.406162 avg loss, 0.001000 rate, 0.801052 seconds, 99008 images\n",
            "Loaded: 1.417858 seconds\n",
            "\n",
            " (next mAP calculation at 1600 iterations) \n",
            " Last accuracy mAP@0.5 = 98.46 % \n",
            " 1548: 0.439419, 0.409488 avg loss, 0.001000 rate, 0.939032 seconds, 99072 images\n",
            "Loaded: 1.174469 seconds\n",
            "\n",
            " (next mAP calculation at 1600 iterations) \n",
            " Last accuracy mAP@0.5 = 98.46 % \n",
            " 1549: 0.377871, 0.406326 avg loss, 0.001000 rate, 0.908075 seconds, 99136 images\n",
            "Loaded: 1.341928 seconds\n",
            "\n",
            " (next mAP calculation at 1600 iterations) \n",
            " Last accuracy mAP@0.5 = 98.46 % \n",
            " 1550: 0.574139, 0.423108 avg loss, 0.001000 rate, 0.922435 seconds, 99200 images\n",
            "Resizing\n",
            "576 x 576 \n",
            " try to allocate additional workspace_size = 4605.87 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 1.458591 seconds\n",
            "\n",
            " (next mAP calculation at 1600 iterations) \n",
            " Last accuracy mAP@0.5 = 98.46 % \n",
            " 1551: 0.441412, 0.424938 avg loss, 0.001000 rate, 1.026170 seconds, 99264 images\n",
            "Loaded: 1.114547 seconds\n",
            "\n",
            " (next mAP calculation at 1600 iterations) \n",
            " Last accuracy mAP@0.5 = 98.46 % \n",
            " 1552: 0.548598, 0.437304 avg loss, 0.001000 rate, 0.915532 seconds, 99328 images\n",
            "Loaded: 1.286124 seconds\n",
            "\n",
            " (next mAP calculation at 1600 iterations) \n",
            " Last accuracy mAP@0.5 = 98.46 % \n",
            " 1553: 0.394890, 0.433063 avg loss, 0.001000 rate, 0.831008 seconds, 99392 images\n",
            "Loaded: 1.362450 seconds\n",
            "\n",
            " (next mAP calculation at 1600 iterations) \n",
            " Last accuracy mAP@0.5 = 98.46 % \n",
            " 1554: 0.489809, 0.438737 avg loss, 0.001000 rate, 0.900531 seconds, 99456 images\n",
            "Loaded: 1.263596 seconds\n",
            "\n",
            " (next mAP calculation at 1600 iterations) \n",
            " Last accuracy mAP@0.5 = 98.46 % \n",
            " 1555: 0.490368, 0.443900 avg loss, 0.001000 rate, 0.858772 seconds, 99520 images\n",
            "Loaded: 1.326063 seconds\n",
            "\n",
            " (next mAP calculation at 1600 iterations) \n",
            " Last accuracy mAP@0.5 = 98.46 % \n",
            " 1556: 0.412553, 0.440766 avg loss, 0.001000 rate, 1.037455 seconds, 99584 images\n",
            "Loaded: 1.170672 seconds\n",
            "\n",
            " (next mAP calculation at 1600 iterations) \n",
            " Last accuracy mAP@0.5 = 98.46 % \n",
            " 1557: 0.250557, 0.421745 avg loss, 0.001000 rate, 0.874948 seconds, 99648 images\n",
            "Loaded: 1.266983 seconds\n",
            "\n",
            " (next mAP calculation at 1600 iterations) \n",
            " Last accuracy mAP@0.5 = 98.46 % \n",
            " 1558: 0.522819, 0.431852 avg loss, 0.001000 rate, 0.998480 seconds, 99712 images\n",
            "Loaded: 1.249092 seconds\n",
            "\n",
            " (next mAP calculation at 1600 iterations) \n",
            " Last accuracy mAP@0.5 = 98.46 % \n",
            " 1559: 0.350290, 0.423696 avg loss, 0.001000 rate, 0.962375 seconds, 99776 images\n",
            "Loaded: 1.300041 seconds\n",
            "\n",
            " (next mAP calculation at 1600 iterations) \n",
            " Last accuracy mAP@0.5 = 98.46 % \n",
            " 1560: 0.451419, 0.426468 avg loss, 0.001000 rate, 1.003211 seconds, 99840 images\n",
            "Resizing\n",
            "448 x 448 \n",
            " try to allocate additional workspace_size = 1233.65 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 1.309193 seconds\n",
            "\n",
            " (next mAP calculation at 1600 iterations) \n",
            " Last accuracy mAP@0.5 = 98.46 % \n",
            " 1561: 0.536129, 0.437434 avg loss, 0.001000 rate, 0.557396 seconds, 99904 images\n",
            "Loaded: 1.375788 seconds\n",
            "\n",
            " (next mAP calculation at 1600 iterations) \n",
            " Last accuracy mAP@0.5 = 98.46 % \n",
            " 1562: 0.415364, 0.435227 avg loss, 0.001000 rate, 0.540591 seconds, 99968 images\n",
            "Loaded: 1.366954 seconds\n",
            "\n",
            " (next mAP calculation at 1600 iterations) \n",
            " Last accuracy mAP@0.5 = 98.46 % \n",
            " 1563: 0.361995, 0.427904 avg loss, 0.001000 rate, 0.470849 seconds, 100032 images\n",
            "Loaded: 1.889546 seconds\n",
            "\n",
            " (next mAP calculation at 1600 iterations) \n",
            " Last accuracy mAP@0.5 = 98.46 % \n",
            " 1564: 0.440176, 0.429131 avg loss, 0.001000 rate, 0.755347 seconds, 100096 images\n",
            "Loaded: 1.494019 seconds\n",
            "\n",
            " (next mAP calculation at 1600 iterations) \n",
            " Last accuracy mAP@0.5 = 98.46 % \n",
            " 1565: 0.387147, 0.424933 avg loss, 0.001000 rate, 0.575920 seconds, 100160 images\n",
            "Loaded: 1.387345 seconds\n",
            "\n",
            " (next mAP calculation at 1600 iterations) \n",
            " Last accuracy mAP@0.5 = 98.46 % \n",
            " 1566: 0.338948, 0.416334 avg loss, 0.001000 rate, 0.459649 seconds, 100224 images\n",
            "Loaded: 1.427250 seconds\n",
            "\n",
            " (next mAP calculation at 1600 iterations) \n",
            " Last accuracy mAP@0.5 = 98.46 % \n",
            " 1567: 0.371335, 0.411835 avg loss, 0.001000 rate, 0.515238 seconds, 100288 images\n",
            "Loaded: 1.361168 seconds\n",
            "\n",
            " (next mAP calculation at 1600 iterations) \n",
            " Last accuracy mAP@0.5 = 98.46 % \n",
            " 1568: 0.358399, 0.406491 avg loss, 0.001000 rate, 0.505106 seconds, 100352 images\n",
            "Loaded: 1.344418 seconds\n",
            "\n",
            " (next mAP calculation at 1600 iterations) \n",
            " Last accuracy mAP@0.5 = 98.46 % \n",
            " 1569: 0.284921, 0.394334 avg loss, 0.001000 rate, 0.472382 seconds, 100416 images\n",
            "Loaded: 1.441897 seconds\n",
            "\n",
            " (next mAP calculation at 1600 iterations) \n",
            " Last accuracy mAP@0.5 = 98.46 % \n",
            " 1570: 0.493537, 0.404254 avg loss, 0.001000 rate, 0.541179 seconds, 100480 images\n",
            "Resizing\n",
            "608 x 608 \n",
            " try to allocate additional workspace_size = 4610.72 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.000039 seconds\n",
            "\n",
            " (next mAP calculation at 1600 iterations) \n",
            " Last accuracy mAP@0.5 = 98.46 % \n",
            " 1571: 0.600389, 0.423868 avg loss, 0.001000 rate, 1.012744 seconds, 100544 images\n",
            "Loaded: 1.210367 seconds\n",
            "\n",
            " (next mAP calculation at 1600 iterations) \n",
            " Last accuracy mAP@0.5 = 98.46 % \n",
            " 1572: 0.404076, 0.421889 avg loss, 0.001000 rate, 0.985361 seconds, 100608 images\n",
            "Loaded: 1.247165 seconds\n",
            "\n",
            " (next mAP calculation at 1600 iterations) \n",
            " Last accuracy mAP@0.5 = 98.46 % \n",
            " 1573: 0.495007, 0.429200 avg loss, 0.001000 rate, 1.098069 seconds, 100672 images\n",
            "Loaded: 1.109767 seconds\n",
            "\n",
            " (next mAP calculation at 1600 iterations) \n",
            " Last accuracy mAP@0.5 = 98.46 % \n",
            " 1574: 0.435256, 0.429806 avg loss, 0.001000 rate, 1.039699 seconds, 100736 images\n",
            "Loaded: 1.317006 seconds\n",
            "\n",
            " (next mAP calculation at 1600 iterations) \n",
            " Last accuracy mAP@0.5 = 98.46 % \n",
            " 1575: 0.336648, 0.420490 avg loss, 0.001000 rate, 1.096658 seconds, 100800 images\n",
            "Loaded: 1.209466 seconds\n",
            "\n",
            " (next mAP calculation at 1600 iterations) \n",
            " Last accuracy mAP@0.5 = 98.46 % \n",
            " 1576: 0.457180, 0.424159 avg loss, 0.001000 rate, 1.063210 seconds, 100864 images\n",
            "Loaded: 1.257740 seconds\n",
            "\n",
            " (next mAP calculation at 1600 iterations) \n",
            " Last accuracy mAP@0.5 = 98.46 % \n",
            " 1577: 0.519988, 0.433742 avg loss, 0.001000 rate, 0.983138 seconds, 100928 images\n",
            "Loaded: 1.263081 seconds\n",
            "\n",
            " (next mAP calculation at 1600 iterations) \n",
            " Last accuracy mAP@0.5 = 98.46 % \n",
            " 1578: 0.386418, 0.429010 avg loss, 0.001000 rate, 1.133740 seconds, 100992 images\n",
            "Loaded: 1.063924 seconds\n",
            "\n",
            " (next mAP calculation at 1600 iterations) \n",
            " Last accuracy mAP@0.5 = 98.46 % \n",
            " 1579: 0.449904, 0.431099 avg loss, 0.001000 rate, 0.994630 seconds, 101056 images\n",
            "Loaded: 1.288579 seconds\n",
            "\n",
            " (next mAP calculation at 1600 iterations) \n",
            " Last accuracy mAP@0.5 = 98.46 % \n",
            " 1580: 0.490453, 0.437035 avg loss, 0.001000 rate, 1.116940 seconds, 101120 images\n",
            "Resizing\n",
            "544 x 544 \n",
            " try to allocate additional workspace_size = 4601.28 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 1.456252 seconds\n",
            "\n",
            " (next mAP calculation at 1600 iterations) \n",
            " Last accuracy mAP@0.5 = 98.46 % \n",
            " 1581: 0.367703, 0.430101 avg loss, 0.001000 rate, 0.853361 seconds, 101184 images\n",
            "Loaded: 1.279093 seconds\n",
            "\n",
            " (next mAP calculation at 1600 iterations) \n",
            " Last accuracy mAP@0.5 = 98.46 % \n",
            " 1582: 0.328631, 0.419954 avg loss, 0.001000 rate, 0.903789 seconds, 101248 images\n",
            "Loaded: 1.236922 seconds\n",
            "\n",
            " (next mAP calculation at 1600 iterations) \n",
            " Last accuracy mAP@0.5 = 98.46 % \n",
            " 1583: 0.426208, 0.420580 avg loss, 0.001000 rate, 0.837354 seconds, 101312 images\n",
            "Loaded: 1.440561 seconds\n",
            "\n",
            " (next mAP calculation at 1600 iterations) \n",
            " Last accuracy mAP@0.5 = 98.46 % \n",
            " 1584: 0.394866, 0.418008 avg loss, 0.001000 rate, 0.850321 seconds, 101376 images\n",
            "Loaded: 1.395153 seconds\n",
            "\n",
            " (next mAP calculation at 1600 iterations) \n",
            " Last accuracy mAP@0.5 = 98.46 % \n",
            " 1585: 0.399893, 0.416197 avg loss, 0.001000 rate, 0.822889 seconds, 101440 images\n",
            "Loaded: 1.325289 seconds\n",
            "\n",
            " (next mAP calculation at 1600 iterations) \n",
            " Last accuracy mAP@0.5 = 98.46 % \n",
            " 1586: 0.393500, 0.413927 avg loss, 0.001000 rate, 0.798745 seconds, 101504 images\n",
            "Loaded: 1.401566 seconds\n",
            "\n",
            " (next mAP calculation at 1600 iterations) \n",
            " Last accuracy mAP@0.5 = 98.46 % \n",
            " 1587: 0.436067, 0.416141 avg loss, 0.001000 rate, 0.821387 seconds, 101568 images\n",
            "Loaded: 1.264813 seconds\n",
            "\n",
            " (next mAP calculation at 1600 iterations) \n",
            " Last accuracy mAP@0.5 = 98.46 % \n",
            " 1588: 0.439331, 0.418460 avg loss, 0.001000 rate, 0.892650 seconds, 101632 images\n",
            "Loaded: 1.196576 seconds\n",
            "\n",
            " (next mAP calculation at 1600 iterations) \n",
            " Last accuracy mAP@0.5 = 98.46 % \n",
            " 1589: 0.407111, 0.417325 avg loss, 0.001000 rate, 0.756098 seconds, 101696 images\n",
            "Loaded: 1.379439 seconds\n",
            "\n",
            " (next mAP calculation at 1600 iterations) \n",
            " Last accuracy mAP@0.5 = 98.46 % \n",
            " 1590: 0.452010, 0.420794 avg loss, 0.001000 rate, 0.733149 seconds, 101760 images\n",
            "Resizing\n",
            "576 x 576 \n",
            " try to allocate additional workspace_size = 4605.87 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.000040 seconds\n",
            "\n",
            " (next mAP calculation at 1600 iterations) \n",
            " Last accuracy mAP@0.5 = 98.46 % \n",
            " 1591: 0.476021, 0.426316 avg loss, 0.001000 rate, 0.870595 seconds, 101824 images\n",
            "Loaded: 1.313070 seconds\n",
            "\n",
            " (next mAP calculation at 1600 iterations) \n",
            " Last accuracy mAP@0.5 = 98.46 % \n",
            " 1592: 0.432555, 0.426940 avg loss, 0.001000 rate, 0.907778 seconds, 101888 images\n",
            "Loaded: 1.274883 seconds\n",
            "\n",
            " (next mAP calculation at 1600 iterations) \n",
            " Last accuracy mAP@0.5 = 98.46 % \n",
            " 1593: 0.379512, 0.422197 avg loss, 0.001000 rate, 0.860614 seconds, 101952 images\n",
            "Loaded: 1.364645 seconds\n",
            "\n",
            " (next mAP calculation at 1600 iterations) \n",
            " Last accuracy mAP@0.5 = 98.46 % \n",
            " 1594: 0.452696, 0.425247 avg loss, 0.001000 rate, 0.972954 seconds, 102016 images\n",
            "Loaded: 1.178059 seconds\n",
            "\n",
            " (next mAP calculation at 1600 iterations) \n",
            " Last accuracy mAP@0.5 = 98.46 % \n",
            " 1595: 0.448808, 0.427603 avg loss, 0.001000 rate, 0.859536 seconds, 102080 images\n",
            "Loaded: 1.383538 seconds\n",
            "\n",
            " (next mAP calculation at 1600 iterations) \n",
            " Last accuracy mAP@0.5 = 98.46 % \n",
            " 1596: 0.402063, 0.425049 avg loss, 0.001000 rate, 0.879607 seconds, 102144 images\n",
            "Loaded: 1.284077 seconds\n",
            "\n",
            " (next mAP calculation at 1600 iterations) \n",
            " Last accuracy mAP@0.5 = 98.46 % \n",
            " 1597: 0.403774, 0.422922 avg loss, 0.001000 rate, 0.919937 seconds, 102208 images\n",
            "Loaded: 1.289002 seconds\n",
            "\n",
            " (next mAP calculation at 1600 iterations) \n",
            " Last accuracy mAP@0.5 = 98.46 % \n",
            " 1598: 0.439064, 0.424536 avg loss, 0.001000 rate, 0.971749 seconds, 102272 images\n",
            "Loaded: 1.305779 seconds\n",
            "\n",
            " (next mAP calculation at 1600 iterations) \n",
            " Last accuracy mAP@0.5 = 98.46 % \n",
            " 1599: 0.457476, 0.427830 avg loss, 0.001000 rate, 0.832864 seconds, 102336 images\n",
            "Loaded: 1.392245 seconds\n",
            "\n",
            " (next mAP calculation at 1600 iterations) \n",
            " Last accuracy mAP@0.5 = 98.46 % \n",
            " 1600: 0.621088, 0.447156 avg loss, 0.001000 rate, 0.919437 seconds, 102400 images\n",
            "Resizing to initial size: 416 x 416 \n",
            " try to allocate additional workspace_size = 1302.33 MB \n",
            " CUDA allocate done! \n",
            "\n",
            " calculation mAP (mean average precision)...\n",
            "368\n",
            " detections_count = 580, unique_truth_count = 374  \n",
            "class_id = 0, name = banana, ap = 98.72%   \t (TP = 364, FP = 5) \n",
            "\n",
            " for thresh = 0.25, precision = 0.99, recall = 0.97, F1-score = 0.98 \n",
            " for thresh = 0.25, TP = 364, FP = 5, FN = 10, average IoU = 75.92 % \n",
            "\n",
            " IoU threshold = 50 %, used Area-Under-Curve for each unique Recall \n",
            " mean average precision (mAP@0.50) = 0.987219, or 98.72 % \n",
            "Total Detection Time: 14.000000 Seconds\n",
            "\n",
            "Set -points flag:\n",
            " `-points 101` for MS COCO \n",
            " `-points 11` for PascalVOC 2007 (uncomment `difficult` in voc.data) \n",
            " `-points 0` (AUC) for ImageNet, PascalVOC 2010-2012, your custom dataset\n",
            "\n",
            " mean_average_precision (mAP@0.5) = 0.987219 \n",
            "Saving weights to /content/gdrive/My Drive/darknet/backup/yolov3-tiny-280120_last.weights\n",
            "Resizing\n",
            "480 x 480 \n",
            " try to allocate additional workspace_size = 4592.89 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.000036 seconds\n",
            "\n",
            " (next mAP calculation at 1700 iterations) \n",
            " Last accuracy mAP@0.5 = 98.72 % \n",
            " 1601: 0.346149, 0.437055 avg loss, 0.001000 rate, 0.624685 seconds, 102464 images\n",
            "Loaded: 1.347937 seconds\n",
            "\n",
            " (next mAP calculation at 1700 iterations) \n",
            " Last accuracy mAP@0.5 = 98.72 % \n",
            " 1602: 0.381089, 0.431459 avg loss, 0.001000 rate, 0.602867 seconds, 102528 images\n",
            "Loaded: 1.381420 seconds\n",
            "\n",
            " (next mAP calculation at 1700 iterations) \n",
            " Last accuracy mAP@0.5 = 98.72 % \n",
            " 1603: 0.369805, 0.425293 avg loss, 0.001000 rate, 0.675162 seconds, 102592 images\n",
            "Loaded: 1.305883 seconds\n",
            "\n",
            " (next mAP calculation at 1700 iterations) \n",
            " Last accuracy mAP@0.5 = 98.72 % \n",
            " 1604: 0.415986, 0.424362 avg loss, 0.001000 rate, 0.593420 seconds, 102656 images\n",
            "Loaded: 1.398661 seconds\n",
            "\n",
            " (next mAP calculation at 1700 iterations) \n",
            " Last accuracy mAP@0.5 = 98.72 % \n",
            " 1605: 0.425307, 0.424457 avg loss, 0.001000 rate, 0.655029 seconds, 102720 images\n",
            "Loaded: 1.434616 seconds\n",
            "\n",
            " (next mAP calculation at 1700 iterations) \n",
            " Last accuracy mAP@0.5 = 98.72 % \n",
            " 1606: 0.458167, 0.427828 avg loss, 0.001000 rate, 0.668753 seconds, 102784 images\n",
            "Loaded: 1.396423 seconds\n",
            "\n",
            " (next mAP calculation at 1700 iterations) \n",
            " Last accuracy mAP@0.5 = 98.72 % \n",
            " 1607: 0.370261, 0.422071 avg loss, 0.001000 rate, 0.589238 seconds, 102848 images\n",
            "Loaded: 1.421985 seconds\n",
            "\n",
            " (next mAP calculation at 1700 iterations) \n",
            " Last accuracy mAP@0.5 = 98.72 % \n",
            " 1608: 0.422485, 0.422112 avg loss, 0.001000 rate, 0.582283 seconds, 102912 images\n",
            "Loaded: 1.402324 seconds\n",
            "\n",
            " (next mAP calculation at 1700 iterations) \n",
            " Last accuracy mAP@0.5 = 98.72 % \n",
            " 1609: 0.308694, 0.410771 avg loss, 0.001000 rate, 0.700150 seconds, 102976 images\n",
            "Loaded: 1.315579 seconds\n",
            "\n",
            " (next mAP calculation at 1700 iterations) \n",
            " Last accuracy mAP@0.5 = 98.72 % \n",
            " 1610: 0.436648, 0.413358 avg loss, 0.001000 rate, 0.704445 seconds, 103040 images\n",
            "Resizing\n",
            "352 x 352 \n",
            " try to allocate additional workspace_size = 1264.58 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 1.409572 seconds\n",
            "\n",
            " (next mAP calculation at 1700 iterations) \n",
            " Last accuracy mAP@0.5 = 98.72 % \n",
            " 1611: 0.390880, 0.411111 avg loss, 0.001000 rate, 0.285352 seconds, 103104 images\n",
            "Loaded: 1.481577 seconds\n",
            "\n",
            " (next mAP calculation at 1700 iterations) \n",
            " Last accuracy mAP@0.5 = 98.72 % \n",
            " 1612: 0.498070, 0.419806 avg loss, 0.001000 rate, 0.299984 seconds, 103168 images\n",
            "Loaded: 1.507445 seconds\n",
            "\n",
            " (next mAP calculation at 1700 iterations) \n",
            " Last accuracy mAP@0.5 = 98.72 % \n",
            " 1613: 0.502281, 0.428054 avg loss, 0.001000 rate, 0.313795 seconds, 103232 images\n",
            "Loaded: 1.442712 seconds\n",
            "\n",
            " (next mAP calculation at 1700 iterations) \n",
            " Last accuracy mAP@0.5 = 98.72 % \n",
            " 1614: 0.456091, 0.430858 avg loss, 0.001000 rate, 0.300692 seconds, 103296 images\n",
            "Loaded: 1.458316 seconds\n",
            "\n",
            " (next mAP calculation at 1700 iterations) \n",
            " Last accuracy mAP@0.5 = 98.72 % \n",
            " 1615: 0.594958, 0.447268 avg loss, 0.001000 rate, 0.293320 seconds, 103360 images\n",
            "Loaded: 1.460005 seconds\n",
            "\n",
            " (next mAP calculation at 1700 iterations) \n",
            " Last accuracy mAP@0.5 = 98.72 % \n",
            " 1616: 0.436029, 0.446144 avg loss, 0.001000 rate, 0.304748 seconds, 103424 images\n",
            "Loaded: 1.473602 seconds\n",
            "\n",
            " (next mAP calculation at 1700 iterations) \n",
            " Last accuracy mAP@0.5 = 98.72 % \n",
            " 1617: 0.447210, 0.446250 avg loss, 0.001000 rate, 0.332887 seconds, 103488 images\n",
            "Loaded: 1.434092 seconds\n",
            "\n",
            " (next mAP calculation at 1700 iterations) \n",
            " Last accuracy mAP@0.5 = 98.72 % \n",
            " 1618: 0.339429, 0.435568 avg loss, 0.001000 rate, 0.310147 seconds, 103552 images\n",
            "Loaded: 1.467325 seconds\n",
            "\n",
            " (next mAP calculation at 1700 iterations) \n",
            " Last accuracy mAP@0.5 = 98.72 % \n",
            " 1619: 0.350754, 0.427087 avg loss, 0.001000 rate, 0.294961 seconds, 103616 images\n",
            "Loaded: 1.467082 seconds\n",
            "\n",
            " (next mAP calculation at 1700 iterations) \n",
            " Last accuracy mAP@0.5 = 98.72 % \n",
            " 1620: 0.447411, 0.429119 avg loss, 0.001000 rate, 0.289230 seconds, 103680 images\n",
            "Resizing\n",
            "416 x 416 \n",
            " try to allocate additional workspace_size = 1302.33 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.028459 seconds\n",
            "\n",
            " (next mAP calculation at 1700 iterations) \n",
            " Last accuracy mAP@0.5 = 98.72 % \n",
            " 1621: 0.486437, 0.434851 avg loss, 0.001000 rate, 0.444385 seconds, 103744 images\n",
            "Loaded: 1.411916 seconds\n",
            "\n",
            " (next mAP calculation at 1700 iterations) \n",
            " Last accuracy mAP@0.5 = 98.72 % \n",
            " 1622: 0.392321, 0.430598 avg loss, 0.001000 rate, 0.433834 seconds, 103808 images\n",
            "Loaded: 1.406720 seconds\n",
            "\n",
            " (next mAP calculation at 1700 iterations) \n",
            " Last accuracy mAP@0.5 = 98.72 % \n",
            " 1623: 0.424484, 0.429987 avg loss, 0.001000 rate, 0.438906 seconds, 103872 images\n",
            "Loaded: 1.441958 seconds\n",
            "\n",
            " (next mAP calculation at 1700 iterations) \n",
            " Last accuracy mAP@0.5 = 98.72 % \n",
            " 1624: 0.418962, 0.428884 avg loss, 0.001000 rate, 0.438559 seconds, 103936 images\n",
            "Loaded: 1.460795 seconds\n",
            "\n",
            " (next mAP calculation at 1700 iterations) \n",
            " Last accuracy mAP@0.5 = 98.72 % \n",
            " 1625: 0.531919, 0.439188 avg loss, 0.001000 rate, 0.410765 seconds, 104000 images\n",
            "Loaded: 1.516405 seconds\n",
            "\n",
            " (next mAP calculation at 1700 iterations) \n",
            " Last accuracy mAP@0.5 = 98.72 % \n",
            " 1626: 0.411075, 0.436376 avg loss, 0.001000 rate, 0.465316 seconds, 104064 images\n",
            "Loaded: 1.408571 seconds\n",
            "\n",
            " (next mAP calculation at 1700 iterations) \n",
            " Last accuracy mAP@0.5 = 98.72 % \n",
            " 1627: 0.498478, 0.442587 avg loss, 0.001000 rate, 0.422171 seconds, 104128 images\n",
            "Loaded: 1.570711 seconds\n",
            "\n",
            " (next mAP calculation at 1700 iterations) \n",
            " Last accuracy mAP@0.5 = 98.72 % \n",
            " 1628: 0.502164, 0.448544 avg loss, 0.001000 rate, 0.427638 seconds, 104192 images\n",
            "Loaded: 1.432385 seconds\n",
            "\n",
            " (next mAP calculation at 1700 iterations) \n",
            " Last accuracy mAP@0.5 = 98.72 % \n",
            " 1629: 0.382873, 0.441977 avg loss, 0.001000 rate, 0.486437 seconds, 104256 images\n",
            "Loaded: 1.501739 seconds\n",
            "\n",
            " (next mAP calculation at 1700 iterations) \n",
            " Last accuracy mAP@0.5 = 98.72 % \n",
            " 1630: 0.351839, 0.432963 avg loss, 0.001000 rate, 0.469719 seconds, 104320 images\n",
            "Resizing\n",
            "544 x 544 \n",
            " try to allocate additional workspace_size = 4601.28 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.000041 seconds\n",
            "\n",
            " (next mAP calculation at 1700 iterations) \n",
            " Last accuracy mAP@0.5 = 98.72 % \n",
            " 1631: 0.411057, 0.430773 avg loss, 0.001000 rate, 0.822533 seconds, 104384 images\n",
            "Loaded: 1.354894 seconds\n",
            "\n",
            " (next mAP calculation at 1700 iterations) \n",
            " Last accuracy mAP@0.5 = 98.72 % \n",
            " 1632: 0.472905, 0.434986 avg loss, 0.001000 rate, 0.734260 seconds, 104448 images\n",
            "Loaded: 1.699655 seconds\n",
            "\n",
            " (next mAP calculation at 1700 iterations) \n",
            " Last accuracy mAP@0.5 = 98.72 % \n",
            " 1633: 0.530906, 0.444578 avg loss, 0.001000 rate, 0.831425 seconds, 104512 images\n",
            "Loaded: 1.326936 seconds\n",
            "\n",
            " (next mAP calculation at 1700 iterations) \n",
            " Last accuracy mAP@0.5 = 98.72 % \n",
            " 1634: 0.382273, 0.438347 avg loss, 0.001000 rate, 0.774630 seconds, 104576 images\n",
            "Loaded: 1.382334 seconds\n",
            "\n",
            " (next mAP calculation at 1700 iterations) \n",
            " Last accuracy mAP@0.5 = 98.72 % \n",
            " 1635: 0.471241, 0.441637 avg loss, 0.001000 rate, 0.874384 seconds, 104640 images\n",
            "Loaded: 1.252945 seconds\n",
            "\n",
            " (next mAP calculation at 1700 iterations) \n",
            " Last accuracy mAP@0.5 = 98.72 % \n",
            " 1636: 0.404919, 0.437965 avg loss, 0.001000 rate, 0.877486 seconds, 104704 images\n",
            "Loaded: 1.281194 seconds\n",
            "\n",
            " (next mAP calculation at 1700 iterations) \n",
            " Last accuracy mAP@0.5 = 98.72 % \n",
            " 1637: 0.419898, 0.436158 avg loss, 0.001000 rate, 0.813341 seconds, 104768 images\n",
            "Loaded: 1.322443 seconds\n",
            "\n",
            " (next mAP calculation at 1700 iterations) \n",
            " Last accuracy mAP@0.5 = 98.72 % \n",
            " 1638: 0.466089, 0.439151 avg loss, 0.001000 rate, 0.759969 seconds, 104832 images\n",
            "Loaded: 1.300528 seconds\n",
            "\n",
            " (next mAP calculation at 1700 iterations) \n",
            " Last accuracy mAP@0.5 = 98.72 % \n",
            " 1639: 0.336490, 0.428885 avg loss, 0.001000 rate, 0.812290 seconds, 104896 images\n",
            "Loaded: 1.345557 seconds\n",
            "\n",
            " (next mAP calculation at 1700 iterations) \n",
            " Last accuracy mAP@0.5 = 98.72 % \n",
            " 1640: 0.438802, 0.429877 avg loss, 0.001000 rate, 0.819041 seconds, 104960 images\n",
            "Resizing\n",
            "512 x 512 \n",
            " try to allocate additional workspace_size = 4596.96 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 1.288822 seconds\n",
            "\n",
            " (next mAP calculation at 1700 iterations) \n",
            " Last accuracy mAP@0.5 = 98.72 % \n",
            " 1641: 0.334689, 0.420358 avg loss, 0.001000 rate, 0.755487 seconds, 105024 images\n",
            "Loaded: 1.343588 seconds\n",
            "\n",
            " (next mAP calculation at 1700 iterations) \n",
            " Last accuracy mAP@0.5 = 98.72 % \n",
            " 1642: 0.388408, 0.417163 avg loss, 0.001000 rate, 0.747894 seconds, 105088 images\n",
            "Loaded: 1.407733 seconds\n",
            "\n",
            " (next mAP calculation at 1700 iterations) \n",
            " Last accuracy mAP@0.5 = 98.72 % \n",
            " 1643: 0.404826, 0.415929 avg loss, 0.001000 rate, 0.709287 seconds, 105152 images\n",
            "Loaded: 1.366230 seconds\n",
            "\n",
            " (next mAP calculation at 1700 iterations) \n",
            " Last accuracy mAP@0.5 = 98.72 % \n",
            " 1644: 0.463343, 0.420671 avg loss, 0.001000 rate, 0.687918 seconds, 105216 images\n",
            "Loaded: 1.316091 seconds\n",
            "\n",
            " (next mAP calculation at 1700 iterations) \n",
            " Last accuracy mAP@0.5 = 98.72 % \n",
            " 1645: 0.395259, 0.418130 avg loss, 0.001000 rate, 0.722002 seconds, 105280 images\n",
            "Loaded: 1.330386 seconds\n",
            "\n",
            " (next mAP calculation at 1700 iterations) \n",
            " Last accuracy mAP@0.5 = 98.72 % \n",
            " 1646: 0.411565, 0.417473 avg loss, 0.001000 rate, 0.655040 seconds, 105344 images\n",
            "Loaded: 1.369790 seconds\n",
            "\n",
            " (next mAP calculation at 1700 iterations) \n",
            " Last accuracy mAP@0.5 = 98.72 % \n",
            " 1647: 0.334007, 0.409127 avg loss, 0.001000 rate, 0.743961 seconds, 105408 images\n",
            "Loaded: 1.284628 seconds\n",
            "\n",
            " (next mAP calculation at 1700 iterations) \n",
            " Last accuracy mAP@0.5 = 98.72 % \n",
            " 1648: 0.385744, 0.406788 avg loss, 0.001000 rate, 0.639299 seconds, 105472 images\n",
            "Loaded: 1.398376 seconds\n",
            "\n",
            " (next mAP calculation at 1700 iterations) \n",
            " Last accuracy mAP@0.5 = 98.72 % \n",
            " 1649: 0.374726, 0.403582 avg loss, 0.001000 rate, 0.738803 seconds, 105536 images\n",
            "Loaded: 1.307719 seconds\n",
            "\n",
            " (next mAP calculation at 1700 iterations) \n",
            " Last accuracy mAP@0.5 = 98.72 % \n",
            " 1650: 0.395860, 0.402810 avg loss, 0.001000 rate, 0.639506 seconds, 105600 images\n",
            "Resizing\n",
            "512 x 512 \n",
            " try to allocate additional workspace_size = 4596.96 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 1.520475 seconds\n",
            "\n",
            " (next mAP calculation at 1700 iterations) \n",
            " Last accuracy mAP@0.5 = 98.72 % \n",
            " 1651: 0.351046, 0.397633 avg loss, 0.001000 rate, 0.817962 seconds, 105664 images\n",
            "Loaded: 1.278212 seconds\n",
            "\n",
            " (next mAP calculation at 1700 iterations) \n",
            " Last accuracy mAP@0.5 = 98.72 % \n",
            " 1652: 0.356119, 0.393482 avg loss, 0.001000 rate, 0.771725 seconds, 105728 images\n",
            "Loaded: 1.329386 seconds\n",
            "\n",
            " (next mAP calculation at 1700 iterations) \n",
            " Last accuracy mAP@0.5 = 98.72 % \n",
            " 1653: 0.415812, 0.395715 avg loss, 0.001000 rate, 0.707549 seconds, 105792 images\n",
            "Loaded: 1.522193 seconds\n",
            "\n",
            " (next mAP calculation at 1700 iterations) \n",
            " Last accuracy mAP@0.5 = 98.72 % \n",
            " 1654: 0.421311, 0.398275 avg loss, 0.001000 rate, 0.755375 seconds, 105856 images\n",
            "Loaded: 1.296965 seconds\n",
            "\n",
            " (next mAP calculation at 1700 iterations) \n",
            " Last accuracy mAP@0.5 = 98.72 % \n",
            " 1655: 0.392010, 0.397648 avg loss, 0.001000 rate, 0.752594 seconds, 105920 images\n",
            "Loaded: 1.314001 seconds\n",
            "\n",
            " (next mAP calculation at 1700 iterations) \n",
            " Last accuracy mAP@0.5 = 98.72 % \n",
            " 1656: 0.341749, 0.392058 avg loss, 0.001000 rate, 0.628173 seconds, 105984 images\n",
            "Loaded: 1.396360 seconds\n",
            "\n",
            " (next mAP calculation at 1700 iterations) \n",
            " Last accuracy mAP@0.5 = 98.72 % \n",
            " 1657: 0.390130, 0.391865 avg loss, 0.001000 rate, 0.638029 seconds, 106048 images\n",
            "Loaded: 1.386818 seconds\n",
            "\n",
            " (next mAP calculation at 1700 iterations) \n",
            " Last accuracy mAP@0.5 = 98.72 % \n",
            " 1658: 0.317364, 0.384415 avg loss, 0.001000 rate, 0.670402 seconds, 106112 images\n",
            "Loaded: 1.437383 seconds\n",
            "\n",
            " (next mAP calculation at 1700 iterations) \n",
            " Last accuracy mAP@0.5 = 98.72 % \n",
            " 1659: 0.321244, 0.378098 avg loss, 0.001000 rate, 0.651037 seconds, 106176 images\n",
            "Loaded: 1.493314 seconds\n",
            "\n",
            " (next mAP calculation at 1700 iterations) \n",
            " Last accuracy mAP@0.5 = 98.72 % \n",
            " 1660: 0.475994, 0.387888 avg loss, 0.001000 rate, 0.659830 seconds, 106240 images\n",
            "Resizing\n",
            "352 x 352 \n",
            " try to allocate additional workspace_size = 1264.58 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.038012 seconds\n",
            "\n",
            " (next mAP calculation at 1700 iterations) \n",
            " Last accuracy mAP@0.5 = 98.72 % \n",
            " 1661: 0.332669, 0.382366 avg loss, 0.001000 rate, 0.359673 seconds, 106304 images\n",
            "Loaded: 1.387733 seconds\n",
            "\n",
            " (next mAP calculation at 1700 iterations) \n",
            " Last accuracy mAP@0.5 = 98.72 % \n",
            " 1662: 0.295201, 0.373649 avg loss, 0.001000 rate, 0.316766 seconds, 106368 images\n",
            "Loaded: 1.436429 seconds\n",
            "\n",
            " (next mAP calculation at 1700 iterations) \n",
            " Last accuracy mAP@0.5 = 98.72 % \n",
            " 1663: 0.401357, 0.376420 avg loss, 0.001000 rate, 0.289279 seconds, 106432 images\n",
            "Loaded: 1.491059 seconds\n",
            "\n",
            " (next mAP calculation at 1700 iterations) \n",
            " Last accuracy mAP@0.5 = 98.72 % \n",
            " 1664: 0.510418, 0.389820 avg loss, 0.001000 rate, 0.280649 seconds, 106496 images\n",
            "Loaded: 1.528923 seconds\n",
            "\n",
            " (next mAP calculation at 1700 iterations) \n",
            " Last accuracy mAP@0.5 = 98.72 % \n",
            " 1665: 0.435781, 0.394416 avg loss, 0.001000 rate, 0.305395 seconds, 106560 images\n",
            "Loaded: 1.489717 seconds\n",
            "\n",
            " (next mAP calculation at 1700 iterations) \n",
            " Last accuracy mAP@0.5 = 98.72 % \n",
            " 1666: 0.389819, 0.393956 avg loss, 0.001000 rate, 0.278570 seconds, 106624 images\n",
            "Loaded: 1.437535 seconds\n",
            "\n",
            " (next mAP calculation at 1700 iterations) \n",
            " Last accuracy mAP@0.5 = 98.72 % \n",
            " 1667: 0.403954, 0.394956 avg loss, 0.001000 rate, 0.297974 seconds, 106688 images\n",
            "Loaded: 1.481828 seconds\n",
            "\n",
            " (next mAP calculation at 1700 iterations) \n",
            " Last accuracy mAP@0.5 = 98.72 % \n",
            " 1668: 0.432724, 0.398733 avg loss, 0.001000 rate, 0.281492 seconds, 106752 images\n",
            "Loaded: 1.529101 seconds\n",
            "\n",
            " (next mAP calculation at 1700 iterations) \n",
            " Last accuracy mAP@0.5 = 98.72 % \n",
            " 1669: 0.329299, 0.391790 avg loss, 0.001000 rate, 0.289454 seconds, 106816 images\n",
            "Loaded: 1.407178 seconds\n",
            "\n",
            " (next mAP calculation at 1700 iterations) \n",
            " Last accuracy mAP@0.5 = 98.72 % \n",
            " 1670: 0.366074, 0.389218 avg loss, 0.001000 rate, 0.283550 seconds, 106880 images\n",
            "Resizing\n",
            "384 x 384 \n",
            " try to allocate additional workspace_size = 1302.33 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.000036 seconds\n",
            "\n",
            " (next mAP calculation at 1700 iterations) \n",
            " Last accuracy mAP@0.5 = 98.72 % \n",
            " 1671: 0.325552, 0.382851 avg loss, 0.001000 rate, 0.409975 seconds, 106944 images\n",
            "Loaded: 1.435492 seconds\n",
            "\n",
            " (next mAP calculation at 1700 iterations) \n",
            " Last accuracy mAP@0.5 = 98.72 % \n",
            " 1672: 0.389146, 0.383481 avg loss, 0.001000 rate, 0.403341 seconds, 107008 images\n",
            "Loaded: 1.442091 seconds\n",
            "\n",
            " (next mAP calculation at 1700 iterations) \n",
            " Last accuracy mAP@0.5 = 98.72 % \n",
            " 1673: 0.389627, 0.384096 avg loss, 0.001000 rate, 0.340826 seconds, 107072 images\n",
            "Loaded: 1.507361 seconds\n",
            "\n",
            " (next mAP calculation at 1700 iterations) \n",
            " Last accuracy mAP@0.5 = 98.72 % \n",
            " 1674: 0.268258, 0.372512 avg loss, 0.001000 rate, 0.376872 seconds, 107136 images\n",
            "Loaded: 1.429049 seconds\n",
            "\n",
            " (next mAP calculation at 1700 iterations) \n",
            " Last accuracy mAP@0.5 = 98.72 % \n",
            " 1675: 0.360372, 0.371298 avg loss, 0.001000 rate, 0.349122 seconds, 107200 images\n",
            "Loaded: 1.489610 seconds\n",
            "\n",
            " (next mAP calculation at 1700 iterations) \n",
            " Last accuracy mAP@0.5 = 98.72 % \n",
            " 1676: 0.433386, 0.377507 avg loss, 0.001000 rate, 0.366490 seconds, 107264 images\n",
            "Loaded: 1.491888 seconds\n",
            "\n",
            " (next mAP calculation at 1700 iterations) \n",
            " Last accuracy mAP@0.5 = 98.72 % \n",
            " 1677: 0.395253, 0.379281 avg loss, 0.001000 rate, 0.379009 seconds, 107328 images\n",
            "Loaded: 1.515980 seconds\n",
            "\n",
            " (next mAP calculation at 1700 iterations) \n",
            " Last accuracy mAP@0.5 = 98.72 % \n",
            " 1678: 0.356042, 0.376957 avg loss, 0.001000 rate, 0.377522 seconds, 107392 images\n",
            "Loaded: 1.575459 seconds\n",
            "\n",
            " (next mAP calculation at 1700 iterations) \n",
            " Last accuracy mAP@0.5 = 98.72 % \n",
            " 1679: 0.391847, 0.378446 avg loss, 0.001000 rate, 0.371613 seconds, 107456 images\n",
            "Loaded: 1.503034 seconds\n",
            "\n",
            " (next mAP calculation at 1700 iterations) \n",
            " Last accuracy mAP@0.5 = 98.72 % \n",
            " 1680: 0.345809, 0.375183 avg loss, 0.001000 rate, 0.399121 seconds, 107520 images\n",
            "Resizing\n",
            "608 x 608 \n",
            " try to allocate additional workspace_size = 4610.72 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.000041 seconds\n",
            "\n",
            " (next mAP calculation at 1700 iterations) \n",
            " Last accuracy mAP@0.5 = 98.72 % \n",
            " 1681: 0.433617, 0.381026 avg loss, 0.001000 rate, 1.415025 seconds, 107584 images\n",
            "Loaded: 1.016159 seconds\n",
            "\n",
            " (next mAP calculation at 1700 iterations) \n",
            " Last accuracy mAP@0.5 = 98.72 % \n",
            " 1682: 0.455346, 0.388458 avg loss, 0.001000 rate, 1.021767 seconds, 107648 images\n",
            "Loaded: 1.230581 seconds\n",
            "\n",
            " (next mAP calculation at 1700 iterations) \n",
            " Last accuracy mAP@0.5 = 98.72 % \n",
            " 1683: 0.543848, 0.403997 avg loss, 0.001000 rate, 1.117284 seconds, 107712 images\n",
            "Loaded: 1.174213 seconds\n",
            "\n",
            " (next mAP calculation at 1700 iterations) \n",
            " Last accuracy mAP@0.5 = 98.72 % \n",
            " 1684: 0.317959, 0.395393 avg loss, 0.001000 rate, 1.135573 seconds, 107776 images\n",
            "Loaded: 1.253182 seconds\n",
            "\n",
            " (next mAP calculation at 1700 iterations) \n",
            " Last accuracy mAP@0.5 = 98.72 % \n",
            " 1685: 0.526488, 0.408503 avg loss, 0.001000 rate, 1.002557 seconds, 107840 images\n",
            "Loaded: 1.322025 seconds\n",
            "\n",
            " (next mAP calculation at 1700 iterations) \n",
            " Last accuracy mAP@0.5 = 98.72 % \n",
            " 1686: 0.338561, 0.401508 avg loss, 0.001000 rate, 1.126376 seconds, 107904 images\n",
            "Loaded: 1.162826 seconds\n",
            "\n",
            " (next mAP calculation at 1700 iterations) \n",
            " Last accuracy mAP@0.5 = 98.72 % \n",
            " 1687: 0.448313, 0.406189 avg loss, 0.001000 rate, 1.130825 seconds, 107968 images\n",
            "Loaded: 1.151150 seconds\n",
            "\n",
            " (next mAP calculation at 1700 iterations) \n",
            " Last accuracy mAP@0.5 = 98.72 % \n",
            " 1688: 0.482068, 0.413777 avg loss, 0.001000 rate, 1.023204 seconds, 108032 images\n",
            "Loaded: 1.263064 seconds\n",
            "\n",
            " (next mAP calculation at 1700 iterations) \n",
            " Last accuracy mAP@0.5 = 98.72 % \n",
            " 1689: 0.385721, 0.410971 avg loss, 0.001000 rate, 1.110574 seconds, 108096 images\n",
            "Loaded: 1.257906 seconds\n",
            "\n",
            " (next mAP calculation at 1700 iterations) \n",
            " Last accuracy mAP@0.5 = 98.72 % \n",
            " 1690: 0.332378, 0.403112 avg loss, 0.001000 rate, 1.063959 seconds, 108160 images\n",
            "Resizing\n",
            "608 x 608 \n",
            " try to allocate additional workspace_size = 4610.72 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 1.392817 seconds\n",
            "\n",
            " (next mAP calculation at 1700 iterations) \n",
            " Last accuracy mAP@0.5 = 98.72 % \n",
            " 1691: 0.308776, 0.393678 avg loss, 0.001000 rate, 1.120456 seconds, 108224 images\n",
            "Loaded: 1.144367 seconds\n",
            "\n",
            " (next mAP calculation at 1700 iterations) \n",
            " Last accuracy mAP@0.5 = 98.72 % \n",
            " 1692: 0.417572, 0.396068 avg loss, 0.001000 rate, 1.118142 seconds, 108288 images\n",
            "Loaded: 1.163285 seconds\n",
            "\n",
            " (next mAP calculation at 1700 iterations) \n",
            " Last accuracy mAP@0.5 = 98.72 % \n",
            " 1693: 0.356128, 0.392074 avg loss, 0.001000 rate, 1.164623 seconds, 108352 images\n",
            "Loaded: 1.248883 seconds\n",
            "\n",
            " (next mAP calculation at 1700 iterations) \n",
            " Last accuracy mAP@0.5 = 98.72 % \n",
            " 1694: 0.485987, 0.401465 avg loss, 0.001000 rate, 1.024781 seconds, 108416 images\n",
            "Loaded: 1.267976 seconds\n",
            "\n",
            " (next mAP calculation at 1700 iterations) \n",
            " Last accuracy mAP@0.5 = 98.72 % \n",
            " 1695: 0.290582, 0.390377 avg loss, 0.001000 rate, 1.084580 seconds, 108480 images\n",
            "Loaded: 1.171809 seconds\n",
            "\n",
            " (next mAP calculation at 1700 iterations) \n",
            " Last accuracy mAP@0.5 = 98.72 % \n",
            " 1696: 0.317576, 0.383097 avg loss, 0.001000 rate, 0.981634 seconds, 108544 images\n",
            "Loaded: 1.309964 seconds\n",
            "\n",
            " (next mAP calculation at 1700 iterations) \n",
            " Last accuracy mAP@0.5 = 98.72 % \n",
            " 1697: 0.357629, 0.380550 avg loss, 0.001000 rate, 0.993326 seconds, 108608 images\n",
            "Loaded: 1.311763 seconds\n",
            "\n",
            " (next mAP calculation at 1700 iterations) \n",
            " Last accuracy mAP@0.5 = 98.72 % \n",
            " 1698: 0.441205, 0.386615 avg loss, 0.001000 rate, 1.028455 seconds, 108672 images\n",
            "Loaded: 1.307902 seconds\n",
            "\n",
            " (next mAP calculation at 1700 iterations) \n",
            " Last accuracy mAP@0.5 = 98.72 % \n",
            " 1699: 0.370714, 0.385025 avg loss, 0.001000 rate, 1.104711 seconds, 108736 images\n",
            "Loaded: 1.220674 seconds\n",
            "\n",
            " (next mAP calculation at 1700 iterations) \n",
            " Last accuracy mAP@0.5 = 98.72 % \n",
            " 1700: 0.397420, 0.386265 avg loss, 0.001000 rate, 0.987467 seconds, 108800 images\n",
            "Resizing to initial size: 416 x 416 \n",
            " try to allocate additional workspace_size = 1302.33 MB \n",
            " CUDA allocate done! \n",
            "\n",
            " calculation mAP (mean average precision)...\n",
            "368\n",
            " detections_count = 555, unique_truth_count = 374  \n",
            "class_id = 0, name = banana, ap = 98.30%   \t (TP = 362, FP = 13) \n",
            "\n",
            " for thresh = 0.25, precision = 0.97, recall = 0.97, F1-score = 0.97 \n",
            " for thresh = 0.25, TP = 362, FP = 13, FN = 12, average IoU = 73.64 % \n",
            "\n",
            " IoU threshold = 50 %, used Area-Under-Curve for each unique Recall \n",
            " mean average precision (mAP@0.50) = 0.983021, or 98.30 % \n",
            "Total Detection Time: 14.000000 Seconds\n",
            "\n",
            "Set -points flag:\n",
            " `-points 101` for MS COCO \n",
            " `-points 11` for PascalVOC 2007 (uncomment `difficult` in voc.data) \n",
            " `-points 0` (AUC) for ImageNet, PascalVOC 2010-2012, your custom dataset\n",
            "\n",
            " mean_average_precision (mAP@0.5) = 0.983021 \n",
            "Saving weights to /content/gdrive/My Drive/darknet/backup/yolov3-tiny-280120_last.weights\n",
            "Resizing\n",
            "320 x 320 \n",
            " try to allocate additional workspace_size = 868.75 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 1.550421 seconds\n",
            "\n",
            " (next mAP calculation at 1800 iterations) \n",
            " Last accuracy mAP@0.5 = 98.30 % \n",
            " 1701: 0.464591, 0.394097 avg loss, 0.001000 rate, 0.320362 seconds, 108864 images\n",
            "Loaded: 1.403518 seconds\n",
            "\n",
            " (next mAP calculation at 1800 iterations) \n",
            " Last accuracy mAP@0.5 = 98.30 % \n",
            " 1702: 0.452509, 0.399938 avg loss, 0.001000 rate, 0.255842 seconds, 108928 images\n",
            "Loaded: 1.426818 seconds\n",
            "\n",
            " (next mAP calculation at 1800 iterations) \n",
            " Last accuracy mAP@0.5 = 98.30 % \n",
            " 1703: 0.489538, 0.408898 avg loss, 0.001000 rate, 0.255767 seconds, 108992 images\n",
            "Loaded: 1.498496 seconds\n",
            "\n",
            " (next mAP calculation at 1800 iterations) \n",
            " Last accuracy mAP@0.5 = 98.30 % \n",
            " 1704: 0.374452, 0.405454 avg loss, 0.001000 rate, 0.256057 seconds, 109056 images\n",
            "Loaded: 1.464644 seconds\n",
            "\n",
            " (next mAP calculation at 1800 iterations) \n",
            " Last accuracy mAP@0.5 = 98.30 % \n",
            " 1705: 0.375130, 0.402421 avg loss, 0.001000 rate, 0.257789 seconds, 109120 images\n",
            "Loaded: 1.427249 seconds\n",
            "\n",
            " (next mAP calculation at 1800 iterations) \n",
            " Last accuracy mAP@0.5 = 98.30 % \n",
            " 1706: 0.602247, 0.422404 avg loss, 0.001000 rate, 0.265153 seconds, 109184 images\n",
            "Loaded: 1.460407 seconds\n",
            "\n",
            " (next mAP calculation at 1800 iterations) \n",
            " Last accuracy mAP@0.5 = 98.30 % \n",
            " 1707: 0.300674, 0.410231 avg loss, 0.001000 rate, 0.240842 seconds, 109248 images\n",
            "Loaded: 1.507329 seconds\n",
            "\n",
            " (next mAP calculation at 1800 iterations) \n",
            " Last accuracy mAP@0.5 = 98.30 % \n",
            " 1708: 0.416750, 0.410883 avg loss, 0.001000 rate, 0.247071 seconds, 109312 images\n",
            "Loaded: 1.418616 seconds\n",
            "\n",
            " (next mAP calculation at 1800 iterations) \n",
            " Last accuracy mAP@0.5 = 98.30 % \n",
            " 1709: 0.354323, 0.405227 avg loss, 0.001000 rate, 0.247892 seconds, 109376 images\n",
            "Loaded: 1.470811 seconds\n",
            "\n",
            " (next mAP calculation at 1800 iterations) \n",
            " Last accuracy mAP@0.5 = 98.30 % \n",
            " 1710: 0.424708, 0.407175 avg loss, 0.001000 rate, 0.251612 seconds, 109440 images\n",
            "Resizing\n",
            "352 x 352 \n",
            " try to allocate additional workspace_size = 1264.58 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.422312 seconds\n",
            "\n",
            " (next mAP calculation at 1800 iterations) \n",
            " Last accuracy mAP@0.5 = 98.30 % \n",
            " 1711: 0.392158, 0.405673 avg loss, 0.001000 rate, 0.335452 seconds, 109504 images\n",
            "Loaded: 1.419202 seconds\n",
            "\n",
            " (next mAP calculation at 1800 iterations) \n",
            " Last accuracy mAP@0.5 = 98.30 % \n",
            " 1712: 0.373190, 0.402425 avg loss, 0.001000 rate, 0.282345 seconds, 109568 images\n",
            "Loaded: 1.593161 seconds\n",
            "\n",
            " (next mAP calculation at 1800 iterations) \n",
            " Last accuracy mAP@0.5 = 98.30 % \n",
            " 1713: 0.466434, 0.408826 avg loss, 0.001000 rate, 0.299321 seconds, 109632 images\n",
            "Loaded: 1.471483 seconds\n",
            "\n",
            " (next mAP calculation at 1800 iterations) \n",
            " Last accuracy mAP@0.5 = 98.30 % \n",
            " 1714: 0.346961, 0.402639 avg loss, 0.001000 rate, 0.279157 seconds, 109696 images\n",
            "Loaded: 1.502301 seconds\n",
            "\n",
            " (next mAP calculation at 1800 iterations) \n",
            " Last accuracy mAP@0.5 = 98.30 % \n",
            " 1715: 0.480166, 0.410392 avg loss, 0.001000 rate, 0.300042 seconds, 109760 images\n",
            "Loaded: 1.522899 seconds\n",
            "\n",
            " (next mAP calculation at 1800 iterations) \n",
            " Last accuracy mAP@0.5 = 98.30 % \n",
            " 1716: 0.380229, 0.407376 avg loss, 0.001000 rate, 0.308128 seconds, 109824 images\n",
            "Loaded: 1.438301 seconds\n",
            "\n",
            " (next mAP calculation at 1800 iterations) \n",
            " Last accuracy mAP@0.5 = 98.30 % \n",
            " 1717: 0.396722, 0.406310 avg loss, 0.001000 rate, 0.299672 seconds, 109888 images\n",
            "Loaded: 1.428099 seconds\n",
            "\n",
            " (next mAP calculation at 1800 iterations) \n",
            " Last accuracy mAP@0.5 = 98.30 % \n",
            " 1718: 0.424996, 0.408179 avg loss, 0.001000 rate, 0.292412 seconds, 109952 images\n",
            "Loaded: 1.499211 seconds\n",
            "\n",
            " (next mAP calculation at 1800 iterations) \n",
            " Last accuracy mAP@0.5 = 98.30 % \n",
            " 1719: 0.310780, 0.398439 avg loss, 0.001000 rate, 0.321604 seconds, 110016 images\n",
            "Loaded: 1.447852 seconds\n",
            "\n",
            " (next mAP calculation at 1800 iterations) \n",
            " Last accuracy mAP@0.5 = 98.30 % \n",
            " 1720: 0.481228, 0.406718 avg loss, 0.001000 rate, 0.334342 seconds, 110080 images\n",
            "Resizing\n",
            "608 x 608 \n",
            " try to allocate additional workspace_size = 4610.72 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.000041 seconds\n",
            "\n",
            " (next mAP calculation at 1800 iterations) \n",
            " Last accuracy mAP@0.5 = 98.30 % \n",
            " 1721: 0.446391, 0.410685 avg loss, 0.001000 rate, 1.061966 seconds, 110144 images\n",
            "Loaded: 1.235210 seconds\n",
            "\n",
            " (next mAP calculation at 1800 iterations) \n",
            " Last accuracy mAP@0.5 = 98.30 % \n",
            " 1722: 0.438132, 0.413430 avg loss, 0.001000 rate, 1.275423 seconds, 110208 images\n",
            "Loaded: 1.173625 seconds\n",
            "\n",
            " (next mAP calculation at 1800 iterations) \n",
            " Last accuracy mAP@0.5 = 98.30 % \n",
            " 1723: 0.443434, 0.416430 avg loss, 0.001000 rate, 1.056518 seconds, 110272 images\n",
            "Loaded: 1.325829 seconds\n",
            "\n",
            " (next mAP calculation at 1800 iterations) \n",
            " Last accuracy mAP@0.5 = 98.30 % \n",
            " 1724: 0.360195, 0.410807 avg loss, 0.001000 rate, 1.044182 seconds, 110336 images\n",
            "Loaded: 1.159791 seconds\n",
            "\n",
            " (next mAP calculation at 1800 iterations) \n",
            " Last accuracy mAP@0.5 = 98.30 % \n",
            " 1725: 0.314221, 0.401148 avg loss, 0.001000 rate, 1.145523 seconds, 110400 images\n",
            "Loaded: 1.112575 seconds\n",
            "\n",
            " (next mAP calculation at 1800 iterations) \n",
            " Last accuracy mAP@0.5 = 98.30 % \n",
            " 1726: 0.316494, 0.392683 avg loss, 0.001000 rate, 0.987763 seconds, 110464 images\n",
            "Loaded: 1.266497 seconds\n",
            "\n",
            " (next mAP calculation at 1800 iterations) \n",
            " Last accuracy mAP@0.5 = 98.30 % \n",
            " 1727: 0.428806, 0.396295 avg loss, 0.001000 rate, 1.082898 seconds, 110528 images\n",
            "Loaded: 1.293195 seconds\n",
            "\n",
            " (next mAP calculation at 1800 iterations) \n",
            " Last accuracy mAP@0.5 = 98.30 % \n",
            " 1728: 0.420953, 0.398761 avg loss, 0.001000 rate, 1.059105 seconds, 110592 images\n",
            "Loaded: 1.387021 seconds\n",
            "\n",
            " (next mAP calculation at 1800 iterations) \n",
            " Last accuracy mAP@0.5 = 98.30 % \n",
            " 1729: 0.387948, 0.397680 avg loss, 0.001000 rate, 1.137831 seconds, 110656 images\n",
            "Loaded: 1.106766 seconds\n",
            "\n",
            " (next mAP calculation at 1800 iterations) \n",
            " Last accuracy mAP@0.5 = 98.30 % \n",
            " 1730: 0.428911, 0.400803 avg loss, 0.001000 rate, 1.012346 seconds, 110720 images\n",
            "Resizing\n",
            "416 x 416 \n",
            " try to allocate additional workspace_size = 1302.33 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.000031 seconds\n",
            "\n",
            " (next mAP calculation at 1800 iterations) \n",
            " Last accuracy mAP@0.5 = 98.30 % \n",
            " 1731: 0.447839, 0.405506 avg loss, 0.001000 rate, 0.490465 seconds, 110784 images\n",
            "Loaded: 1.357095 seconds\n",
            "\n",
            " (next mAP calculation at 1800 iterations) \n",
            " Last accuracy mAP@0.5 = 98.30 % \n",
            " 1732: 0.433360, 0.408292 avg loss, 0.001000 rate, 0.440532 seconds, 110848 images\n",
            "Loaded: 1.397747 seconds\n",
            "\n",
            " (next mAP calculation at 1800 iterations) \n",
            " Last accuracy mAP@0.5 = 98.30 % \n",
            " 1733: 0.393419, 0.406804 avg loss, 0.001000 rate, 0.497967 seconds, 110912 images\n",
            "Loaded: 1.406892 seconds\n",
            "\n",
            " (next mAP calculation at 1800 iterations) \n",
            " Last accuracy mAP@0.5 = 98.30 % \n",
            " 1734: 0.311167, 0.397241 avg loss, 0.001000 rate, 0.466787 seconds, 110976 images\n",
            "Loaded: 1.470016 seconds\n",
            "\n",
            " (next mAP calculation at 1800 iterations) \n",
            " Last accuracy mAP@0.5 = 98.30 % \n",
            " 1735: 0.363142, 0.393831 avg loss, 0.001000 rate, 0.415679 seconds, 111040 images\n",
            "Loaded: 1.440631 seconds\n",
            "\n",
            " (next mAP calculation at 1800 iterations) \n",
            " Last accuracy mAP@0.5 = 98.30 % \n",
            " 1736: 0.314887, 0.385936 avg loss, 0.001000 rate, 0.455724 seconds, 111104 images\n",
            "Loaded: 1.429356 seconds\n",
            "\n",
            " (next mAP calculation at 1800 iterations) \n",
            " Last accuracy mAP@0.5 = 98.30 % \n",
            " 1737: 0.371557, 0.384498 avg loss, 0.001000 rate, 0.390014 seconds, 111168 images\n",
            "Loaded: 1.484963 seconds\n",
            "\n",
            " (next mAP calculation at 1800 iterations) \n",
            " Last accuracy mAP@0.5 = 98.30 % \n",
            " 1738: 0.392908, 0.385339 avg loss, 0.001000 rate, 0.466814 seconds, 111232 images\n",
            "Loaded: 1.540786 seconds\n",
            "\n",
            " (next mAP calculation at 1800 iterations) \n",
            " Last accuracy mAP@0.5 = 98.30 % \n",
            " 1739: 0.419598, 0.388765 avg loss, 0.001000 rate, 0.448816 seconds, 111296 images\n",
            "Loaded: 1.387394 seconds\n",
            "\n",
            " (next mAP calculation at 1800 iterations) \n",
            " Last accuracy mAP@0.5 = 98.30 % \n",
            " 1740: 0.412412, 0.391130 avg loss, 0.001000 rate, 0.418480 seconds, 111360 images\n",
            "Resizing\n",
            "352 x 352 \n",
            " try to allocate additional workspace_size = 1264.58 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 1.569172 seconds\n",
            "\n",
            " (next mAP calculation at 1800 iterations) \n",
            " Last accuracy mAP@0.5 = 98.30 % \n",
            " 1741: 0.365726, 0.388589 avg loss, 0.001000 rate, 0.364926 seconds, 111424 images\n",
            "Loaded: 1.415887 seconds\n",
            "\n",
            " (next mAP calculation at 1800 iterations) \n",
            " Last accuracy mAP@0.5 = 98.30 % \n",
            " 1742: 0.303596, 0.380090 avg loss, 0.001000 rate, 0.292607 seconds, 111488 images\n",
            "Loaded: 1.463301 seconds\n",
            "\n",
            " (next mAP calculation at 1800 iterations) \n",
            " Last accuracy mAP@0.5 = 98.30 % \n",
            " 1743: 0.337619, 0.375843 avg loss, 0.001000 rate, 0.280363 seconds, 111552 images\n",
            "Loaded: 1.475646 seconds\n",
            "\n",
            " (next mAP calculation at 1800 iterations) \n",
            " Last accuracy mAP@0.5 = 98.30 % \n",
            " 1744: 0.415093, 0.379768 avg loss, 0.001000 rate, 0.288223 seconds, 111616 images\n",
            "Loaded: 1.410493 seconds\n",
            "\n",
            " (next mAP calculation at 1800 iterations) \n",
            " Last accuracy mAP@0.5 = 98.30 % \n",
            " 1745: 0.344922, 0.376283 avg loss, 0.001000 rate, 0.338636 seconds, 111680 images\n",
            "Loaded: 1.430802 seconds\n",
            "\n",
            " (next mAP calculation at 1800 iterations) \n",
            " Last accuracy mAP@0.5 = 98.30 % \n",
            " 1746: 0.447109, 0.383366 avg loss, 0.001000 rate, 0.326714 seconds, 111744 images\n",
            "Loaded: 1.437332 seconds\n",
            "\n",
            " (next mAP calculation at 1800 iterations) \n",
            " Last accuracy mAP@0.5 = 98.30 % \n",
            " 1747: 0.302960, 0.375326 avg loss, 0.001000 rate, 0.274825 seconds, 111808 images\n",
            "Loaded: 1.496070 seconds\n",
            "\n",
            " (next mAP calculation at 1800 iterations) \n",
            " Last accuracy mAP@0.5 = 98.30 % \n",
            " 1748: 0.641573, 0.401950 avg loss, 0.001000 rate, 0.283505 seconds, 111872 images\n",
            "Loaded: 1.424476 seconds\n",
            "\n",
            " (next mAP calculation at 1800 iterations) \n",
            " Last accuracy mAP@0.5 = 98.30 % \n",
            " 1749: 0.689476, 0.430703 avg loss, 0.001000 rate, 0.298452 seconds, 111936 images\n",
            "Loaded: 1.571475 seconds\n",
            "\n",
            " (next mAP calculation at 1800 iterations) \n",
            " Last accuracy mAP@0.5 = 98.30 % \n",
            " 1750: 0.315992, 0.419232 avg loss, 0.001000 rate, 0.330335 seconds, 112000 images\n",
            "Resizing\n",
            "512 x 512 \n",
            " try to allocate additional workspace_size = 4596.96 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.000035 seconds\n",
            "\n",
            " (next mAP calculation at 1800 iterations) \n",
            " Last accuracy mAP@0.5 = 98.30 % \n",
            " 1751: 0.411805, 0.418489 avg loss, 0.001000 rate, 0.740128 seconds, 112064 images\n",
            "Loaded: 1.296471 seconds\n",
            "\n",
            " (next mAP calculation at 1800 iterations) \n",
            " Last accuracy mAP@0.5 = 98.30 % \n",
            " 1752: 0.324468, 0.409087 avg loss, 0.001000 rate, 0.737393 seconds, 112128 images\n",
            "Loaded: 1.268595 seconds\n",
            "\n",
            " (next mAP calculation at 1800 iterations) \n",
            " Last accuracy mAP@0.5 = 98.30 % \n",
            " 1753: 0.379196, 0.406098 avg loss, 0.001000 rate, 0.695643 seconds, 112192 images\n",
            "Loaded: 1.331426 seconds\n",
            "\n",
            " (next mAP calculation at 1800 iterations) \n",
            " Last accuracy mAP@0.5 = 98.30 % \n",
            " 1754: 0.505841, 0.416072 avg loss, 0.001000 rate, 0.685623 seconds, 112256 images\n",
            "Loaded: 1.300837 seconds\n",
            "\n",
            " (next mAP calculation at 1800 iterations) \n",
            " Last accuracy mAP@0.5 = 98.30 % \n",
            " 1755: 0.389136, 0.413379 avg loss, 0.001000 rate, 0.754380 seconds, 112320 images\n",
            "Loaded: 1.438052 seconds\n",
            "\n",
            " (next mAP calculation at 1800 iterations) \n",
            " Last accuracy mAP@0.5 = 98.30 % \n",
            " 1756: 0.344023, 0.406443 avg loss, 0.001000 rate, 0.729322 seconds, 112384 images\n",
            "Loaded: 1.288243 seconds\n",
            "\n",
            " (next mAP calculation at 1800 iterations) \n",
            " Last accuracy mAP@0.5 = 98.30 % \n",
            " 1757: 0.336590, 0.399458 avg loss, 0.001000 rate, 0.735097 seconds, 112448 images\n",
            "Loaded: 1.338012 seconds\n",
            "\n",
            " (next mAP calculation at 1800 iterations) \n",
            " Last accuracy mAP@0.5 = 98.30 % \n",
            " 1758: 0.290169, 0.388529 avg loss, 0.001000 rate, 0.779993 seconds, 112512 images\n",
            "Loaded: 1.298801 seconds\n",
            "\n",
            " (next mAP calculation at 1800 iterations) \n",
            " Last accuracy mAP@0.5 = 98.30 % \n",
            " 1759: 0.406942, 0.390370 avg loss, 0.001000 rate, 0.708658 seconds, 112576 images\n",
            "Loaded: 1.490884 seconds\n",
            "\n",
            " (next mAP calculation at 1800 iterations) \n",
            " Last accuracy mAP@0.5 = 98.30 % \n",
            " 1760: 0.395262, 0.390859 avg loss, 0.001000 rate, 0.743125 seconds, 112640 images\n",
            "Resizing\n",
            "384 x 384 \n",
            " try to allocate additional workspace_size = 1302.33 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 1.498883 seconds\n",
            "\n",
            " (next mAP calculation at 1800 iterations) \n",
            " Last accuracy mAP@0.5 = 98.30 % \n",
            " 1761: 0.611522, 0.412926 avg loss, 0.001000 rate, 0.408709 seconds, 112704 images\n",
            "Loaded: 1.430990 seconds\n",
            "\n",
            " (next mAP calculation at 1800 iterations) \n",
            " Last accuracy mAP@0.5 = 98.30 % \n",
            " 1762: 0.334569, 0.405090 avg loss, 0.001000 rate, 0.387767 seconds, 112768 images\n",
            "Loaded: 1.387778 seconds\n",
            "\n",
            " (next mAP calculation at 1800 iterations) \n",
            " Last accuracy mAP@0.5 = 98.30 % \n",
            " 1763: 0.558117, 0.420393 avg loss, 0.001000 rate, 0.347327 seconds, 112832 images\n",
            "Loaded: 1.485314 seconds\n",
            "\n",
            " (next mAP calculation at 1800 iterations) \n",
            " Last accuracy mAP@0.5 = 98.30 % \n",
            " 1764: 0.388379, 0.417191 avg loss, 0.001000 rate, 0.358559 seconds, 112896 images\n",
            "Loaded: 1.467945 seconds\n",
            "\n",
            " (next mAP calculation at 1800 iterations) \n",
            " Last accuracy mAP@0.5 = 98.30 % \n",
            " 1765: 0.302807, 0.405753 avg loss, 0.001000 rate, 0.355799 seconds, 112960 images\n",
            "Loaded: 1.566700 seconds\n",
            "\n",
            " (next mAP calculation at 1800 iterations) \n",
            " Last accuracy mAP@0.5 = 98.30 % \n",
            " 1766: 0.439752, 0.409153 avg loss, 0.001000 rate, 0.359642 seconds, 113024 images\n",
            "Loaded: 1.396310 seconds\n",
            "\n",
            " (next mAP calculation at 1800 iterations) \n",
            " Last accuracy mAP@0.5 = 98.30 % \n",
            " 1767: 0.420110, 0.410249 avg loss, 0.001000 rate, 0.361969 seconds, 113088 images\n",
            "Loaded: 1.394300 seconds\n",
            "\n",
            " (next mAP calculation at 1800 iterations) \n",
            " Last accuracy mAP@0.5 = 98.30 % \n",
            " 1768: 0.427774, 0.412001 avg loss, 0.001000 rate, 0.355333 seconds, 113152 images\n",
            "Loaded: 1.461860 seconds\n",
            "\n",
            " (next mAP calculation at 1800 iterations) \n",
            " Last accuracy mAP@0.5 = 98.30 % \n",
            " 1769: 0.382898, 0.409091 avg loss, 0.001000 rate, 0.365179 seconds, 113216 images\n",
            "Loaded: 1.461929 seconds\n",
            "\n",
            " (next mAP calculation at 1800 iterations) \n",
            " Last accuracy mAP@0.5 = 98.30 % \n",
            " 1770: 0.392816, 0.407463 avg loss, 0.001000 rate, 0.360154 seconds, 113280 images\n",
            "Resizing\n",
            "384 x 384 \n",
            " try to allocate additional workspace_size = 1302.33 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 1.472891 seconds\n",
            "\n",
            " (next mAP calculation at 1800 iterations) \n",
            " Last accuracy mAP@0.5 = 98.30 % \n",
            " 1771: 0.330520, 0.399769 avg loss, 0.001000 rate, 0.420357 seconds, 113344 images\n",
            "Loaded: 1.375254 seconds\n",
            "\n",
            " (next mAP calculation at 1800 iterations) \n",
            " Last accuracy mAP@0.5 = 98.30 % \n",
            " 1772: 0.352061, 0.394998 avg loss, 0.001000 rate, 0.383445 seconds, 113408 images\n",
            "Loaded: 1.420198 seconds\n",
            "\n",
            " (next mAP calculation at 1800 iterations) \n",
            " Last accuracy mAP@0.5 = 98.30 % \n",
            " 1773: 0.318354, 0.387334 avg loss, 0.001000 rate, 0.350652 seconds, 113472 images\n",
            "Loaded: 1.497574 seconds\n",
            "\n",
            " (next mAP calculation at 1800 iterations) \n",
            " Last accuracy mAP@0.5 = 98.30 % \n",
            " 1774: 0.290131, 0.377614 avg loss, 0.001000 rate, 0.348367 seconds, 113536 images\n",
            "Loaded: 1.485609 seconds\n",
            "\n",
            " (next mAP calculation at 1800 iterations) \n",
            " Last accuracy mAP@0.5 = 98.30 % \n",
            " 1775: 0.304281, 0.370280 avg loss, 0.001000 rate, 0.374771 seconds, 113600 images\n",
            "Loaded: 1.474836 seconds\n",
            "\n",
            " (next mAP calculation at 1800 iterations) \n",
            " Last accuracy mAP@0.5 = 98.30 % \n",
            " 1776: 0.418730, 0.375125 avg loss, 0.001000 rate, 0.366006 seconds, 113664 images\n",
            "Loaded: 1.571864 seconds\n",
            "\n",
            " (next mAP calculation at 1800 iterations) \n",
            " Last accuracy mAP@0.5 = 98.30 % \n",
            " 1777: 0.353732, 0.372986 avg loss, 0.001000 rate, 0.401413 seconds, 113728 images\n",
            "Loaded: 1.383084 seconds\n",
            "\n",
            " (next mAP calculation at 1800 iterations) \n",
            " Last accuracy mAP@0.5 = 98.30 % \n",
            " 1778: 0.268006, 0.362488 avg loss, 0.001000 rate, 0.416766 seconds, 113792 images\n",
            "Loaded: 1.527414 seconds\n",
            "\n",
            " (next mAP calculation at 1800 iterations) \n",
            " Last accuracy mAP@0.5 = 98.30 % \n",
            " 1779: 0.229370, 0.349176 avg loss, 0.001000 rate, 0.379866 seconds, 113856 images\n",
            "Loaded: 1.335812 seconds\n",
            "\n",
            " (next mAP calculation at 1800 iterations) \n",
            " Last accuracy mAP@0.5 = 98.30 % \n",
            " 1780: 0.348051, 0.349064 avg loss, 0.001000 rate, 0.362057 seconds, 113920 images\n",
            "Resizing\n",
            "512 x 512 \n",
            " try to allocate additional workspace_size = 4596.96 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.000040 seconds\n",
            "\n",
            " (next mAP calculation at 1800 iterations) \n",
            " Last accuracy mAP@0.5 = 98.30 % \n",
            " 1781: 0.417916, 0.355949 avg loss, 0.001000 rate, 0.669585 seconds, 113984 images\n",
            "Loaded: 1.329910 seconds\n",
            "\n",
            " (next mAP calculation at 1800 iterations) \n",
            " Last accuracy mAP@0.5 = 98.30 % \n",
            " 1782: 0.326599, 0.353014 avg loss, 0.001000 rate, 0.756573 seconds, 114048 images\n",
            "Loaded: 1.432875 seconds\n",
            "\n",
            " (next mAP calculation at 1800 iterations) \n",
            " Last accuracy mAP@0.5 = 98.30 % \n",
            " 1783: 0.340042, 0.351717 avg loss, 0.001000 rate, 0.737040 seconds, 114112 images\n",
            "Loaded: 1.405858 seconds\n",
            "\n",
            " (next mAP calculation at 1800 iterations) \n",
            " Last accuracy mAP@0.5 = 98.30 % \n",
            " 1784: 0.409795, 0.357524 avg loss, 0.001000 rate, 0.635518 seconds, 114176 images\n",
            "Loaded: 1.351529 seconds\n",
            "\n",
            " (next mAP calculation at 1800 iterations) \n",
            " Last accuracy mAP@0.5 = 98.30 % \n",
            " 1785: 0.296790, 0.351451 avg loss, 0.001000 rate, 0.635076 seconds, 114240 images\n",
            "Loaded: 1.389411 seconds\n",
            "\n",
            " (next mAP calculation at 1800 iterations) \n",
            " Last accuracy mAP@0.5 = 98.30 % \n",
            " 1786: 0.362003, 0.352506 avg loss, 0.001000 rate, 0.644563 seconds, 114304 images\n",
            "Loaded: 1.382234 seconds\n",
            "\n",
            " (next mAP calculation at 1800 iterations) \n",
            " Last accuracy mAP@0.5 = 98.30 % \n",
            " 1787: 0.340852, 0.351341 avg loss, 0.001000 rate, 0.680568 seconds, 114368 images\n",
            "Loaded: 1.347373 seconds\n",
            "\n",
            " (next mAP calculation at 1800 iterations) \n",
            " Last accuracy mAP@0.5 = 98.30 % \n",
            " 1788: 0.458043, 0.362011 avg loss, 0.001000 rate, 0.692747 seconds, 114432 images\n",
            "Loaded: 1.327167 seconds\n",
            "\n",
            " (next mAP calculation at 1800 iterations) \n",
            " Last accuracy mAP@0.5 = 98.30 % \n",
            " 1789: 0.399715, 0.365781 avg loss, 0.001000 rate, 0.640207 seconds, 114496 images\n",
            "Loaded: 1.446057 seconds\n",
            "\n",
            " (next mAP calculation at 1800 iterations) \n",
            " Last accuracy mAP@0.5 = 98.30 % \n",
            " 1790: 0.403462, 0.369549 avg loss, 0.001000 rate, 0.681751 seconds, 114560 images\n",
            "Resizing\n",
            "576 x 576 \n",
            " try to allocate additional workspace_size = 4605.87 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.000035 seconds\n",
            "\n",
            " (next mAP calculation at 1800 iterations) \n",
            " Last accuracy mAP@0.5 = 98.30 % \n",
            " 1791: 0.464283, 0.379023 avg loss, 0.001000 rate, 1.042600 seconds, 114624 images\n",
            "Loaded: 1.263724 seconds\n",
            "\n",
            " (next mAP calculation at 1800 iterations) \n",
            " Last accuracy mAP@0.5 = 98.30 % \n",
            " 1792: 0.410856, 0.382206 avg loss, 0.001000 rate, 0.897573 seconds, 114688 images\n",
            "Loaded: 1.336587 seconds\n",
            "\n",
            " (next mAP calculation at 1800 iterations) \n",
            " Last accuracy mAP@0.5 = 98.30 % \n",
            " 1793: 0.355275, 0.379513 avg loss, 0.001000 rate, 0.912976 seconds, 114752 images\n",
            "Loaded: 1.259135 seconds\n",
            "\n",
            " (next mAP calculation at 1800 iterations) \n",
            " Last accuracy mAP@0.5 = 98.30 % \n",
            " 1794: 0.413123, 0.382874 avg loss, 0.001000 rate, 0.883245 seconds, 114816 images\n",
            "Loaded: 1.342022 seconds\n",
            "\n",
            " (next mAP calculation at 1800 iterations) \n",
            " Last accuracy mAP@0.5 = 98.30 % \n",
            " 1795: 0.419084, 0.386495 avg loss, 0.001000 rate, 0.836126 seconds, 114880 images\n",
            "Loaded: 1.441778 seconds\n",
            "\n",
            " (next mAP calculation at 1800 iterations) \n",
            " Last accuracy mAP@0.5 = 98.30 % \n",
            " 1796: 0.445435, 0.392389 avg loss, 0.001000 rate, 0.864686 seconds, 114944 images\n",
            "Loaded: 1.248603 seconds\n",
            "\n",
            " (next mAP calculation at 1800 iterations) \n",
            " Last accuracy mAP@0.5 = 98.30 % \n",
            " 1797: 0.295579, 0.382708 avg loss, 0.001000 rate, 0.899197 seconds, 115008 images\n",
            "Loaded: 1.300329 seconds\n",
            "\n",
            " (next mAP calculation at 1800 iterations) \n",
            " Last accuracy mAP@0.5 = 98.30 % \n",
            " 1798: 0.363128, 0.380750 avg loss, 0.001000 rate, 0.929923 seconds, 115072 images\n",
            "Loaded: 1.277216 seconds\n",
            "\n",
            " (next mAP calculation at 1800 iterations) \n",
            " Last accuracy mAP@0.5 = 98.30 % \n",
            " 1799: 0.417604, 0.384435 avg loss, 0.001000 rate, 0.856816 seconds, 115136 images\n",
            "Loaded: 1.367381 seconds\n",
            "\n",
            " (next mAP calculation at 1800 iterations) \n",
            " Last accuracy mAP@0.5 = 98.30 % \n",
            " 1800: 0.312057, 0.377198 avg loss, 0.001000 rate, 1.015072 seconds, 115200 images\n",
            "Resizing to initial size: 416 x 416 \n",
            " try to allocate additional workspace_size = 1302.33 MB \n",
            " CUDA allocate done! \n",
            "\n",
            " calculation mAP (mean average precision)...\n",
            "368\n",
            " detections_count = 582, unique_truth_count = 374  \n",
            "class_id = 0, name = banana, ap = 98.78%   \t (TP = 364, FP = 9) \n",
            "\n",
            " for thresh = 0.25, precision = 0.98, recall = 0.97, F1-score = 0.97 \n",
            " for thresh = 0.25, TP = 364, FP = 9, FN = 10, average IoU = 76.90 % \n",
            "\n",
            " IoU threshold = 50 %, used Area-Under-Curve for each unique Recall \n",
            " mean average precision (mAP@0.50) = 0.987801, or 98.78 % \n",
            "Total Detection Time: 16.000000 Seconds\n",
            "\n",
            "Set -points flag:\n",
            " `-points 101` for MS COCO \n",
            " `-points 11` for PascalVOC 2007 (uncomment `difficult` in voc.data) \n",
            " `-points 0` (AUC) for ImageNet, PascalVOC 2010-2012, your custom dataset\n",
            "\n",
            " mean_average_precision (mAP@0.5) = 0.987801 \n",
            "Saving weights to /content/gdrive/My Drive/darknet/backup/yolov3-tiny-280120_last.weights\n",
            "Resizing\n",
            "352 x 352 \n",
            " try to allocate additional workspace_size = 1264.58 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.078097 seconds\n",
            "\n",
            " (next mAP calculation at 1900 iterations) \n",
            " Last accuracy mAP@0.5 = 98.78 % \n",
            " 1801: 0.250727, 0.364551 avg loss, 0.001000 rate, 0.376936 seconds, 115264 images\n",
            "Loaded: 1.358341 seconds\n",
            "\n",
            " (next mAP calculation at 1900 iterations) \n",
            " Last accuracy mAP@0.5 = 98.78 % \n",
            " 1802: 0.404977, 0.368593 avg loss, 0.001000 rate, 0.286935 seconds, 115328 images\n",
            "Loaded: 1.509466 seconds\n",
            "\n",
            " (next mAP calculation at 1900 iterations) \n",
            " Last accuracy mAP@0.5 = 98.78 % \n",
            " 1803: 0.373405, 0.369074 avg loss, 0.001000 rate, 0.279384 seconds, 115392 images\n",
            "Loaded: 1.618872 seconds\n",
            "\n",
            " (next mAP calculation at 1900 iterations) \n",
            " Last accuracy mAP@0.5 = 98.78 % \n",
            " 1804: 0.346168, 0.366784 avg loss, 0.001000 rate, 0.294512 seconds, 115456 images\n",
            "Loaded: 1.444339 seconds\n",
            "\n",
            " (next mAP calculation at 1900 iterations) \n",
            " Last accuracy mAP@0.5 = 98.78 % \n",
            " 1805: 0.329034, 0.363009 avg loss, 0.001000 rate, 0.305150 seconds, 115520 images\n",
            "Loaded: 1.481340 seconds\n",
            "\n",
            " (next mAP calculation at 1900 iterations) \n",
            " Last accuracy mAP@0.5 = 98.78 % \n",
            " 1806: 0.390196, 0.365727 avg loss, 0.001000 rate, 0.319099 seconds, 115584 images\n",
            "Loaded: 1.490051 seconds\n",
            "\n",
            " (next mAP calculation at 1900 iterations) \n",
            " Last accuracy mAP@0.5 = 98.78 % \n",
            " 1807: 0.499951, 0.379150 avg loss, 0.001000 rate, 0.308721 seconds, 115648 images\n",
            "Loaded: 1.453480 seconds\n",
            "\n",
            " (next mAP calculation at 1900 iterations) \n",
            " Last accuracy mAP@0.5 = 98.78 % \n",
            " 1808: 0.306100, 0.371845 avg loss, 0.001000 rate, 0.290787 seconds, 115712 images\n",
            "Loaded: 1.402877 seconds\n",
            "\n",
            " (next mAP calculation at 1900 iterations) \n",
            " Last accuracy mAP@0.5 = 98.78 % \n",
            " 1809: 0.388034, 0.373464 avg loss, 0.001000 rate, 0.296303 seconds, 115776 images\n",
            "Loaded: 1.457996 seconds\n",
            "\n",
            " (next mAP calculation at 1900 iterations) \n",
            " Last accuracy mAP@0.5 = 98.78 % \n",
            " 1810: 0.412439, 0.377361 avg loss, 0.001000 rate, 0.279646 seconds, 115840 images\n",
            "Resizing\n",
            "448 x 448 \n",
            " try to allocate additional workspace_size = 1233.65 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.000041 seconds\n",
            "\n",
            " (next mAP calculation at 1900 iterations) \n",
            " Last accuracy mAP@0.5 = 98.78 % \n",
            " 1811: 0.302706, 0.369896 avg loss, 0.001000 rate, 0.563873 seconds, 115904 images\n",
            "Loaded: 1.396507 seconds\n",
            "\n",
            " (next mAP calculation at 1900 iterations) \n",
            " Last accuracy mAP@0.5 = 98.78 % \n",
            " 1812: 0.371560, 0.370062 avg loss, 0.001000 rate, 0.538277 seconds, 115968 images\n",
            "Loaded: 1.412230 seconds\n",
            "\n",
            " (next mAP calculation at 1900 iterations) \n",
            " Last accuracy mAP@0.5 = 98.78 % \n",
            " 1813: 0.342433, 0.367299 avg loss, 0.001000 rate, 0.563680 seconds, 116032 images\n",
            "Loaded: 1.372778 seconds\n",
            "\n",
            " (next mAP calculation at 1900 iterations) \n",
            " Last accuracy mAP@0.5 = 98.78 % \n",
            " 1814: 0.307110, 0.361280 avg loss, 0.001000 rate, 0.484113 seconds, 116096 images\n",
            "Loaded: 1.454924 seconds\n",
            "\n",
            " (next mAP calculation at 1900 iterations) \n",
            " Last accuracy mAP@0.5 = 98.78 % \n",
            " 1815: 0.363880, 0.361540 avg loss, 0.001000 rate, 0.487751 seconds, 116160 images\n",
            "Loaded: 1.511458 seconds\n",
            "\n",
            " (next mAP calculation at 1900 iterations) \n",
            " Last accuracy mAP@0.5 = 98.78 % \n",
            " 1816: 0.509482, 0.376334 avg loss, 0.001000 rate, 0.462296 seconds, 116224 images\n",
            "Loaded: 1.399921 seconds\n",
            "\n",
            " (next mAP calculation at 1900 iterations) \n",
            " Last accuracy mAP@0.5 = 98.78 % \n",
            " 1817: 0.484658, 0.387167 avg loss, 0.001000 rate, 0.479158 seconds, 116288 images\n",
            "Loaded: 1.512273 seconds\n",
            "\n",
            " (next mAP calculation at 1900 iterations) \n",
            " Last accuracy mAP@0.5 = 98.78 % \n",
            " 1818: 0.397473, 0.388197 avg loss, 0.001000 rate, 0.531749 seconds, 116352 images\n",
            "Loaded: 1.451341 seconds\n",
            "\n",
            " (next mAP calculation at 1900 iterations) \n",
            " Last accuracy mAP@0.5 = 98.78 % \n",
            " 1819: 0.304510, 0.379829 avg loss, 0.001000 rate, 0.495276 seconds, 116416 images\n",
            "Loaded: 1.441137 seconds\n",
            "\n",
            " (next mAP calculation at 1900 iterations) \n",
            " Last accuracy mAP@0.5 = 98.78 % \n",
            " 1820: 0.351474, 0.376993 avg loss, 0.001000 rate, 0.475377 seconds, 116480 images\n",
            "Resizing\n",
            "320 x 320 \n",
            " try to allocate additional workspace_size = 868.75 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 1.641721 seconds\n",
            "\n",
            " (next mAP calculation at 1900 iterations) \n",
            " Last accuracy mAP@0.5 = 98.78 % \n",
            " 1821: 0.384540, 0.377748 avg loss, 0.001000 rate, 0.245974 seconds, 116544 images\n",
            "Loaded: 1.523711 seconds\n",
            "\n",
            " (next mAP calculation at 1900 iterations) \n",
            " Last accuracy mAP@0.5 = 98.78 % \n",
            " 1822: 0.298466, 0.369820 avg loss, 0.001000 rate, 0.248131 seconds, 116608 images\n",
            "Loaded: 1.471281 seconds\n",
            "\n",
            " (next mAP calculation at 1900 iterations) \n",
            " Last accuracy mAP@0.5 = 98.78 % \n",
            " 1823: 0.440535, 0.376891 avg loss, 0.001000 rate, 0.245649 seconds, 116672 images\n",
            "Loaded: 1.437446 seconds\n",
            "\n",
            " (next mAP calculation at 1900 iterations) \n",
            " Last accuracy mAP@0.5 = 98.78 % \n",
            " 1824: 0.537180, 0.392920 avg loss, 0.001000 rate, 0.254851 seconds, 116736 images\n",
            "Loaded: 1.429639 seconds\n",
            "\n",
            " (next mAP calculation at 1900 iterations) \n",
            " Last accuracy mAP@0.5 = 98.78 % \n",
            " 1825: 0.514840, 0.405112 avg loss, 0.001000 rate, 0.266016 seconds, 116800 images\n",
            "Loaded: 1.403039 seconds\n",
            "\n",
            " (next mAP calculation at 1900 iterations) \n",
            " Last accuracy mAP@0.5 = 98.78 % \n",
            " 1826: 0.478521, 0.412453 avg loss, 0.001000 rate, 0.260309 seconds, 116864 images\n",
            "Loaded: 1.415424 seconds\n",
            "\n",
            " (next mAP calculation at 1900 iterations) \n",
            " Last accuracy mAP@0.5 = 98.78 % \n",
            " 1827: 0.347292, 0.405937 avg loss, 0.001000 rate, 0.270529 seconds, 116928 images\n",
            "Loaded: 1.420915 seconds\n",
            "\n",
            " (next mAP calculation at 1900 iterations) \n",
            " Last accuracy mAP@0.5 = 98.78 % \n",
            " 1828: 0.530929, 0.418436 avg loss, 0.001000 rate, 0.270680 seconds, 116992 images\n",
            "Loaded: 1.409027 seconds\n",
            "\n",
            " (next mAP calculation at 1900 iterations) \n",
            " Last accuracy mAP@0.5 = 98.78 % \n",
            " 1829: 0.329526, 0.409545 avg loss, 0.001000 rate, 0.271399 seconds, 117056 images\n",
            "Loaded: 1.503988 seconds\n",
            "\n",
            " (next mAP calculation at 1900 iterations) \n",
            " Last accuracy mAP@0.5 = 98.78 % \n",
            " 1830: 0.382457, 0.406836 avg loss, 0.001000 rate, 0.273950 seconds, 117120 images\n",
            "Resizing\n",
            "384 x 384 \n",
            " try to allocate additional workspace_size = 1302.33 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.285278 seconds\n",
            "\n",
            " (next mAP calculation at 1900 iterations) \n",
            " Last accuracy mAP@0.5 = 98.78 % \n",
            " 1831: 0.337401, 0.399893 avg loss, 0.001000 rate, 0.420207 seconds, 117184 images\n",
            "Loaded: 1.377643 seconds\n",
            "\n",
            " (next mAP calculation at 1900 iterations) \n",
            " Last accuracy mAP@0.5 = 98.78 % \n",
            " 1832: 0.302066, 0.390110 avg loss, 0.001000 rate, 0.400619 seconds, 117248 images\n",
            "Loaded: 1.371993 seconds\n",
            "\n",
            " (next mAP calculation at 1900 iterations) \n",
            " Last accuracy mAP@0.5 = 98.78 % \n",
            " 1833: 0.451753, 0.396274 avg loss, 0.001000 rate, 0.392334 seconds, 117312 images\n",
            "Loaded: 1.387249 seconds\n",
            "\n",
            " (next mAP calculation at 1900 iterations) \n",
            " Last accuracy mAP@0.5 = 98.78 % \n",
            " 1834: 0.431845, 0.399831 avg loss, 0.001000 rate, 0.357823 seconds, 117376 images\n",
            "Loaded: 1.475406 seconds\n",
            "\n",
            " (next mAP calculation at 1900 iterations) \n",
            " Last accuracy mAP@0.5 = 98.78 % \n",
            " 1835: 0.268478, 0.386696 avg loss, 0.001000 rate, 0.358755 seconds, 117440 images\n",
            "Loaded: 1.487826 seconds\n",
            "\n",
            " (next mAP calculation at 1900 iterations) \n",
            " Last accuracy mAP@0.5 = 98.78 % \n",
            " 1836: 0.420256, 0.390052 avg loss, 0.001000 rate, 0.341655 seconds, 117504 images\n",
            "Loaded: 1.413979 seconds\n",
            "\n",
            " (next mAP calculation at 1900 iterations) \n",
            " Last accuracy mAP@0.5 = 98.78 % \n",
            " 1837: 0.330934, 0.384140 avg loss, 0.001000 rate, 0.384602 seconds, 117568 images\n",
            "Loaded: 1.425961 seconds\n",
            "\n",
            " (next mAP calculation at 1900 iterations) \n",
            " Last accuracy mAP@0.5 = 98.78 % \n",
            " 1838: 0.318734, 0.377600 avg loss, 0.001000 rate, 0.382183 seconds, 117632 images\n",
            "Loaded: 1.415977 seconds\n",
            "\n",
            " (next mAP calculation at 1900 iterations) \n",
            " Last accuracy mAP@0.5 = 98.78 % \n",
            " 1839: 0.336547, 0.373494 avg loss, 0.001000 rate, 0.363899 seconds, 117696 images\n",
            "Loaded: 1.437651 seconds\n",
            "\n",
            " (next mAP calculation at 1900 iterations) \n",
            " Last accuracy mAP@0.5 = 98.78 % \n",
            " 1840: 0.263111, 0.362456 avg loss, 0.001000 rate, 0.352070 seconds, 117760 images\n",
            "Resizing\n",
            "448 x 448 \n",
            " try to allocate additional workspace_size = 1233.65 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.000035 seconds\n",
            "\n",
            " (next mAP calculation at 1900 iterations) \n",
            " Last accuracy mAP@0.5 = 98.78 % \n",
            " 1841: 0.340377, 0.360248 avg loss, 0.001000 rate, 0.529428 seconds, 117824 images\n",
            "Loaded: 1.395197 seconds\n",
            "\n",
            " (next mAP calculation at 1900 iterations) \n",
            " Last accuracy mAP@0.5 = 98.78 % \n",
            " 1842: 0.339671, 0.358190 avg loss, 0.001000 rate, 0.501787 seconds, 117888 images\n",
            "Loaded: 1.391096 seconds\n",
            "\n",
            " (next mAP calculation at 1900 iterations) \n",
            " Last accuracy mAP@0.5 = 98.78 % \n",
            " 1843: 0.355929, 0.357964 avg loss, 0.001000 rate, 0.476786 seconds, 117952 images\n",
            "Loaded: 1.426244 seconds\n",
            "\n",
            " (next mAP calculation at 1900 iterations) \n",
            " Last accuracy mAP@0.5 = 98.78 % \n",
            " 1844: 0.307028, 0.352871 avg loss, 0.001000 rate, 0.496965 seconds, 118016 images\n",
            "Loaded: 1.396224 seconds\n",
            "\n",
            " (next mAP calculation at 1900 iterations) \n",
            " Last accuracy mAP@0.5 = 98.78 % \n",
            " 1845: 0.432986, 0.360882 avg loss, 0.001000 rate, 0.474098 seconds, 118080 images\n",
            "Loaded: 1.477541 seconds\n",
            "\n",
            " (next mAP calculation at 1900 iterations) \n",
            " Last accuracy mAP@0.5 = 98.78 % \n",
            " 1846: 0.361000, 0.360894 avg loss, 0.001000 rate, 0.486727 seconds, 118144 images\n",
            "Loaded: 1.468494 seconds\n",
            "\n",
            " (next mAP calculation at 1900 iterations) \n",
            " Last accuracy mAP@0.5 = 98.78 % \n",
            " 1847: 0.354460, 0.360251 avg loss, 0.001000 rate, 0.551321 seconds, 118208 images\n",
            "Loaded: 1.417602 seconds\n",
            "\n",
            " (next mAP calculation at 1900 iterations) \n",
            " Last accuracy mAP@0.5 = 98.78 % \n",
            " 1848: 0.335705, 0.357796 avg loss, 0.001000 rate, 0.542519 seconds, 118272 images\n",
            "Loaded: 1.468316 seconds\n",
            "\n",
            " (next mAP calculation at 1900 iterations) \n",
            " Last accuracy mAP@0.5 = 98.78 % \n",
            " 1849: 0.447444, 0.366761 avg loss, 0.001000 rate, 0.556665 seconds, 118336 images\n",
            "Loaded: 1.365691 seconds\n",
            "\n",
            " (next mAP calculation at 1900 iterations) \n",
            " Last accuracy mAP@0.5 = 98.78 % \n",
            " 1850: 0.355294, 0.365614 avg loss, 0.001000 rate, 0.537669 seconds, 118400 images\n",
            "Resizing\n",
            "576 x 576 \n",
            " try to allocate additional workspace_size = 4605.87 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.000036 seconds\n",
            "\n",
            " (next mAP calculation at 1900 iterations) \n",
            " Last accuracy mAP@0.5 = 98.78 % \n",
            " 1851: 0.349591, 0.364012 avg loss, 0.001000 rate, 0.901609 seconds, 118464 images\n",
            "Loaded: 1.310698 seconds\n",
            "\n",
            " (next mAP calculation at 1900 iterations) \n",
            " Last accuracy mAP@0.5 = 98.78 % \n",
            " 1852: 0.429986, 0.370609 avg loss, 0.001000 rate, 0.908734 seconds, 118528 images\n",
            "Loaded: 1.266183 seconds\n",
            "\n",
            " (next mAP calculation at 1900 iterations) \n",
            " Last accuracy mAP@0.5 = 98.78 % \n",
            " 1853: 0.434159, 0.376964 avg loss, 0.001000 rate, 0.902117 seconds, 118592 images\n",
            "Loaded: 1.271213 seconds\n",
            "\n",
            " (next mAP calculation at 1900 iterations) \n",
            " Last accuracy mAP@0.5 = 98.78 % \n",
            " 1854: 0.383542, 0.377622 avg loss, 0.001000 rate, 0.941526 seconds, 118656 images\n",
            "Loaded: 1.200023 seconds\n",
            "\n",
            " (next mAP calculation at 1900 iterations) \n",
            " Last accuracy mAP@0.5 = 98.78 % \n",
            " 1855: 0.387833, 0.378643 avg loss, 0.001000 rate, 0.905509 seconds, 118720 images\n",
            "Loaded: 1.239052 seconds\n",
            "\n",
            " (next mAP calculation at 1900 iterations) \n",
            " Last accuracy mAP@0.5 = 98.78 % \n",
            " 1856: 0.456443, 0.386423 avg loss, 0.001000 rate, 0.973156 seconds, 118784 images\n",
            "Loaded: 1.245866 seconds\n",
            "\n",
            " (next mAP calculation at 1900 iterations) \n",
            " Last accuracy mAP@0.5 = 98.78 % \n",
            " 1857: 0.386063, 0.386387 avg loss, 0.001000 rate, 0.956522 seconds, 118848 images\n",
            "Loaded: 1.259174 seconds\n",
            "\n",
            " (next mAP calculation at 1900 iterations) \n",
            " Last accuracy mAP@0.5 = 98.78 % \n",
            " 1858: 0.386103, 0.386359 avg loss, 0.001000 rate, 0.989550 seconds, 118912 images\n",
            "Loaded: 1.184059 seconds\n",
            "\n",
            " (next mAP calculation at 1900 iterations) \n",
            " Last accuracy mAP@0.5 = 98.78 % \n",
            " 1859: 0.381678, 0.385891 avg loss, 0.001000 rate, 0.879288 seconds, 118976 images\n",
            "Loaded: 1.315747 seconds\n",
            "\n",
            " (next mAP calculation at 1900 iterations) \n",
            " Last accuracy mAP@0.5 = 98.78 % \n",
            " 1860: 0.341660, 0.381467 avg loss, 0.001000 rate, 0.852638 seconds, 119040 images\n",
            "Resizing\n",
            "576 x 576 \n",
            " try to allocate additional workspace_size = 4605.87 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 1.547359 seconds\n",
            "\n",
            " (next mAP calculation at 1900 iterations) \n",
            " Last accuracy mAP@0.5 = 98.78 % \n",
            " 1861: 0.357065, 0.379027 avg loss, 0.001000 rate, 0.900081 seconds, 119104 images\n",
            "Loaded: 1.358449 seconds\n",
            "\n",
            " (next mAP calculation at 1900 iterations) \n",
            " Last accuracy mAP@0.5 = 98.78 % \n",
            " 1862: 0.377144, 0.378839 avg loss, 0.001000 rate, 0.911887 seconds, 119168 images\n",
            "Loaded: 1.297652 seconds\n",
            "\n",
            " (next mAP calculation at 1900 iterations) \n",
            " Last accuracy mAP@0.5 = 98.78 % \n",
            " 1863: 0.416170, 0.382572 avg loss, 0.001000 rate, 0.865028 seconds, 119232 images\n",
            "Loaded: 1.295912 seconds\n",
            "\n",
            " (next mAP calculation at 1900 iterations) \n",
            " Last accuracy mAP@0.5 = 98.78 % \n",
            " 1864: 0.430871, 0.387402 avg loss, 0.001000 rate, 0.867308 seconds, 119296 images\n",
            "Loaded: 1.435597 seconds\n",
            "\n",
            " (next mAP calculation at 1900 iterations) \n",
            " Last accuracy mAP@0.5 = 98.78 % \n",
            " 1865: 0.492862, 0.397948 avg loss, 0.001000 rate, 0.972655 seconds, 119360 images\n",
            "Loaded: 1.274563 seconds\n",
            "\n",
            " (next mAP calculation at 1900 iterations) \n",
            " Last accuracy mAP@0.5 = 98.78 % \n",
            " 1866: 0.355953, 0.393748 avg loss, 0.001000 rate, 0.838644 seconds, 119424 images\n",
            "Loaded: 1.357617 seconds\n",
            "\n",
            " (next mAP calculation at 1900 iterations) \n",
            " Last accuracy mAP@0.5 = 98.78 % \n",
            " 1867: 0.359159, 0.390290 avg loss, 0.001000 rate, 0.950813 seconds, 119488 images\n",
            "Loaded: 1.255407 seconds\n",
            "\n",
            " (next mAP calculation at 1900 iterations) \n",
            " Last accuracy mAP@0.5 = 98.78 % \n",
            " 1868: 0.406423, 0.391903 avg loss, 0.001000 rate, 0.858113 seconds, 119552 images\n",
            "Loaded: 1.348858 seconds\n",
            "\n",
            " (next mAP calculation at 1900 iterations) \n",
            " Last accuracy mAP@0.5 = 98.78 % \n",
            " 1869: 0.371182, 0.389831 avg loss, 0.001000 rate, 0.961157 seconds, 119616 images\n",
            "Loaded: 1.279999 seconds\n",
            "\n",
            " (next mAP calculation at 1900 iterations) \n",
            " Last accuracy mAP@0.5 = 98.78 % \n",
            " 1870: 0.403563, 0.391204 avg loss, 0.001000 rate, 0.873653 seconds, 119680 images\n",
            "Resizing\n",
            "384 x 384 \n",
            " try to allocate additional workspace_size = 1302.33 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.000033 seconds\n",
            "\n",
            " (next mAP calculation at 1900 iterations) \n",
            " Last accuracy mAP@0.5 = 98.78 % \n",
            " 1871: 0.296468, 0.381730 avg loss, 0.001000 rate, 0.425930 seconds, 119744 images\n",
            "Loaded: 1.377187 seconds\n",
            "\n",
            " (next mAP calculation at 1900 iterations) \n",
            " Last accuracy mAP@0.5 = 98.78 % \n",
            " 1872: 0.383079, 0.381865 avg loss, 0.001000 rate, 0.369918 seconds, 119808 images\n",
            "Loaded: 1.431669 seconds\n",
            "\n",
            " (next mAP calculation at 1900 iterations) \n",
            " Last accuracy mAP@0.5 = 98.78 % \n",
            " 1873: 0.387739, 0.382453 avg loss, 0.001000 rate, 0.339588 seconds, 119872 images\n",
            "Loaded: 1.660211 seconds\n",
            "\n",
            " (next mAP calculation at 1900 iterations) \n",
            " Last accuracy mAP@0.5 = 98.78 % \n",
            " 1874: 0.318580, 0.376065 avg loss, 0.001000 rate, 0.385699 seconds, 119936 images\n",
            "Loaded: 1.502404 seconds\n",
            "\n",
            " (next mAP calculation at 1900 iterations) \n",
            " Last accuracy mAP@0.5 = 98.78 % \n",
            " 1875: 0.349234, 0.373382 avg loss, 0.001000 rate, 0.359470 seconds, 120000 images\n",
            "Loaded: 1.577804 seconds\n",
            "\n",
            " (next mAP calculation at 1900 iterations) \n",
            " Last accuracy mAP@0.5 = 98.78 % \n",
            " 1876: 0.362265, 0.372271 avg loss, 0.001000 rate, 0.400208 seconds, 120064 images\n",
            "Loaded: 1.423059 seconds\n",
            "\n",
            " (next mAP calculation at 1900 iterations) \n",
            " Last accuracy mAP@0.5 = 98.78 % \n",
            " 1877: 0.301995, 0.365243 avg loss, 0.001000 rate, 0.391504 seconds, 120128 images\n",
            "Loaded: 1.466692 seconds\n",
            "\n",
            " (next mAP calculation at 1900 iterations) \n",
            " Last accuracy mAP@0.5 = 98.78 % \n",
            " 1878: 0.523269, 0.381046 avg loss, 0.001000 rate, 0.369943 seconds, 120192 images\n",
            "Loaded: 1.459262 seconds\n",
            "\n",
            " (next mAP calculation at 1900 iterations) \n",
            " Last accuracy mAP@0.5 = 98.78 % \n",
            " 1879: 0.310956, 0.374037 avg loss, 0.001000 rate, 0.333171 seconds, 120256 images\n",
            "Loaded: 1.482299 seconds\n",
            "\n",
            " (next mAP calculation at 1900 iterations) \n",
            " Last accuracy mAP@0.5 = 98.78 % \n",
            " 1880: 0.394886, 0.376122 avg loss, 0.001000 rate, 0.373530 seconds, 120320 images\n",
            "Resizing\n",
            "352 x 352 \n",
            " try to allocate additional workspace_size = 1264.58 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 1.506519 seconds\n",
            "\n",
            " (next mAP calculation at 1900 iterations) \n",
            " Last accuracy mAP@0.5 = 98.78 % \n",
            " 1881: 0.575336, 0.396043 avg loss, 0.001000 rate, 0.370314 seconds, 120384 images\n",
            "Loaded: 1.374462 seconds\n",
            "\n",
            " (next mAP calculation at 1900 iterations) \n",
            " Last accuracy mAP@0.5 = 98.78 % \n",
            " 1882: 0.360829, 0.392522 avg loss, 0.001000 rate, 0.308813 seconds, 120448 images\n",
            "Loaded: 1.452853 seconds\n",
            "\n",
            " (next mAP calculation at 1900 iterations) \n",
            " Last accuracy mAP@0.5 = 98.78 % \n",
            " 1883: 0.394869, 0.392756 avg loss, 0.001000 rate, 0.313489 seconds, 120512 images\n",
            "Loaded: 1.459781 seconds\n",
            "\n",
            " (next mAP calculation at 1900 iterations) \n",
            " Last accuracy mAP@0.5 = 98.78 % \n",
            " 1884: 0.404594, 0.393940 avg loss, 0.001000 rate, 0.302265 seconds, 120576 images\n",
            "Loaded: 1.405572 seconds\n",
            "\n",
            " (next mAP calculation at 1900 iterations) \n",
            " Last accuracy mAP@0.5 = 98.78 % \n",
            " 1885: 0.411462, 0.395692 avg loss, 0.001000 rate, 0.308659 seconds, 120640 images\n",
            "Loaded: 1.425238 seconds\n",
            "\n",
            " (next mAP calculation at 1900 iterations) \n",
            " Last accuracy mAP@0.5 = 98.78 % \n",
            " 1886: 0.386907, 0.394814 avg loss, 0.001000 rate, 0.306723 seconds, 120704 images\n",
            "Loaded: 1.524660 seconds\n",
            "\n",
            " (next mAP calculation at 1900 iterations) \n",
            " Last accuracy mAP@0.5 = 98.78 % \n",
            " 1887: 0.278084, 0.383141 avg loss, 0.001000 rate, 0.309404 seconds, 120768 images\n",
            "Loaded: 1.503181 seconds\n",
            "\n",
            " (next mAP calculation at 1900 iterations) \n",
            " Last accuracy mAP@0.5 = 98.78 % \n",
            " 1888: 0.330979, 0.377925 avg loss, 0.001000 rate, 0.298707 seconds, 120832 images\n",
            "Loaded: 1.535413 seconds\n",
            "\n",
            " (next mAP calculation at 1900 iterations) \n",
            " Last accuracy mAP@0.5 = 98.78 % \n",
            " 1889: 0.299077, 0.370040 avg loss, 0.001000 rate, 0.292611 seconds, 120896 images\n",
            "Loaded: 1.451974 seconds\n",
            "\n",
            " (next mAP calculation at 1900 iterations) \n",
            " Last accuracy mAP@0.5 = 98.78 % \n",
            " 1890: 0.512605, 0.384296 avg loss, 0.001000 rate, 0.309290 seconds, 120960 images\n",
            "Resizing\n",
            "512 x 512 \n",
            " try to allocate additional workspace_size = 4596.96 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.000043 seconds\n",
            "\n",
            " (next mAP calculation at 1900 iterations) \n",
            " Last accuracy mAP@0.5 = 98.78 % \n",
            " 1891: 0.403463, 0.386213 avg loss, 0.001000 rate, 0.760976 seconds, 121024 images\n",
            "Loaded: 1.514866 seconds\n",
            "\n",
            " (next mAP calculation at 1900 iterations) \n",
            " Last accuracy mAP@0.5 = 98.78 % \n",
            " 1892: 0.393996, 0.386991 avg loss, 0.001000 rate, 0.631350 seconds, 121088 images\n",
            "Loaded: 1.481241 seconds\n",
            "\n",
            " (next mAP calculation at 1900 iterations) \n",
            " Last accuracy mAP@0.5 = 98.78 % \n",
            " 1893: 0.455425, 0.393835 avg loss, 0.001000 rate, 0.690280 seconds, 121152 images\n",
            "Loaded: 1.488634 seconds\n",
            "\n",
            " (next mAP calculation at 1900 iterations) \n",
            " Last accuracy mAP@0.5 = 98.78 % \n",
            " 1894: 0.329790, 0.387430 avg loss, 0.001000 rate, 0.769498 seconds, 121216 images\n",
            "Loaded: 1.281478 seconds\n",
            "\n",
            " (next mAP calculation at 1900 iterations) \n",
            " Last accuracy mAP@0.5 = 98.78 % \n",
            " 1895: 0.545055, 0.403193 avg loss, 0.001000 rate, 0.677404 seconds, 121280 images\n",
            "Loaded: 1.358639 seconds\n",
            "\n",
            " (next mAP calculation at 1900 iterations) \n",
            " Last accuracy mAP@0.5 = 98.78 % \n",
            " 1896: 0.309190, 0.393792 avg loss, 0.001000 rate, 0.708077 seconds, 121344 images\n",
            "Loaded: 1.426054 seconds\n",
            "\n",
            " (next mAP calculation at 1900 iterations) \n",
            " Last accuracy mAP@0.5 = 98.78 % \n",
            " 1897: 0.462704, 0.400684 avg loss, 0.001000 rate, 0.669025 seconds, 121408 images\n",
            "Loaded: 1.416103 seconds\n",
            "\n",
            " (next mAP calculation at 1900 iterations) \n",
            " Last accuracy mAP@0.5 = 98.78 % \n",
            " 1898: 0.364403, 0.397056 avg loss, 0.001000 rate, 0.762184 seconds, 121472 images\n",
            "Loaded: 1.294995 seconds\n",
            "\n",
            " (next mAP calculation at 1900 iterations) \n",
            " Last accuracy mAP@0.5 = 98.78 % \n",
            " 1899: 0.328658, 0.390216 avg loss, 0.001000 rate, 0.658741 seconds, 121536 images\n",
            "Loaded: 1.406518 seconds\n",
            "\n",
            " (next mAP calculation at 1900 iterations) \n",
            " Last accuracy mAP@0.5 = 98.78 % \n",
            " 1900: 0.266876, 0.377882 avg loss, 0.001000 rate, 0.678231 seconds, 121600 images\n",
            "Resizing to initial size: 416 x 416 \n",
            " try to allocate additional workspace_size = 1302.33 MB \n",
            " CUDA allocate done! \n",
            "\n",
            " calculation mAP (mean average precision)...\n",
            "368\n",
            " detections_count = 586, unique_truth_count = 374  \n",
            "class_id = 0, name = banana, ap = 98.59%   \t (TP = 366, FP = 11) \n",
            "\n",
            " for thresh = 0.25, precision = 0.97, recall = 0.98, F1-score = 0.97 \n",
            " for thresh = 0.25, TP = 366, FP = 11, FN = 8, average IoU = 75.29 % \n",
            "\n",
            " IoU threshold = 50 %, used Area-Under-Curve for each unique Recall \n",
            " mean average precision (mAP@0.50) = 0.985876, or 98.59 % \n",
            "Total Detection Time: 16.000000 Seconds\n",
            "\n",
            "Set -points flag:\n",
            " `-points 101` for MS COCO \n",
            " `-points 11` for PascalVOC 2007 (uncomment `difficult` in voc.data) \n",
            " `-points 0` (AUC) for ImageNet, PascalVOC 2010-2012, your custom dataset\n",
            "\n",
            " mean_average_precision (mAP@0.5) = 0.985876 \n",
            "Saving weights to /content/gdrive/My Drive/darknet/backup/yolov3-tiny-280120_last.weights\n",
            "Resizing\n",
            "384 x 384 \n",
            " try to allocate additional workspace_size = 1302.33 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 1.638603 seconds\n",
            "\n",
            " (next mAP calculation at 2000 iterations) \n",
            " Last accuracy mAP@0.5 = 98.59 % \n",
            " 1901: 0.340196, 0.374113 avg loss, 0.001000 rate, 0.415023 seconds, 121664 images\n",
            "Loaded: 1.440424 seconds\n",
            "\n",
            " (next mAP calculation at 2000 iterations) \n",
            " Last accuracy mAP@0.5 = 98.59 % \n",
            " 1902: 0.327532, 0.369455 avg loss, 0.001000 rate, 0.389814 seconds, 121728 images\n",
            "Loaded: 1.497977 seconds\n",
            "\n",
            " (next mAP calculation at 2000 iterations) \n",
            " Last accuracy mAP@0.5 = 98.59 % \n",
            " 1903: 0.358970, 0.368407 avg loss, 0.001000 rate, 0.329663 seconds, 121792 images\n",
            "Loaded: 1.494899 seconds\n",
            "\n",
            " (next mAP calculation at 2000 iterations) \n",
            " Last accuracy mAP@0.5 = 98.59 % \n",
            " 1904: 0.329988, 0.364565 avg loss, 0.001000 rate, 0.352749 seconds, 121856 images\n",
            "Loaded: 1.433416 seconds\n",
            "\n",
            " (next mAP calculation at 2000 iterations) \n",
            " Last accuracy mAP@0.5 = 98.59 % \n",
            " 1905: 0.374532, 0.365561 avg loss, 0.001000 rate, 0.365781 seconds, 121920 images\n",
            "Loaded: 1.533047 seconds\n",
            "\n",
            " (next mAP calculation at 2000 iterations) \n",
            " Last accuracy mAP@0.5 = 98.59 % \n",
            " 1906: 0.278795, 0.356885 avg loss, 0.001000 rate, 0.347857 seconds, 121984 images\n",
            "Loaded: 1.468225 seconds\n",
            "\n",
            " (next mAP calculation at 2000 iterations) \n",
            " Last accuracy mAP@0.5 = 98.59 % \n",
            " 1907: 0.471530, 0.368349 avg loss, 0.001000 rate, 0.345164 seconds, 122048 images\n",
            "Loaded: 1.479257 seconds\n",
            "\n",
            " (next mAP calculation at 2000 iterations) \n",
            " Last accuracy mAP@0.5 = 98.59 % \n",
            " 1908: 0.368640, 0.368378 avg loss, 0.001000 rate, 0.398577 seconds, 122112 images\n",
            "Loaded: 1.396962 seconds\n",
            "\n",
            " (next mAP calculation at 2000 iterations) \n",
            " Last accuracy mAP@0.5 = 98.59 % \n",
            " 1909: 0.382627, 0.369803 avg loss, 0.001000 rate, 0.353753 seconds, 122176 images\n",
            "Loaded: 1.433047 seconds\n",
            "\n",
            " (next mAP calculation at 2000 iterations) \n",
            " Last accuracy mAP@0.5 = 98.59 % \n",
            " 1910: 0.496206, 0.382444 avg loss, 0.001000 rate, 0.377272 seconds, 122240 images\n",
            "Resizing\n",
            "512 x 512 \n",
            " try to allocate additional workspace_size = 4596.96 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.000035 seconds\n",
            "\n",
            " (next mAP calculation at 2000 iterations) \n",
            " Last accuracy mAP@0.5 = 98.59 % \n",
            " 1911: 0.412753, 0.385475 avg loss, 0.001000 rate, 0.690139 seconds, 122304 images\n",
            "Loaded: 1.310708 seconds\n",
            "\n",
            " (next mAP calculation at 2000 iterations) \n",
            " Last accuracy mAP@0.5 = 98.59 % \n",
            " 1912: 0.401567, 0.387084 avg loss, 0.001000 rate, 0.615460 seconds, 122368 images\n",
            "Loaded: 1.415995 seconds\n",
            "\n",
            " (next mAP calculation at 2000 iterations) \n",
            " Last accuracy mAP@0.5 = 98.59 % \n",
            " 1913: 0.431666, 0.391542 avg loss, 0.001000 rate, 0.756012 seconds, 122432 images\n",
            "Loaded: 1.333442 seconds\n",
            "\n",
            " (next mAP calculation at 2000 iterations) \n",
            " Last accuracy mAP@0.5 = 98.59 % \n",
            " 1914: 0.318216, 0.384209 avg loss, 0.001000 rate, 0.777892 seconds, 122496 images\n",
            "Loaded: 1.226046 seconds\n",
            "\n",
            " (next mAP calculation at 2000 iterations) \n",
            " Last accuracy mAP@0.5 = 98.59 % \n",
            " 1915: 0.329630, 0.378751 avg loss, 0.001000 rate, 0.723226 seconds, 122560 images\n",
            "Loaded: 1.343499 seconds\n",
            "\n",
            " (next mAP calculation at 2000 iterations) \n",
            " Last accuracy mAP@0.5 = 98.59 % \n",
            " 1916: 0.329314, 0.373808 avg loss, 0.001000 rate, 0.676896 seconds, 122624 images\n",
            "Loaded: 1.382882 seconds\n",
            "\n",
            " (next mAP calculation at 2000 iterations) \n",
            " Last accuracy mAP@0.5 = 98.59 % \n",
            " 1917: 0.365013, 0.372928 avg loss, 0.001000 rate, 0.680677 seconds, 122688 images\n",
            "Loaded: 1.382752 seconds\n",
            "\n",
            " (next mAP calculation at 2000 iterations) \n",
            " Last accuracy mAP@0.5 = 98.59 % \n",
            " 1918: 0.287233, 0.364359 avg loss, 0.001000 rate, 0.732310 seconds, 122752 images\n",
            "Loaded: 1.317527 seconds\n",
            "\n",
            " (next mAP calculation at 2000 iterations) \n",
            " Last accuracy mAP@0.5 = 98.59 % \n",
            " 1919: 0.320275, 0.359950 avg loss, 0.001000 rate, 0.680557 seconds, 122816 images\n",
            "Loaded: 1.400388 seconds\n",
            "\n",
            " (next mAP calculation at 2000 iterations) \n",
            " Last accuracy mAP@0.5 = 98.59 % \n",
            " 1920: 0.391916, 0.363147 avg loss, 0.001000 rate, 0.665423 seconds, 122880 images\n",
            "Resizing\n",
            "448 x 448 \n",
            " try to allocate additional workspace_size = 1233.65 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 1.570910 seconds\n",
            "\n",
            " (next mAP calculation at 2000 iterations) \n",
            " Last accuracy mAP@0.5 = 98.59 % \n",
            " 1921: 0.380337, 0.364866 avg loss, 0.001000 rate, 0.530123 seconds, 122944 images\n",
            "Loaded: 1.570961 seconds\n",
            "\n",
            " (next mAP calculation at 2000 iterations) \n",
            " Last accuracy mAP@0.5 = 98.59 % \n",
            " 1922: 0.405635, 0.368943 avg loss, 0.001000 rate, 0.504450 seconds, 123008 images\n",
            "Loaded: 1.476806 seconds\n",
            "\n",
            " (next mAP calculation at 2000 iterations) \n",
            " Last accuracy mAP@0.5 = 98.59 % \n",
            " 1923: 0.358683, 0.367917 avg loss, 0.001000 rate, 0.465666 seconds, 123072 images\n",
            "Loaded: 1.454600 seconds\n",
            "\n",
            " (next mAP calculation at 2000 iterations) \n",
            " Last accuracy mAP@0.5 = 98.59 % \n",
            " 1924: 0.277170, 0.358842 avg loss, 0.001000 rate, 0.459844 seconds, 123136 images\n",
            "Loaded: 1.442206 seconds\n",
            "\n",
            " (next mAP calculation at 2000 iterations) \n",
            " Last accuracy mAP@0.5 = 98.59 % \n",
            " 1925: 0.315240, 0.354482 avg loss, 0.001000 rate, 0.474480 seconds, 123200 images\n",
            "Loaded: 1.476055 seconds\n",
            "\n",
            " (next mAP calculation at 2000 iterations) \n",
            " Last accuracy mAP@0.5 = 98.59 % \n",
            " 1926: 0.390192, 0.358053 avg loss, 0.001000 rate, 0.484767 seconds, 123264 images\n",
            "Loaded: 1.487208 seconds\n",
            "\n",
            " (next mAP calculation at 2000 iterations) \n",
            " Last accuracy mAP@0.5 = 98.59 % \n",
            " 1927: 0.339936, 0.356241 avg loss, 0.001000 rate, 0.558701 seconds, 123328 images\n",
            "Loaded: 1.372145 seconds\n",
            "\n",
            " (next mAP calculation at 2000 iterations) \n",
            " Last accuracy mAP@0.5 = 98.59 % \n",
            " 1928: 0.378767, 0.358494 avg loss, 0.001000 rate, 0.503814 seconds, 123392 images\n",
            "Loaded: 1.423203 seconds\n",
            "\n",
            " (next mAP calculation at 2000 iterations) \n",
            " Last accuracy mAP@0.5 = 98.59 % \n",
            " 1929: 0.402211, 0.362865 avg loss, 0.001000 rate, 0.473554 seconds, 123456 images\n",
            "Loaded: 1.549736 seconds\n",
            "\n",
            " (next mAP calculation at 2000 iterations) \n",
            " Last accuracy mAP@0.5 = 98.59 % \n",
            " 1930: 0.289697, 0.355549 avg loss, 0.001000 rate, 0.510892 seconds, 123520 images\n",
            "Resizing\n",
            "544 x 544 \n",
            " try to allocate additional workspace_size = 4601.28 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.000196 seconds\n",
            "\n",
            " (next mAP calculation at 2000 iterations) \n",
            " Last accuracy mAP@0.5 = 98.59 % \n",
            " 1931: 0.352641, 0.355258 avg loss, 0.001000 rate, 0.831617 seconds, 123584 images\n",
            "Loaded: 1.285977 seconds\n",
            "\n",
            " (next mAP calculation at 2000 iterations) \n",
            " Last accuracy mAP@0.5 = 98.59 % \n",
            " 1932: 0.414540, 0.361186 avg loss, 0.001000 rate, 0.813940 seconds, 123648 images\n",
            "Loaded: 1.395143 seconds\n",
            "\n",
            " (next mAP calculation at 2000 iterations) \n",
            " Last accuracy mAP@0.5 = 98.59 % \n",
            " 1933: 0.360261, 0.361094 avg loss, 0.001000 rate, 0.786154 seconds, 123712 images\n",
            "Loaded: 1.349630 seconds\n",
            "\n",
            " (next mAP calculation at 2000 iterations) \n",
            " Last accuracy mAP@0.5 = 98.59 % \n",
            " 1934: 0.350358, 0.360020 avg loss, 0.001000 rate, 0.906303 seconds, 123776 images\n",
            "Loaded: 1.201739 seconds\n",
            "\n",
            " (next mAP calculation at 2000 iterations) \n",
            " Last accuracy mAP@0.5 = 98.59 % \n",
            " 1935: 0.434617, 0.367480 avg loss, 0.001000 rate, 0.772039 seconds, 123840 images\n",
            "Loaded: 1.429449 seconds\n",
            "\n",
            " (next mAP calculation at 2000 iterations) \n",
            " Last accuracy mAP@0.5 = 98.59 % \n",
            " 1936: 0.290894, 0.359821 avg loss, 0.001000 rate, 0.859565 seconds, 123904 images\n",
            "Loaded: 1.355439 seconds\n",
            "\n",
            " (next mAP calculation at 2000 iterations) \n",
            " Last accuracy mAP@0.5 = 98.59 % \n",
            " 1937: 0.288093, 0.352648 avg loss, 0.001000 rate, 0.926437 seconds, 123968 images\n",
            "Loaded: 1.190402 seconds\n",
            "\n",
            " (next mAP calculation at 2000 iterations) \n",
            " Last accuracy mAP@0.5 = 98.59 % \n",
            " 1938: 0.347066, 0.352090 avg loss, 0.001000 rate, 0.865094 seconds, 124032 images\n",
            "Loaded: 1.312033 seconds\n",
            "\n",
            " (next mAP calculation at 2000 iterations) \n",
            " Last accuracy mAP@0.5 = 98.59 % \n",
            " 1939: 0.318320, 0.348713 avg loss, 0.001000 rate, 0.813844 seconds, 124096 images\n",
            "Loaded: 1.306719 seconds\n",
            "\n",
            " (next mAP calculation at 2000 iterations) \n",
            " Last accuracy mAP@0.5 = 98.59 % \n",
            " 1940: 0.340070, 0.347849 avg loss, 0.001000 rate, 0.870089 seconds, 124160 images\n",
            "Resizing\n",
            "480 x 480 \n",
            " try to allocate additional workspace_size = 4592.89 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 1.400031 seconds\n",
            "\n",
            " (next mAP calculation at 2000 iterations) \n",
            " Last accuracy mAP@0.5 = 98.59 % \n",
            " 1941: 0.336983, 0.346762 avg loss, 0.001000 rate, 0.623031 seconds, 124224 images\n",
            "Loaded: 1.675564 seconds\n",
            "\n",
            " (next mAP calculation at 2000 iterations) \n",
            " Last accuracy mAP@0.5 = 98.59 % \n",
            " 1942: 0.358720, 0.347958 avg loss, 0.001000 rate, 0.944043 seconds, 124288 images\n",
            "Loaded: 1.590469 seconds\n",
            "\n",
            " (next mAP calculation at 2000 iterations) \n",
            " Last accuracy mAP@0.5 = 98.59 % \n",
            " 1943: 0.290624, 0.342225 avg loss, 0.001000 rate, 0.612824 seconds, 124352 images\n",
            "Loaded: 1.470339 seconds\n",
            "\n",
            " (next mAP calculation at 2000 iterations) \n",
            " Last accuracy mAP@0.5 = 98.59 % \n",
            " 1944: 0.337203, 0.341723 avg loss, 0.001000 rate, 0.619839 seconds, 124416 images\n",
            "Loaded: 1.392970 seconds\n",
            "\n",
            " (next mAP calculation at 2000 iterations) \n",
            " Last accuracy mAP@0.5 = 98.59 % \n",
            " 1945: 0.343734, 0.341924 avg loss, 0.001000 rate, 0.708418 seconds, 124480 images\n",
            "Loaded: 1.456165 seconds\n",
            "\n",
            " (next mAP calculation at 2000 iterations) \n",
            " Last accuracy mAP@0.5 = 98.59 % \n",
            " 1946: 0.423746, 0.350106 avg loss, 0.001000 rate, 0.592173 seconds, 124544 images\n",
            "Loaded: 1.644377 seconds\n",
            "\n",
            " (next mAP calculation at 2000 iterations) \n",
            " Last accuracy mAP@0.5 = 98.59 % \n",
            " 1947: 0.400195, 0.355115 avg loss, 0.001000 rate, 0.668514 seconds, 124608 images\n",
            "Loaded: 1.354138 seconds\n",
            "\n",
            " (next mAP calculation at 2000 iterations) \n",
            " Last accuracy mAP@0.5 = 98.59 % \n",
            " 1948: 0.288941, 0.348497 avg loss, 0.001000 rate, 0.687150 seconds, 124672 images\n",
            "Loaded: 1.349195 seconds\n",
            "\n",
            " (next mAP calculation at 2000 iterations) \n",
            " Last accuracy mAP@0.5 = 98.59 % \n",
            " 1949: 0.319856, 0.345633 avg loss, 0.001000 rate, 0.655235 seconds, 124736 images\n",
            "Loaded: 1.428667 seconds\n",
            "\n",
            " (next mAP calculation at 2000 iterations) \n",
            " Last accuracy mAP@0.5 = 98.59 % \n",
            " 1950: 0.224184, 0.333488 avg loss, 0.001000 rate, 0.634337 seconds, 124800 images\n",
            "Resizing\n",
            "352 x 352 \n",
            " try to allocate additional workspace_size = 1264.58 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.057832 seconds\n",
            "\n",
            " (next mAP calculation at 2000 iterations) \n",
            " Last accuracy mAP@0.5 = 98.59 % \n",
            " 1951: 0.356927, 0.335832 avg loss, 0.001000 rate, 0.361019 seconds, 124864 images\n",
            "Loaded: 1.438883 seconds\n",
            "\n",
            " (next mAP calculation at 2000 iterations) \n",
            " Last accuracy mAP@0.5 = 98.59 % \n",
            " 1952: 0.332491, 0.335498 avg loss, 0.001000 rate, 0.268136 seconds, 124928 images\n",
            "Loaded: 1.538611 seconds\n",
            "\n",
            " (next mAP calculation at 2000 iterations) \n",
            " Last accuracy mAP@0.5 = 98.59 % \n",
            " 1953: 0.286715, 0.330620 avg loss, 0.001000 rate, 0.313154 seconds, 124992 images\n",
            "Loaded: 1.477185 seconds\n",
            "\n",
            " (next mAP calculation at 2000 iterations) \n",
            " Last accuracy mAP@0.5 = 98.59 % \n",
            " 1954: 0.385084, 0.336066 avg loss, 0.001000 rate, 0.312647 seconds, 125056 images\n",
            "Loaded: 1.457489 seconds\n",
            "\n",
            " (next mAP calculation at 2000 iterations) \n",
            " Last accuracy mAP@0.5 = 98.59 % \n",
            " 1955: 0.445861, 0.347046 avg loss, 0.001000 rate, 0.305624 seconds, 125120 images\n",
            "Loaded: 1.531259 seconds\n",
            "\n",
            " (next mAP calculation at 2000 iterations) \n",
            " Last accuracy mAP@0.5 = 98.59 % \n",
            " 1956: 0.406532, 0.352994 avg loss, 0.001000 rate, 0.299585 seconds, 125184 images\n",
            "Loaded: 1.505915 seconds\n",
            "\n",
            " (next mAP calculation at 2000 iterations) \n",
            " Last accuracy mAP@0.5 = 98.59 % \n",
            " 1957: 0.466377, 0.364333 avg loss, 0.001000 rate, 0.288519 seconds, 125248 images\n",
            "Loaded: 1.476188 seconds\n",
            "\n",
            " (next mAP calculation at 2000 iterations) \n",
            " Last accuracy mAP@0.5 = 98.59 % \n",
            " 1958: 0.309714, 0.358871 avg loss, 0.001000 rate, 0.291064 seconds, 125312 images\n",
            "Loaded: 1.537490 seconds\n",
            "\n",
            " (next mAP calculation at 2000 iterations) \n",
            " Last accuracy mAP@0.5 = 98.59 % \n",
            " 1959: 0.341988, 0.357182 avg loss, 0.001000 rate, 0.305343 seconds, 125376 images\n",
            "Loaded: 1.575322 seconds\n",
            "\n",
            " (next mAP calculation at 2000 iterations) \n",
            " Last accuracy mAP@0.5 = 98.59 % \n",
            " 1960: 0.364611, 0.357925 avg loss, 0.001000 rate, 0.301007 seconds, 125440 images\n",
            "Resizing\n",
            "576 x 576 \n",
            " try to allocate additional workspace_size = 4605.87 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.000039 seconds\n",
            "\n",
            " (next mAP calculation at 2000 iterations) \n",
            " Last accuracy mAP@0.5 = 98.59 % \n",
            " 1961: 0.432571, 0.365390 avg loss, 0.001000 rate, 0.935582 seconds, 125504 images\n",
            "Loaded: 1.429167 seconds\n",
            "\n",
            " (next mAP calculation at 2000 iterations) \n",
            " Last accuracy mAP@0.5 = 98.59 % \n",
            " 1962: 0.431006, 0.371952 avg loss, 0.001000 rate, 0.981079 seconds, 125568 images\n",
            "Loaded: 1.260679 seconds\n",
            "\n",
            " (next mAP calculation at 2000 iterations) \n",
            " Last accuracy mAP@0.5 = 98.59 % \n",
            " 1963: 0.415439, 0.376300 avg loss, 0.001000 rate, 0.842528 seconds, 125632 images\n",
            "Loaded: 1.445286 seconds\n",
            "\n",
            " (next mAP calculation at 2000 iterations) \n",
            " Last accuracy mAP@0.5 = 98.59 % \n",
            " 1964: 0.318543, 0.370525 avg loss, 0.001000 rate, 0.945587 seconds, 125696 images\n",
            "Loaded: 1.215632 seconds\n",
            "\n",
            " (next mAP calculation at 2000 iterations) \n",
            " Last accuracy mAP@0.5 = 98.59 % \n",
            " 1965: 0.375741, 0.371046 avg loss, 0.001000 rate, 0.929827 seconds, 125760 images\n",
            "Loaded: 1.315802 seconds\n",
            "\n",
            " (next mAP calculation at 2000 iterations) \n",
            " Last accuracy mAP@0.5 = 98.59 % \n",
            " 1966: 0.354993, 0.369441 avg loss, 0.001000 rate, 0.921191 seconds, 125824 images\n",
            "Loaded: 1.280865 seconds\n",
            "\n",
            " (next mAP calculation at 2000 iterations) \n",
            " Last accuracy mAP@0.5 = 98.59 % \n",
            " 1967: 0.311842, 0.363681 avg loss, 0.001000 rate, 0.916380 seconds, 125888 images\n",
            "Loaded: 1.348378 seconds\n",
            "\n",
            " (next mAP calculation at 2000 iterations) \n",
            " Last accuracy mAP@0.5 = 98.59 % \n",
            " 1968: 0.362022, 0.363515 avg loss, 0.001000 rate, 0.960388 seconds, 125952 images\n",
            "Loaded: 1.352465 seconds\n",
            "\n",
            " (next mAP calculation at 2000 iterations) \n",
            " Last accuracy mAP@0.5 = 98.59 % \n",
            " 1969: 0.349753, 0.362139 avg loss, 0.001000 rate, 0.998630 seconds, 126016 images\n",
            "Loaded: 1.238783 seconds\n",
            "\n",
            " (next mAP calculation at 2000 iterations) \n",
            " Last accuracy mAP@0.5 = 98.59 % \n",
            " 1970: 0.548504, 0.380775 avg loss, 0.001000 rate, 0.859679 seconds, 126080 images\n",
            "Resizing\n",
            "352 x 352 \n",
            " try to allocate additional workspace_size = 1264.58 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.007677 seconds\n",
            "\n",
            " (next mAP calculation at 2000 iterations) \n",
            " Last accuracy mAP@0.5 = 98.59 % \n",
            " 1971: 0.244718, 0.367170 avg loss, 0.001000 rate, 0.469749 seconds, 126144 images\n",
            "Loaded: 1.467119 seconds\n",
            "\n",
            " (next mAP calculation at 2000 iterations) \n",
            " Last accuracy mAP@0.5 = 98.59 % \n",
            " 1972: 0.295226, 0.359975 avg loss, 0.001000 rate, 0.290210 seconds, 126208 images\n",
            "Loaded: 1.517829 seconds\n",
            "\n",
            " (next mAP calculation at 2000 iterations) \n",
            " Last accuracy mAP@0.5 = 98.59 % \n",
            " 1973: 0.243431, 0.348321 avg loss, 0.001000 rate, 0.294351 seconds, 126272 images\n",
            "Loaded: 1.538416 seconds\n",
            "\n",
            " (next mAP calculation at 2000 iterations) \n",
            " Last accuracy mAP@0.5 = 98.59 % \n",
            " 1974: 0.293315, 0.342820 avg loss, 0.001000 rate, 0.289913 seconds, 126336 images\n",
            "Loaded: 1.555208 seconds\n",
            "\n",
            " (next mAP calculation at 2000 iterations) \n",
            " Last accuracy mAP@0.5 = 98.59 % \n",
            " 1975: 0.372720, 0.345810 avg loss, 0.001000 rate, 0.295940 seconds, 126400 images\n",
            "Loaded: 1.500582 seconds\n",
            "\n",
            " (next mAP calculation at 2000 iterations) \n",
            " Last accuracy mAP@0.5 = 98.59 % \n",
            " 1976: 0.525936, 0.363823 avg loss, 0.001000 rate, 0.290346 seconds, 126464 images\n",
            "Loaded: 1.511728 seconds\n",
            "\n",
            " (next mAP calculation at 2000 iterations) \n",
            " Last accuracy mAP@0.5 = 98.59 % \n",
            " 1977: 0.620785, 0.389519 avg loss, 0.001000 rate, 0.289280 seconds, 126528 images\n",
            "Loaded: 1.523282 seconds\n",
            "\n",
            " (next mAP calculation at 2000 iterations) \n",
            " Last accuracy mAP@0.5 = 98.59 % \n",
            " 1978: 0.418965, 0.392464 avg loss, 0.001000 rate, 0.316482 seconds, 126592 images\n",
            "Loaded: 1.445450 seconds\n",
            "\n",
            " (next mAP calculation at 2000 iterations) \n",
            " Last accuracy mAP@0.5 = 98.59 % \n",
            " 1979: 0.400693, 0.393287 avg loss, 0.001000 rate, 0.298589 seconds, 126656 images\n",
            "Loaded: 1.567890 seconds\n",
            "\n",
            " (next mAP calculation at 2000 iterations) \n",
            " Last accuracy mAP@0.5 = 98.59 % \n",
            " 1980: 0.340210, 0.387979 avg loss, 0.001000 rate, 0.343775 seconds, 126720 images\n",
            "Resizing\n",
            "544 x 544 \n",
            " try to allocate additional workspace_size = 4601.28 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.000038 seconds\n",
            "\n",
            " (next mAP calculation at 2000 iterations) \n",
            " Last accuracy mAP@0.5 = 98.59 % \n",
            " 1981: 0.462015, 0.395383 avg loss, 0.001000 rate, 0.883531 seconds, 126784 images\n",
            "Loaded: 1.339999 seconds\n",
            "\n",
            " (next mAP calculation at 2000 iterations) \n",
            " Last accuracy mAP@0.5 = 98.59 % \n",
            " 1982: 0.405033, 0.396348 avg loss, 0.001000 rate, 0.819983 seconds, 126848 images\n",
            "Loaded: 1.378719 seconds\n",
            "\n",
            " (next mAP calculation at 2000 iterations) \n",
            " Last accuracy mAP@0.5 = 98.59 % \n",
            " 1983: 0.418172, 0.398530 avg loss, 0.001000 rate, 0.818951 seconds, 126912 images\n",
            "Loaded: 1.335523 seconds\n",
            "\n",
            " (next mAP calculation at 2000 iterations) \n",
            " Last accuracy mAP@0.5 = 98.59 % \n",
            " 1984: 0.390196, 0.397697 avg loss, 0.001000 rate, 0.925863 seconds, 126976 images\n",
            "Loaded: 1.178652 seconds\n",
            "\n",
            " (next mAP calculation at 2000 iterations) \n",
            " Last accuracy mAP@0.5 = 98.59 % \n",
            " 1985: 0.392644, 0.397191 avg loss, 0.001000 rate, 0.756940 seconds, 127040 images\n",
            "Loaded: 1.292840 seconds\n",
            "\n",
            " (next mAP calculation at 2000 iterations) \n",
            " Last accuracy mAP@0.5 = 98.59 % \n",
            " 1986: 0.330449, 0.390517 avg loss, 0.001000 rate, 0.741452 seconds, 127104 images\n",
            "Loaded: 1.548012 seconds\n",
            "\n",
            " (next mAP calculation at 2000 iterations) \n",
            " Last accuracy mAP@0.5 = 98.59 % \n",
            " 1987: 0.309451, 0.382411 avg loss, 0.001000 rate, 0.808019 seconds, 127168 images\n",
            "Loaded: 1.404612 seconds\n",
            "\n",
            " (next mAP calculation at 2000 iterations) \n",
            " Last accuracy mAP@0.5 = 98.59 % \n",
            " 1988: 0.343580, 0.378527 avg loss, 0.001000 rate, 0.787653 seconds, 127232 images\n",
            "Loaded: 1.409306 seconds\n",
            "\n",
            " (next mAP calculation at 2000 iterations) \n",
            " Last accuracy mAP@0.5 = 98.59 % \n",
            " 1989: 0.319366, 0.372611 avg loss, 0.001000 rate, 0.793746 seconds, 127296 images\n",
            "Loaded: 1.459509 seconds\n",
            "\n",
            " (next mAP calculation at 2000 iterations) \n",
            " Last accuracy mAP@0.5 = 98.59 % \n",
            " 1990: 0.357649, 0.371115 avg loss, 0.001000 rate, 0.839011 seconds, 127360 images\n",
            "Resizing\n",
            "576 x 576 \n",
            " try to allocate additional workspace_size = 4605.87 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.000050 seconds\n",
            "\n",
            " (next mAP calculation at 2000 iterations) \n",
            " Last accuracy mAP@0.5 = 98.59 % \n",
            " 1991: 0.391639, 0.373167 avg loss, 0.001000 rate, 1.422631 seconds, 127424 images\n",
            "Loaded: 0.984210 seconds\n",
            "\n",
            " (next mAP calculation at 2000 iterations) \n",
            " Last accuracy mAP@0.5 = 98.59 % \n",
            " 1992: 0.390885, 0.374939 avg loss, 0.001000 rate, 0.957141 seconds, 127488 images\n",
            "Loaded: 1.336992 seconds\n",
            "\n",
            " (next mAP calculation at 2000 iterations) \n",
            " Last accuracy mAP@0.5 = 98.59 % \n",
            " 1993: 0.347518, 0.372197 avg loss, 0.001000 rate, 0.878335 seconds, 127552 images\n",
            "Loaded: 1.366237 seconds\n",
            "\n",
            " (next mAP calculation at 2000 iterations) \n",
            " Last accuracy mAP@0.5 = 98.59 % \n",
            " 1994: 0.337245, 0.368702 avg loss, 0.001000 rate, 1.000715 seconds, 127616 images\n",
            "Loaded: 1.252248 seconds\n",
            "\n",
            " (next mAP calculation at 2000 iterations) \n",
            " Last accuracy mAP@0.5 = 98.59 % \n",
            " 1995: 0.351659, 0.366998 avg loss, 0.001000 rate, 0.891665 seconds, 127680 images\n",
            "Loaded: 1.458849 seconds\n",
            "\n",
            " (next mAP calculation at 2000 iterations) \n",
            " Last accuracy mAP@0.5 = 98.59 % \n",
            " 1996: 0.260606, 0.356358 avg loss, 0.001000 rate, 0.882025 seconds, 127744 images\n",
            "Loaded: 1.289519 seconds\n",
            "\n",
            " (next mAP calculation at 2000 iterations) \n",
            " Last accuracy mAP@0.5 = 98.59 % \n",
            " 1997: 0.412626, 0.361985 avg loss, 0.001000 rate, 0.872622 seconds, 127808 images\n",
            "Loaded: 1.324264 seconds\n",
            "\n",
            " (next mAP calculation at 2000 iterations) \n",
            " Last accuracy mAP@0.5 = 98.59 % \n",
            " 1998: 0.335880, 0.359375 avg loss, 0.001000 rate, 0.940992 seconds, 127872 images\n",
            "Loaded: 1.207675 seconds\n",
            "\n",
            " (next mAP calculation at 2000 iterations) \n",
            " Last accuracy mAP@0.5 = 98.59 % \n",
            " 1999: 0.437480, 0.367185 avg loss, 0.001000 rate, 0.848151 seconds, 127936 images\n",
            "Loaded: 1.426097 seconds\n",
            "\n",
            " (next mAP calculation at 2000 iterations) \n",
            " Last accuracy mAP@0.5 = 98.59 % \n",
            " 2000: 0.395337, 0.370000 avg loss, 0.001000 rate, 0.939074 seconds, 128000 images\n",
            "Resizing to initial size: 416 x 416 \n",
            " try to allocate additional workspace_size = 1302.33 MB \n",
            " CUDA allocate done! \n",
            "\n",
            " calculation mAP (mean average precision)...\n",
            "368\n",
            " detections_count = 547, unique_truth_count = 374  \n",
            "class_id = 0, name = banana, ap = 98.47%   \t (TP = 360, FP = 10) \n",
            "\n",
            " for thresh = 0.25, precision = 0.97, recall = 0.96, F1-score = 0.97 \n",
            " for thresh = 0.25, TP = 360, FP = 10, FN = 14, average IoU = 76.54 % \n",
            "\n",
            " IoU threshold = 50 %, used Area-Under-Curve for each unique Recall \n",
            " mean average precision (mAP@0.50) = 0.984723, or 98.47 % \n",
            "Total Detection Time: 15.000000 Seconds\n",
            "\n",
            "Set -points flag:\n",
            " `-points 101` for MS COCO \n",
            " `-points 11` for PascalVOC 2007 (uncomment `difficult` in voc.data) \n",
            " `-points 0` (AUC) for ImageNet, PascalVOC 2010-2012, your custom dataset\n",
            "\n",
            " mean_average_precision (mAP@0.5) = 0.984723 \n",
            "Saving weights to /content/gdrive/My Drive/darknet/backup/yolov3-tiny-280120_2000.weights\n",
            "Saving weights to /content/gdrive/My Drive/darknet/backup/yolov3-tiny-280120_last.weights\n",
            "Resizing\n",
            "480 x 480 \n",
            " try to allocate additional workspace_size = 4592.89 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.000227 seconds\n",
            "\n",
            " (next mAP calculation at 2100 iterations) \n",
            " Last accuracy mAP@0.5 = 98.47 % \n",
            " 2001: 0.335362, 0.366537 avg loss, 0.001000 rate, 0.782068 seconds, 128064 images\n",
            "Loaded: 1.261164 seconds\n",
            "\n",
            " (next mAP calculation at 2100 iterations) \n",
            " Last accuracy mAP@0.5 = 98.47 % \n",
            " 2002: 0.275895, 0.357472 avg loss, 0.001000 rate, 0.713095 seconds, 128128 images\n",
            "Loaded: 1.371781 seconds\n",
            "\n",
            " (next mAP calculation at 2100 iterations) \n",
            " Last accuracy mAP@0.5 = 98.47 % \n",
            " 2003: 0.302517, 0.351977 avg loss, 0.001000 rate, 0.699618 seconds, 128192 images\n",
            "Loaded: 1.344285 seconds\n",
            "\n",
            " (next mAP calculation at 2100 iterations) \n",
            " Last accuracy mAP@0.5 = 98.47 % \n",
            " 2004: 0.383691, 0.355148 avg loss, 0.001000 rate, 0.688235 seconds, 128256 images\n",
            "Loaded: 1.353289 seconds\n",
            "\n",
            " (next mAP calculation at 2100 iterations) \n",
            " Last accuracy mAP@0.5 = 98.47 % \n",
            " 2005: 0.433038, 0.362937 avg loss, 0.001000 rate, 0.631645 seconds, 128320 images\n",
            "Loaded: 1.427719 seconds\n",
            "\n",
            " (next mAP calculation at 2100 iterations) \n",
            " Last accuracy mAP@0.5 = 98.47 % \n",
            " 2006: 0.313877, 0.358031 avg loss, 0.001000 rate, 0.607746 seconds, 128384 images\n",
            "Loaded: 1.419095 seconds\n",
            "\n",
            " (next mAP calculation at 2100 iterations) \n",
            " Last accuracy mAP@0.5 = 98.47 % \n",
            " 2007: 0.362000, 0.358428 avg loss, 0.001000 rate, 0.582187 seconds, 128448 images\n",
            "Loaded: 1.561758 seconds\n",
            "\n",
            " (next mAP calculation at 2100 iterations) \n",
            " Last accuracy mAP@0.5 = 98.47 % \n",
            " 2008: 0.367778, 0.359363 avg loss, 0.001000 rate, 0.631687 seconds, 128512 images\n",
            "Loaded: 1.551858 seconds\n",
            "\n",
            " (next mAP calculation at 2100 iterations) \n",
            " Last accuracy mAP@0.5 = 98.47 % \n",
            " 2009: 0.451361, 0.368563 avg loss, 0.001000 rate, 0.683337 seconds, 128576 images\n",
            "Loaded: 1.343337 seconds\n",
            "\n",
            " (next mAP calculation at 2100 iterations) \n",
            " Last accuracy mAP@0.5 = 98.47 % \n",
            " 2010: 0.230554, 0.354762 avg loss, 0.001000 rate, 0.623825 seconds, 128640 images\n",
            "Resizing\n",
            "352 x 352 \n",
            " try to allocate additional workspace_size = 1264.58 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 1.666903 seconds\n",
            "\n",
            " (next mAP calculation at 2100 iterations) \n",
            " Last accuracy mAP@0.5 = 98.47 % \n",
            " 2011: 0.279092, 0.347195 avg loss, 0.001000 rate, 0.402895 seconds, 128704 images\n",
            "Loaded: 1.492048 seconds\n",
            "\n",
            " (next mAP calculation at 2100 iterations) \n",
            " Last accuracy mAP@0.5 = 98.47 % \n",
            " 2012: 0.331111, 0.345587 avg loss, 0.001000 rate, 0.296242 seconds, 128768 images\n",
            "Loaded: 1.512116 seconds\n",
            "\n",
            " (next mAP calculation at 2100 iterations) \n",
            " Last accuracy mAP@0.5 = 98.47 % \n",
            " 2013: 0.327519, 0.343780 avg loss, 0.001000 rate, 0.293273 seconds, 128832 images\n",
            "Loaded: 1.497456 seconds\n",
            "\n",
            " (next mAP calculation at 2100 iterations) \n",
            " Last accuracy mAP@0.5 = 98.47 % \n",
            " 2014: 0.321320, 0.341534 avg loss, 0.001000 rate, 0.266405 seconds, 128896 images\n",
            "Loaded: 1.555998 seconds\n",
            "\n",
            " (next mAP calculation at 2100 iterations) \n",
            " Last accuracy mAP@0.5 = 98.47 % \n",
            " 2015: 0.347730, 0.342154 avg loss, 0.001000 rate, 0.306764 seconds, 128960 images\n",
            "Loaded: 1.434049 seconds\n",
            "\n",
            " (next mAP calculation at 2100 iterations) \n",
            " Last accuracy mAP@0.5 = 98.47 % \n",
            " 2016: 0.318754, 0.339814 avg loss, 0.001000 rate, 0.302304 seconds, 129024 images\n",
            "Loaded: 1.490702 seconds\n",
            "\n",
            " (next mAP calculation at 2100 iterations) \n",
            " Last accuracy mAP@0.5 = 98.47 % \n",
            " 2017: 0.319448, 0.337777 avg loss, 0.001000 rate, 0.289641 seconds, 129088 images\n",
            "Loaded: 1.471705 seconds\n",
            "\n",
            " (next mAP calculation at 2100 iterations) \n",
            " Last accuracy mAP@0.5 = 98.47 % \n",
            " 2018: 0.403222, 0.344322 avg loss, 0.001000 rate, 0.299462 seconds, 129152 images\n",
            "Loaded: 1.489092 seconds\n",
            "\n",
            " (next mAP calculation at 2100 iterations) \n",
            " Last accuracy mAP@0.5 = 98.47 % \n",
            " 2019: 0.341455, 0.344035 avg loss, 0.001000 rate, 0.293430 seconds, 129216 images\n",
            "Loaded: 1.492010 seconds\n",
            "\n",
            " (next mAP calculation at 2100 iterations) \n",
            " Last accuracy mAP@0.5 = 98.47 % \n",
            " 2020: 0.345532, 0.344185 avg loss, 0.001000 rate, 0.309663 seconds, 129280 images\n",
            "Resizing\n",
            "480 x 480 \n",
            " try to allocate additional workspace_size = 4592.89 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.000033 seconds\n",
            "\n",
            " (next mAP calculation at 2100 iterations) \n",
            " Last accuracy mAP@0.5 = 98.47 % \n",
            " 2021: 0.355152, 0.345281 avg loss, 0.001000 rate, 0.700028 seconds, 129344 images\n",
            "Loaded: 1.305587 seconds\n",
            "\n",
            " (next mAP calculation at 2100 iterations) \n",
            " Last accuracy mAP@0.5 = 98.47 % \n",
            " 2022: 0.362382, 0.346991 avg loss, 0.001000 rate, 0.586923 seconds, 129408 images\n",
            "Loaded: 1.441507 seconds\n",
            "\n",
            " (next mAP calculation at 2100 iterations) \n",
            " Last accuracy mAP@0.5 = 98.47 % \n",
            " 2023: 0.303411, 0.342633 avg loss, 0.001000 rate, 0.586288 seconds, 129472 images\n",
            "Loaded: 1.434229 seconds\n",
            "\n",
            " (next mAP calculation at 2100 iterations) \n",
            " Last accuracy mAP@0.5 = 98.47 % \n",
            " 2024: 0.323042, 0.340674 avg loss, 0.001000 rate, 0.575224 seconds, 129536 images\n",
            "Loaded: 1.484318 seconds\n",
            "\n",
            " (next mAP calculation at 2100 iterations) \n",
            " Last accuracy mAP@0.5 = 98.47 % \n",
            " 2025: 0.302101, 0.336817 avg loss, 0.001000 rate, 0.596862 seconds, 129600 images\n",
            "Loaded: 1.358326 seconds\n",
            "\n",
            " (next mAP calculation at 2100 iterations) \n",
            " Last accuracy mAP@0.5 = 98.47 % \n",
            " 2026: 0.371748, 0.340310 avg loss, 0.001000 rate, 0.590127 seconds, 129664 images\n",
            "Loaded: 1.373716 seconds\n",
            "\n",
            " (next mAP calculation at 2100 iterations) \n",
            " Last accuracy mAP@0.5 = 98.47 % \n",
            " 2027: 0.287875, 0.335066 avg loss, 0.001000 rate, 0.619004 seconds, 129728 images\n",
            "Loaded: 1.346413 seconds\n",
            "\n",
            " (next mAP calculation at 2100 iterations) \n",
            " Last accuracy mAP@0.5 = 98.47 % \n",
            " 2028: 0.474731, 0.349033 avg loss, 0.001000 rate, 0.581537 seconds, 129792 images\n",
            "Loaded: 1.560984 seconds\n",
            "\n",
            " (next mAP calculation at 2100 iterations) \n",
            " Last accuracy mAP@0.5 = 98.47 % \n",
            " 2029: 0.332913, 0.347421 avg loss, 0.001000 rate, 0.567002 seconds, 129856 images\n",
            "Loaded: 1.472596 seconds\n",
            "\n",
            " (next mAP calculation at 2100 iterations) \n",
            " Last accuracy mAP@0.5 = 98.47 % \n",
            " 2030: 0.378471, 0.350526 avg loss, 0.001000 rate, 0.587832 seconds, 129920 images\n",
            "Resizing\n",
            "512 x 512 \n",
            " try to allocate additional workspace_size = 4596.96 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.000037 seconds\n",
            "\n",
            " (next mAP calculation at 2100 iterations) \n",
            " Last accuracy mAP@0.5 = 98.47 % \n",
            " 2031: 0.409012, 0.356375 avg loss, 0.001000 rate, 0.716956 seconds, 129984 images\n",
            "Loaded: 1.341615 seconds\n",
            "\n",
            " (next mAP calculation at 2100 iterations) \n",
            " Last accuracy mAP@0.5 = 98.47 % \n",
            " 2032: 0.352829, 0.356020 avg loss, 0.001000 rate, 0.668324 seconds, 130048 images\n",
            "Loaded: 1.347765 seconds\n",
            "\n",
            " (next mAP calculation at 2100 iterations) \n",
            " Last accuracy mAP@0.5 = 98.47 % \n",
            " 2033: 0.361357, 0.356554 avg loss, 0.001000 rate, 0.752280 seconds, 130112 images\n",
            "Loaded: 1.399669 seconds\n",
            "\n",
            " (next mAP calculation at 2100 iterations) \n",
            " Last accuracy mAP@0.5 = 98.47 % \n",
            " 2034: 0.338014, 0.354700 avg loss, 0.001000 rate, 0.766190 seconds, 130176 images\n",
            "Loaded: 1.319678 seconds\n",
            "\n",
            " (next mAP calculation at 2100 iterations) \n",
            " Last accuracy mAP@0.5 = 98.47 % \n",
            " 2035: 0.357326, 0.354962 avg loss, 0.001000 rate, 0.654053 seconds, 130240 images\n",
            "Loaded: 1.446984 seconds\n",
            "\n",
            " (next mAP calculation at 2100 iterations) \n",
            " Last accuracy mAP@0.5 = 98.47 % \n",
            " 2036: 0.327078, 0.352174 avg loss, 0.001000 rate, 0.701967 seconds, 130304 images\n",
            "Loaded: 1.377858 seconds\n",
            "\n",
            " (next mAP calculation at 2100 iterations) \n",
            " Last accuracy mAP@0.5 = 98.47 % \n",
            " 2037: 0.296856, 0.346642 avg loss, 0.001000 rate, 0.659890 seconds, 130368 images\n",
            "Loaded: 1.452728 seconds\n",
            "\n",
            " (next mAP calculation at 2100 iterations) \n",
            " Last accuracy mAP@0.5 = 98.47 % \n",
            " 2038: 0.424498, 0.354428 avg loss, 0.001000 rate, 0.822367 seconds, 130432 images\n",
            "Loaded: 1.285062 seconds\n",
            "\n",
            " (next mAP calculation at 2100 iterations) \n",
            " Last accuracy mAP@0.5 = 98.47 % \n",
            " 2039: 0.348565, 0.353841 avg loss, 0.001000 rate, 0.652741 seconds, 130496 images\n",
            "Loaded: 1.389265 seconds\n",
            "\n",
            " (next mAP calculation at 2100 iterations) \n",
            " Last accuracy mAP@0.5 = 98.47 % \n",
            " 2040: 0.392189, 0.357676 avg loss, 0.001000 rate, 0.722312 seconds, 130560 images\n",
            "Resizing\n",
            "576 x 576 \n",
            " try to allocate additional workspace_size = 4605.87 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.000046 seconds\n",
            "\n",
            " (next mAP calculation at 2100 iterations) \n",
            " Last accuracy mAP@0.5 = 98.47 % \n",
            " 2041: 0.293820, 0.351291 avg loss, 0.001000 rate, 0.933039 seconds, 130624 images\n",
            "Loaded: 1.283521 seconds\n",
            "\n",
            " (next mAP calculation at 2100 iterations) \n",
            " Last accuracy mAP@0.5 = 98.47 % \n",
            " 2042: 0.399531, 0.356115 avg loss, 0.001000 rate, 0.896771 seconds, 130688 images\n",
            "Loaded: 1.316006 seconds\n",
            "\n",
            " (next mAP calculation at 2100 iterations) \n",
            " Last accuracy mAP@0.5 = 98.47 % \n",
            " 2043: 0.386990, 0.359202 avg loss, 0.001000 rate, 0.854235 seconds, 130752 images\n",
            "Loaded: 1.389317 seconds\n",
            "\n",
            " (next mAP calculation at 2100 iterations) \n",
            " Last accuracy mAP@0.5 = 98.47 % \n",
            " 2044: 0.417077, 0.364990 avg loss, 0.001000 rate, 0.985818 seconds, 130816 images\n",
            "Loaded: 1.190151 seconds\n",
            "\n",
            " (next mAP calculation at 2100 iterations) \n",
            " Last accuracy mAP@0.5 = 98.47 % \n",
            " 2045: 0.400101, 0.368501 avg loss, 0.001000 rate, 0.891875 seconds, 130880 images\n",
            "Loaded: 1.308061 seconds\n",
            "\n",
            " (next mAP calculation at 2100 iterations) \n",
            " Last accuracy mAP@0.5 = 98.47 % \n",
            " 2046: 0.379546, 0.369605 avg loss, 0.001000 rate, 0.948991 seconds, 130944 images\n",
            "Loaded: 1.264578 seconds\n",
            "\n",
            " (next mAP calculation at 2100 iterations) \n",
            " Last accuracy mAP@0.5 = 98.47 % \n",
            " 2047: 0.384948, 0.371140 avg loss, 0.001000 rate, 0.865200 seconds, 131008 images\n",
            "Loaded: 1.281763 seconds\n",
            "\n",
            " (next mAP calculation at 2100 iterations) \n",
            " Last accuracy mAP@0.5 = 98.47 % \n",
            " 2048: 0.308259, 0.364852 avg loss, 0.001000 rate, 0.966444 seconds, 131072 images\n",
            "Loaded: 1.271331 seconds\n",
            "\n",
            " (next mAP calculation at 2100 iterations) \n",
            " Last accuracy mAP@0.5 = 98.47 % \n",
            " 2049: 0.391095, 0.367476 avg loss, 0.001000 rate, 0.871805 seconds, 131136 images\n",
            "Loaded: 1.402988 seconds\n",
            "\n",
            " (next mAP calculation at 2100 iterations) \n",
            " Last accuracy mAP@0.5 = 98.47 % \n",
            " 2050: 0.291470, 0.359875 avg loss, 0.001000 rate, 0.865847 seconds, 131200 images\n",
            "Resizing\n",
            "576 x 576 \n",
            " try to allocate additional workspace_size = 4605.87 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 1.662955 seconds\n",
            "\n",
            " (next mAP calculation at 2100 iterations) \n",
            " Last accuracy mAP@0.5 = 98.47 % \n",
            " 2051: 0.291107, 0.352998 avg loss, 0.001000 rate, 1.052526 seconds, 131264 images\n",
            "Loaded: 1.124198 seconds\n",
            "\n",
            " (next mAP calculation at 2100 iterations) \n",
            " Last accuracy mAP@0.5 = 98.47 % \n",
            " 2052: 0.405852, 0.358284 avg loss, 0.001000 rate, 0.882487 seconds, 131328 images\n",
            "Loaded: 1.316698 seconds\n",
            "\n",
            " (next mAP calculation at 2100 iterations) \n",
            " Last accuracy mAP@0.5 = 98.47 % \n",
            " 2053: 0.363809, 0.358836 avg loss, 0.001000 rate, 0.818802 seconds, 131392 images\n",
            "Loaded: 1.391994 seconds\n",
            "\n",
            " (next mAP calculation at 2100 iterations) \n",
            " Last accuracy mAP@0.5 = 98.47 % \n",
            " 2054: 0.356979, 0.358651 avg loss, 0.001000 rate, 0.945017 seconds, 131456 images\n",
            "Loaded: 1.225036 seconds\n",
            "\n",
            " (next mAP calculation at 2100 iterations) \n",
            " Last accuracy mAP@0.5 = 98.47 % \n",
            " 2055: 0.305948, 0.353380 avg loss, 0.001000 rate, 0.994708 seconds, 131520 images\n",
            "Loaded: 1.192240 seconds\n",
            "\n",
            " (next mAP calculation at 2100 iterations) \n",
            " Last accuracy mAP@0.5 = 98.47 % \n",
            " 2056: 0.466035, 0.364646 avg loss, 0.001000 rate, 0.916227 seconds, 131584 images\n",
            "Loaded: 1.268800 seconds\n",
            "\n",
            " (next mAP calculation at 2100 iterations) \n",
            " Last accuracy mAP@0.5 = 98.47 % \n",
            " 2057: 0.266869, 0.354868 avg loss, 0.001000 rate, 0.906884 seconds, 131648 images\n",
            "Loaded: 1.415969 seconds\n",
            "\n",
            " (next mAP calculation at 2100 iterations) \n",
            " Last accuracy mAP@0.5 = 98.47 % \n",
            " 2058: 0.344977, 0.353879 avg loss, 0.001000 rate, 0.967504 seconds, 131712 images\n",
            "Loaded: 1.254851 seconds\n",
            "\n",
            " (next mAP calculation at 2100 iterations) \n",
            " Last accuracy mAP@0.5 = 98.47 % \n",
            " 2059: 0.336110, 0.352102 avg loss, 0.001000 rate, 0.860777 seconds, 131776 images\n",
            "Loaded: 1.334692 seconds\n",
            "\n",
            " (next mAP calculation at 2100 iterations) \n",
            " Last accuracy mAP@0.5 = 98.47 % \n",
            " 2060: 0.370199, 0.353912 avg loss, 0.001000 rate, 0.975201 seconds, 131840 images\n",
            "Resizing\n",
            "608 x 608 \n",
            " try to allocate additional workspace_size = 4610.72 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.000045 seconds\n",
            "\n",
            " (next mAP calculation at 2100 iterations) \n",
            " Last accuracy mAP@0.5 = 98.47 % \n",
            " 2061: 0.312052, 0.349726 avg loss, 0.001000 rate, 1.061841 seconds, 131904 images\n",
            "Loaded: 1.459274 seconds\n",
            "\n",
            " (next mAP calculation at 2100 iterations) \n",
            " Last accuracy mAP@0.5 = 98.47 % \n",
            " 2062: 0.423415, 0.357095 avg loss, 0.001000 rate, 0.998732 seconds, 131968 images\n",
            "Loaded: 1.393473 seconds\n",
            "\n",
            " (next mAP calculation at 2100 iterations) \n",
            " Last accuracy mAP@0.5 = 98.47 % \n",
            " 2063: 0.288680, 0.350253 avg loss, 0.001000 rate, 0.974127 seconds, 132032 images\n",
            "Loaded: 1.506628 seconds\n",
            "\n",
            " (next mAP calculation at 2100 iterations) \n",
            " Last accuracy mAP@0.5 = 98.47 % \n",
            " 2064: 0.365735, 0.351801 avg loss, 0.001000 rate, 0.970354 seconds, 132096 images\n",
            "Loaded: 1.400236 seconds\n",
            "\n",
            " (next mAP calculation at 2100 iterations) \n",
            " Last accuracy mAP@0.5 = 98.47 % \n",
            " 2065: 0.375707, 0.354192 avg loss, 0.001000 rate, 1.046182 seconds, 132160 images\n",
            "Loaded: 1.401637 seconds\n",
            "\n",
            " (next mAP calculation at 2100 iterations) \n",
            " Last accuracy mAP@0.5 = 98.47 % \n",
            " 2066: 0.522821, 0.371055 avg loss, 0.001000 rate, 1.101287 seconds, 132224 images\n",
            "Loaded: 1.263106 seconds\n",
            "\n",
            " (next mAP calculation at 2100 iterations) \n",
            " Last accuracy mAP@0.5 = 98.47 % \n",
            " 2067: 0.450413, 0.378991 avg loss, 0.001000 rate, 1.041129 seconds, 132288 images\n",
            "Loaded: 1.239534 seconds\n",
            "\n",
            " (next mAP calculation at 2100 iterations) \n",
            " Last accuracy mAP@0.5 = 98.47 % \n",
            " 2068: 0.345127, 0.375604 avg loss, 0.001000 rate, 1.011031 seconds, 132352 images\n",
            "Loaded: 1.376175 seconds\n",
            "\n",
            " (next mAP calculation at 2100 iterations) \n",
            " Last accuracy mAP@0.5 = 98.47 % \n",
            " 2069: 0.478845, 0.385928 avg loss, 0.001000 rate, 1.014586 seconds, 132416 images\n",
            "Loaded: 1.337304 seconds\n",
            "\n",
            " (next mAP calculation at 2100 iterations) \n",
            " Last accuracy mAP@0.5 = 98.47 % \n",
            " 2070: 0.358498, 0.383185 avg loss, 0.001000 rate, 1.098129 seconds, 132480 images\n",
            "Resizing\n",
            "448 x 448 \n",
            " try to allocate additional workspace_size = 1233.65 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.000040 seconds\n",
            "\n",
            " (next mAP calculation at 2100 iterations) \n",
            " Last accuracy mAP@0.5 = 98.47 % \n",
            " 2071: 0.371741, 0.382041 avg loss, 0.001000 rate, 0.616935 seconds, 132544 images\n",
            "Loaded: 1.415262 seconds\n",
            "\n",
            " (next mAP calculation at 2100 iterations) \n",
            " Last accuracy mAP@0.5 = 98.47 % \n",
            " 2072: 0.283223, 0.372159 avg loss, 0.001000 rate, 0.496615 seconds, 132608 images\n",
            "Loaded: 1.529348 seconds\n",
            "\n",
            " (next mAP calculation at 2100 iterations) \n",
            " Last accuracy mAP@0.5 = 98.47 % \n",
            " 2073: 0.318152, 0.366758 avg loss, 0.001000 rate, 0.487359 seconds, 132672 images\n",
            "Loaded: 1.474830 seconds\n",
            "\n",
            " (next mAP calculation at 2100 iterations) \n",
            " Last accuracy mAP@0.5 = 98.47 % \n",
            " 2074: 0.254141, 0.355497 avg loss, 0.001000 rate, 0.497250 seconds, 132736 images\n",
            "Loaded: 1.563316 seconds\n",
            "\n",
            " (next mAP calculation at 2100 iterations) \n",
            " Last accuracy mAP@0.5 = 98.47 % \n",
            " 2075: 0.339202, 0.353867 avg loss, 0.001000 rate, 0.505140 seconds, 132800 images\n",
            "Loaded: 1.552723 seconds\n",
            "\n",
            " (next mAP calculation at 2100 iterations) \n",
            " Last accuracy mAP@0.5 = 98.47 % \n",
            " 2076: 0.362921, 0.354773 avg loss, 0.001000 rate, 0.482538 seconds, 132864 images\n",
            "Loaded: 1.566481 seconds\n",
            "\n",
            " (next mAP calculation at 2100 iterations) \n",
            " Last accuracy mAP@0.5 = 98.47 % \n",
            " 2077: 0.337883, 0.353084 avg loss, 0.001000 rate, 0.512359 seconds, 132928 images\n",
            "Loaded: 1.632620 seconds\n",
            "\n",
            " (next mAP calculation at 2100 iterations) \n",
            " Last accuracy mAP@0.5 = 98.47 % \n",
            " 2078: 0.469515, 0.364727 avg loss, 0.001000 rate, 0.567631 seconds, 132992 images\n",
            "Loaded: 1.472771 seconds\n",
            "\n",
            " (next mAP calculation at 2100 iterations) \n",
            " Last accuracy mAP@0.5 = 98.47 % \n",
            " 2079: 0.307341, 0.358988 avg loss, 0.001000 rate, 0.530003 seconds, 133056 images\n",
            "Loaded: 1.504644 seconds\n",
            "\n",
            " (next mAP calculation at 2100 iterations) \n",
            " Last accuracy mAP@0.5 = 98.47 % \n",
            " 2080: 0.400117, 0.363101 avg loss, 0.001000 rate, 0.477128 seconds, 133120 images\n",
            "Resizing\n",
            "384 x 384 \n",
            " try to allocate additional workspace_size = 1302.33 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 1.523238 seconds\n",
            "\n",
            " (next mAP calculation at 2100 iterations) \n",
            " Last accuracy mAP@0.5 = 98.47 % \n",
            " 2081: 0.275205, 0.354311 avg loss, 0.001000 rate, 0.438949 seconds, 133184 images\n",
            "Loaded: 1.438316 seconds\n",
            "\n",
            " (next mAP calculation at 2100 iterations) \n",
            " Last accuracy mAP@0.5 = 98.47 % \n",
            " 2082: 0.315926, 0.350473 avg loss, 0.001000 rate, 0.370086 seconds, 133248 images\n",
            "Loaded: 1.450437 seconds\n",
            "\n",
            " (next mAP calculation at 2100 iterations) \n",
            " Last accuracy mAP@0.5 = 98.47 % \n",
            " 2083: 0.358009, 0.351226 avg loss, 0.001000 rate, 0.350131 seconds, 133312 images\n",
            "Loaded: 1.460948 seconds\n",
            "\n",
            " (next mAP calculation at 2100 iterations) \n",
            " Last accuracy mAP@0.5 = 98.47 % \n",
            " 2084: 0.273917, 0.343496 avg loss, 0.001000 rate, 0.380559 seconds, 133376 images\n",
            "Loaded: 1.525136 seconds\n",
            "\n",
            " (next mAP calculation at 2100 iterations) \n",
            " Last accuracy mAP@0.5 = 98.47 % \n",
            " 2085: 0.440972, 0.353243 avg loss, 0.001000 rate, 0.420040 seconds, 133440 images\n",
            "Loaded: 1.517773 seconds\n",
            "\n",
            " (next mAP calculation at 2100 iterations) \n",
            " Last accuracy mAP@0.5 = 98.47 % \n",
            " 2086: 0.370380, 0.354957 avg loss, 0.001000 rate, 0.367044 seconds, 133504 images\n",
            "Loaded: 1.523597 seconds\n",
            "\n",
            " (next mAP calculation at 2100 iterations) \n",
            " Last accuracy mAP@0.5 = 98.47 % \n",
            " 2087: 0.326704, 0.352132 avg loss, 0.001000 rate, 0.375484 seconds, 133568 images\n",
            "Loaded: 1.437183 seconds\n",
            "\n",
            " (next mAP calculation at 2100 iterations) \n",
            " Last accuracy mAP@0.5 = 98.47 % \n",
            " 2088: 0.322260, 0.349145 avg loss, 0.001000 rate, 0.372175 seconds, 133632 images\n",
            "Loaded: 1.459112 seconds\n",
            "\n",
            " (next mAP calculation at 2100 iterations) \n",
            " Last accuracy mAP@0.5 = 98.47 % \n",
            " 2089: 0.283934, 0.342624 avg loss, 0.001000 rate, 0.372952 seconds, 133696 images\n",
            "Loaded: 1.417532 seconds\n",
            "\n",
            " (next mAP calculation at 2100 iterations) \n",
            " Last accuracy mAP@0.5 = 98.47 % \n",
            " 2090: 0.306435, 0.339005 avg loss, 0.001000 rate, 0.349072 seconds, 133760 images\n",
            "Resizing\n",
            "384 x 384 \n",
            " try to allocate additional workspace_size = 1302.33 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 1.603057 seconds\n",
            "\n",
            " (next mAP calculation at 2100 iterations) \n",
            " Last accuracy mAP@0.5 = 98.47 % \n",
            " 2091: 0.298960, 0.335000 avg loss, 0.001000 rate, 0.455621 seconds, 133824 images\n",
            "Loaded: 1.392729 seconds\n",
            "\n",
            " (next mAP calculation at 2100 iterations) \n",
            " Last accuracy mAP@0.5 = 98.47 % \n",
            " 2092: 0.446765, 0.346177 avg loss, 0.001000 rate, 0.382219 seconds, 133888 images\n",
            "Loaded: 1.582074 seconds\n",
            "\n",
            " (next mAP calculation at 2100 iterations) \n",
            " Last accuracy mAP@0.5 = 98.47 % \n",
            " 2093: 0.300315, 0.341591 avg loss, 0.001000 rate, 0.360707 seconds, 133952 images\n",
            "Loaded: 1.515488 seconds\n",
            "\n",
            " (next mAP calculation at 2100 iterations) \n",
            " Last accuracy mAP@0.5 = 98.47 % \n",
            " 2094: 0.353547, 0.342786 avg loss, 0.001000 rate, 0.365343 seconds, 134016 images\n",
            "Loaded: 1.459354 seconds\n",
            "\n",
            " (next mAP calculation at 2100 iterations) \n",
            " Last accuracy mAP@0.5 = 98.47 % \n",
            " 2095: 0.298015, 0.338309 avg loss, 0.001000 rate, 0.417878 seconds, 134080 images\n",
            "Loaded: 1.410244 seconds\n",
            "\n",
            " (next mAP calculation at 2100 iterations) \n",
            " Last accuracy mAP@0.5 = 98.47 % \n",
            " 2096: 0.273526, 0.331831 avg loss, 0.001000 rate, 0.402906 seconds, 134144 images\n",
            "Loaded: 1.380432 seconds\n",
            "\n",
            " (next mAP calculation at 2100 iterations) \n",
            " Last accuracy mAP@0.5 = 98.47 % \n",
            " 2097: 0.412437, 0.339891 avg loss, 0.001000 rate, 0.355546 seconds, 134208 images\n",
            "Loaded: 1.455869 seconds\n",
            "\n",
            " (next mAP calculation at 2100 iterations) \n",
            " Last accuracy mAP@0.5 = 98.47 % \n",
            " 2098: 0.418637, 0.347766 avg loss, 0.001000 rate, 0.344126 seconds, 134272 images\n",
            "Loaded: 1.584575 seconds\n",
            "\n",
            " (next mAP calculation at 2100 iterations) \n",
            " Last accuracy mAP@0.5 = 98.47 % \n",
            " 2099: 0.454408, 0.358430 avg loss, 0.001000 rate, 0.352155 seconds, 134336 images\n",
            "Loaded: 1.558542 seconds\n",
            "\n",
            " (next mAP calculation at 2100 iterations) \n",
            " Last accuracy mAP@0.5 = 98.47 % \n",
            " 2100: 0.357193, 0.358306 avg loss, 0.001000 rate, 0.353809 seconds, 134400 images\n",
            "Resizing to initial size: 416 x 416 \n",
            " try to allocate additional workspace_size = 1302.33 MB \n",
            " CUDA allocate done! \n",
            "\n",
            " calculation mAP (mean average precision)...\n",
            "368\n",
            " detections_count = 562, unique_truth_count = 374  \n",
            "class_id = 0, name = banana, ap = 98.73%   \t (TP = 366, FP = 8) \n",
            "\n",
            " for thresh = 0.25, precision = 0.98, recall = 0.98, F1-score = 0.98 \n",
            " for thresh = 0.25, TP = 366, FP = 8, FN = 8, average IoU = 76.80 % \n",
            "\n",
            " IoU threshold = 50 %, used Area-Under-Curve for each unique Recall \n",
            " mean average precision (mAP@0.50) = 0.987278, or 98.73 % \n",
            "Total Detection Time: 16.000000 Seconds\n",
            "\n",
            "Set -points flag:\n",
            " `-points 101` for MS COCO \n",
            " `-points 11` for PascalVOC 2007 (uncomment `difficult` in voc.data) \n",
            " `-points 0` (AUC) for ImageNet, PascalVOC 2010-2012, your custom dataset\n",
            "\n",
            " mean_average_precision (mAP@0.5) = 0.987278 \n",
            "Saving weights to /content/gdrive/My Drive/darknet/backup/yolov3-tiny-280120_last.weights\n",
            "Resizing\n",
            "608 x 608 \n",
            " try to allocate additional workspace_size = 4610.72 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.000040 seconds\n",
            "\n",
            " (next mAP calculation at 2200 iterations) \n",
            " Last accuracy mAP@0.5 = 98.73 % \n",
            " 2101: 0.323033, 0.354779 avg loss, 0.001000 rate, 1.132508 seconds, 134464 images\n",
            "Loaded: 1.237124 seconds\n",
            "\n",
            " (next mAP calculation at 2200 iterations) \n",
            " Last accuracy mAP@0.5 = 98.73 % \n",
            " 2102: 0.472245, 0.366526 avg loss, 0.001000 rate, 1.007138 seconds, 134528 images\n",
            "Loaded: 1.314186 seconds\n",
            "\n",
            " (next mAP calculation at 2200 iterations) \n",
            " Last accuracy mAP@0.5 = 98.73 % \n",
            " 2103: 0.351346, 0.365008 avg loss, 0.001000 rate, 0.983884 seconds, 134592 images\n",
            "Loaded: 1.338803 seconds\n",
            "\n",
            " (next mAP calculation at 2200 iterations) \n",
            " Last accuracy mAP@0.5 = 98.73 % \n",
            " 2104: 0.370706, 0.365578 avg loss, 0.001000 rate, 0.999094 seconds, 134656 images\n",
            "Loaded: 1.308085 seconds\n",
            "\n",
            " (next mAP calculation at 2200 iterations) \n",
            " Last accuracy mAP@0.5 = 98.73 % \n",
            " 2105: 0.474809, 0.376501 avg loss, 0.001000 rate, 1.039222 seconds, 134720 images\n",
            "Loaded: 1.302899 seconds\n",
            "\n",
            " (next mAP calculation at 2200 iterations) \n",
            " Last accuracy mAP@0.5 = 98.73 % \n",
            " 2106: 0.350862, 0.373937 avg loss, 0.001000 rate, 1.018648 seconds, 134784 images\n",
            "Loaded: 1.232779 seconds\n",
            "\n",
            " (next mAP calculation at 2200 iterations) \n",
            " Last accuracy mAP@0.5 = 98.73 % \n",
            " 2107: 0.414803, 0.378023 avg loss, 0.001000 rate, 1.150011 seconds, 134848 images\n",
            "Loaded: 1.135460 seconds\n",
            "\n",
            " (next mAP calculation at 2200 iterations) \n",
            " Last accuracy mAP@0.5 = 98.73 % \n",
            " 2108: 0.363227, 0.376544 avg loss, 0.001000 rate, 1.003373 seconds, 134912 images\n",
            "Loaded: 1.235866 seconds\n",
            "\n",
            " (next mAP calculation at 2200 iterations) \n",
            " Last accuracy mAP@0.5 = 98.73 % \n",
            " 2109: 0.345959, 0.373485 avg loss, 0.001000 rate, 0.983238 seconds, 134976 images\n",
            "Loaded: 1.321666 seconds\n",
            "\n",
            " (next mAP calculation at 2200 iterations) \n",
            " Last accuracy mAP@0.5 = 98.73 % \n",
            " 2110: 0.379997, 0.374136 avg loss, 0.001000 rate, 1.003282 seconds, 135040 images\n",
            "Resizing\n",
            "608 x 608 \n",
            " try to allocate additional workspace_size = 4610.72 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 1.331971 seconds\n",
            "\n",
            " (next mAP calculation at 2200 iterations) \n",
            " Last accuracy mAP@0.5 = 98.73 % \n",
            " 2111: 0.282279, 0.364951 avg loss, 0.001000 rate, 1.203893 seconds, 135104 images\n",
            "Loaded: 1.100054 seconds\n",
            "\n",
            " (next mAP calculation at 2200 iterations) \n",
            " Last accuracy mAP@0.5 = 98.73 % \n",
            " 2112: 0.363214, 0.364777 avg loss, 0.001000 rate, 1.021999 seconds, 135168 images\n",
            "Loaded: 1.253016 seconds\n",
            "\n",
            " (next mAP calculation at 2200 iterations) \n",
            " Last accuracy mAP@0.5 = 98.73 % \n",
            " 2113: 0.348483, 0.363148 avg loss, 0.001000 rate, 1.055710 seconds, 135232 images\n",
            "Loaded: 1.348081 seconds\n",
            "\n",
            " (next mAP calculation at 2200 iterations) \n",
            " Last accuracy mAP@0.5 = 98.73 % \n",
            " 2114: 0.364653, 0.363298 avg loss, 0.001000 rate, 1.141906 seconds, 135296 images\n",
            "Loaded: 1.097639 seconds\n",
            "\n",
            " (next mAP calculation at 2200 iterations) \n",
            " Last accuracy mAP@0.5 = 98.73 % \n",
            " 2115: 0.295162, 0.356485 avg loss, 0.001000 rate, 1.076420 seconds, 135360 images\n",
            "Loaded: 1.225798 seconds\n",
            "\n",
            " (next mAP calculation at 2200 iterations) \n",
            " Last accuracy mAP@0.5 = 98.73 % \n",
            " 2116: 0.352549, 0.356091 avg loss, 0.001000 rate, 1.076791 seconds, 135424 images\n",
            "Loaded: 1.185925 seconds\n",
            "\n",
            " (next mAP calculation at 2200 iterations) \n",
            " Last accuracy mAP@0.5 = 98.73 % \n",
            " 2117: 0.293179, 0.349800 avg loss, 0.001000 rate, 1.144269 seconds, 135488 images\n",
            "Loaded: 1.105113 seconds\n",
            "\n",
            " (next mAP calculation at 2200 iterations) \n",
            " Last accuracy mAP@0.5 = 98.73 % \n",
            " 2118: 0.436502, 0.358470 avg loss, 0.001000 rate, 1.017549 seconds, 135552 images\n",
            "Loaded: 1.262023 seconds\n",
            "\n",
            " (next mAP calculation at 2200 iterations) \n",
            " Last accuracy mAP@0.5 = 98.73 % \n",
            " 2119: 0.320835, 0.354707 avg loss, 0.001000 rate, 1.005934 seconds, 135616 images\n",
            "Loaded: 1.273832 seconds\n",
            "\n",
            " (next mAP calculation at 2200 iterations) \n",
            " Last accuracy mAP@0.5 = 98.73 % \n",
            " 2120: 0.315891, 0.350825 avg loss, 0.001000 rate, 1.038902 seconds, 135680 images\n",
            "Resizing\n",
            "384 x 384 \n",
            " try to allocate additional workspace_size = 1302.33 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.000046 seconds\n",
            "\n",
            " (next mAP calculation at 2200 iterations) \n",
            " Last accuracy mAP@0.5 = 98.73 % \n",
            " 2121: 0.439005, 0.359643 avg loss, 0.001000 rate, 0.439543 seconds, 135744 images\n",
            "Loaded: 1.656124 seconds\n",
            "\n",
            " (next mAP calculation at 2200 iterations) \n",
            " Last accuracy mAP@0.5 = 98.73 % \n",
            " 2122: 0.331626, 0.356841 avg loss, 0.001000 rate, 0.400608 seconds, 135808 images\n",
            "Loaded: 1.535118 seconds\n",
            "\n",
            " (next mAP calculation at 2200 iterations) \n",
            " Last accuracy mAP@0.5 = 98.73 % \n",
            " 2123: 0.289871, 0.350144 avg loss, 0.001000 rate, 0.392245 seconds, 135872 images\n",
            "Loaded: 1.591420 seconds\n",
            "\n",
            " (next mAP calculation at 2200 iterations) \n",
            " Last accuracy mAP@0.5 = 98.73 % \n",
            " 2124: 0.414486, 0.356578 avg loss, 0.001000 rate, 0.376995 seconds, 135936 images\n",
            "Loaded: 1.427482 seconds\n",
            "\n",
            " (next mAP calculation at 2200 iterations) \n",
            " Last accuracy mAP@0.5 = 98.73 % \n",
            " 2125: 0.360496, 0.356970 avg loss, 0.001000 rate, 0.359682 seconds, 136000 images\n",
            "Loaded: 1.476782 seconds\n",
            "\n",
            " (next mAP calculation at 2200 iterations) \n",
            " Last accuracy mAP@0.5 = 98.73 % \n",
            " 2126: 0.303931, 0.351666 avg loss, 0.001000 rate, 0.391160 seconds, 136064 images\n",
            "Loaded: 1.400218 seconds\n",
            "\n",
            " (next mAP calculation at 2200 iterations) \n",
            " Last accuracy mAP@0.5 = 98.73 % \n",
            " 2127: 0.243952, 0.340895 avg loss, 0.001000 rate, 0.381329 seconds, 136128 images\n",
            "Loaded: 1.431235 seconds\n",
            "\n",
            " (next mAP calculation at 2200 iterations) \n",
            " Last accuracy mAP@0.5 = 98.73 % \n",
            " 2128: 0.285411, 0.335346 avg loss, 0.001000 rate, 0.398843 seconds, 136192 images\n",
            "Loaded: 1.425225 seconds\n",
            "\n",
            " (next mAP calculation at 2200 iterations) \n",
            " Last accuracy mAP@0.5 = 98.73 % \n",
            " 2129: 0.346693, 0.336481 avg loss, 0.001000 rate, 0.367722 seconds, 136256 images\n",
            "Loaded: 1.470465 seconds\n",
            "\n",
            " (next mAP calculation at 2200 iterations) \n",
            " Last accuracy mAP@0.5 = 98.73 % \n",
            " 2130: 0.348988, 0.337732 avg loss, 0.001000 rate, 0.369734 seconds, 136320 images\n",
            "Resizing\n",
            "576 x 576 \n",
            " try to allocate additional workspace_size = 4605.87 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.000040 seconds\n",
            "\n",
            " (next mAP calculation at 2200 iterations) \n",
            " Last accuracy mAP@0.5 = 98.73 % \n",
            " 2131: 0.387245, 0.342683 avg loss, 0.001000 rate, 0.944355 seconds, 136384 images\n",
            "Loaded: 1.292888 seconds\n",
            "\n",
            " (next mAP calculation at 2200 iterations) \n",
            " Last accuracy mAP@0.5 = 98.73 % \n",
            " 2132: 0.288650, 0.337280 avg loss, 0.001000 rate, 0.859436 seconds, 136448 images\n",
            "Loaded: 1.362775 seconds\n",
            "\n",
            " (next mAP calculation at 2200 iterations) \n",
            " Last accuracy mAP@0.5 = 98.73 % \n",
            " 2133: 0.328524, 0.336404 avg loss, 0.001000 rate, 1.002331 seconds, 136512 images\n",
            "Loaded: 1.162586 seconds\n",
            "\n",
            " (next mAP calculation at 2200 iterations) \n",
            " Last accuracy mAP@0.5 = 98.73 % \n",
            " 2134: 0.418761, 0.344640 avg loss, 0.001000 rate, 0.931619 seconds, 136576 images\n",
            "Loaded: 1.259962 seconds\n",
            "\n",
            " (next mAP calculation at 2200 iterations) \n",
            " Last accuracy mAP@0.5 = 98.73 % \n",
            " 2135: 0.353519, 0.345528 avg loss, 0.001000 rate, 0.810273 seconds, 136640 images\n",
            "Loaded: 1.403358 seconds\n",
            "\n",
            " (next mAP calculation at 2200 iterations) \n",
            " Last accuracy mAP@0.5 = 98.73 % \n",
            " 2136: 0.316463, 0.342621 avg loss, 0.001000 rate, 0.968978 seconds, 136704 images\n",
            "Loaded: 1.305329 seconds\n",
            "\n",
            " (next mAP calculation at 2200 iterations) \n",
            " Last accuracy mAP@0.5 = 98.73 % \n",
            " 2137: 0.344397, 0.342799 avg loss, 0.001000 rate, 0.855766 seconds, 136768 images\n",
            "Loaded: 1.475642 seconds\n",
            "\n",
            " (next mAP calculation at 2200 iterations) \n",
            " Last accuracy mAP@0.5 = 98.73 % \n",
            " 2138: 0.376342, 0.346153 avg loss, 0.001000 rate, 0.955571 seconds, 136832 images\n",
            "Loaded: 1.269664 seconds\n",
            "\n",
            " (next mAP calculation at 2200 iterations) \n",
            " Last accuracy mAP@0.5 = 98.73 % \n",
            " 2139: 0.468050, 0.358343 avg loss, 0.001000 rate, 0.961719 seconds, 136896 images\n",
            "Loaded: 1.249703 seconds\n",
            "\n",
            " (next mAP calculation at 2200 iterations) \n",
            " Last accuracy mAP@0.5 = 98.73 % \n",
            " 2140: 0.365425, 0.359051 avg loss, 0.001000 rate, 0.866417 seconds, 136960 images\n",
            "Resizing\n",
            "512 x 512 \n",
            " try to allocate additional workspace_size = 4596.96 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 1.360294 seconds\n",
            "\n",
            " (next mAP calculation at 2200 iterations) \n",
            " Last accuracy mAP@0.5 = 98.73 % \n",
            " 2141: 0.325632, 0.355709 avg loss, 0.001000 rate, 0.765485 seconds, 137024 images\n",
            "Loaded: 1.290243 seconds\n",
            "\n",
            " (next mAP calculation at 2200 iterations) \n",
            " Last accuracy mAP@0.5 = 98.73 % \n",
            " 2142: 0.395473, 0.359686 avg loss, 0.001000 rate, 0.662523 seconds, 137088 images\n",
            "Loaded: 1.317908 seconds\n",
            "\n",
            " (next mAP calculation at 2200 iterations) \n",
            " Last accuracy mAP@0.5 = 98.73 % \n",
            " 2143: 0.354505, 0.359168 avg loss, 0.001000 rate, 0.636970 seconds, 137152 images\n",
            "Loaded: 1.392454 seconds\n",
            "\n",
            " (next mAP calculation at 2200 iterations) \n",
            " Last accuracy mAP@0.5 = 98.73 % \n",
            " 2144: 0.331222, 0.356373 avg loss, 0.001000 rate, 0.787965 seconds, 137216 images\n",
            "Loaded: 1.399193 seconds\n",
            "\n",
            " (next mAP calculation at 2200 iterations) \n",
            " Last accuracy mAP@0.5 = 98.73 % \n",
            " 2145: 0.313954, 0.352131 avg loss, 0.001000 rate, 0.626820 seconds, 137280 images\n",
            "Loaded: 1.415629 seconds\n",
            "\n",
            " (next mAP calculation at 2200 iterations) \n",
            " Last accuracy mAP@0.5 = 98.73 % \n",
            " 2146: 0.377297, 0.354648 avg loss, 0.001000 rate, 0.775866 seconds, 137344 images\n",
            "Loaded: 1.263757 seconds\n",
            "\n",
            " (next mAP calculation at 2200 iterations) \n",
            " Last accuracy mAP@0.5 = 98.73 % \n",
            " 2147: 0.429232, 0.362106 avg loss, 0.001000 rate, 0.776818 seconds, 137408 images\n",
            "Loaded: 1.412680 seconds\n",
            "\n",
            " (next mAP calculation at 2200 iterations) \n",
            " Last accuracy mAP@0.5 = 98.73 % \n",
            " 2148: 0.299376, 0.355833 avg loss, 0.001000 rate, 0.709570 seconds, 137472 images\n",
            "Loaded: 1.528648 seconds\n",
            "\n",
            " (next mAP calculation at 2200 iterations) \n",
            " Last accuracy mAP@0.5 = 98.73 % \n",
            " 2149: 0.313321, 0.351582 avg loss, 0.001000 rate, 0.723228 seconds, 137536 images\n",
            "Loaded: 1.346526 seconds\n",
            "\n",
            " (next mAP calculation at 2200 iterations) \n",
            " Last accuracy mAP@0.5 = 98.73 % \n",
            " 2150: 0.400037, 0.356427 avg loss, 0.001000 rate, 0.703987 seconds, 137600 images\n",
            "Resizing\n",
            "512 x 512 \n",
            " try to allocate additional workspace_size = 4596.96 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 1.481975 seconds\n",
            "\n",
            " (next mAP calculation at 2200 iterations) \n",
            " Last accuracy mAP@0.5 = 98.73 % \n",
            " 2151: 0.347171, 0.355502 avg loss, 0.001000 rate, 0.734946 seconds, 137664 images\n",
            "Loaded: 1.285042 seconds\n",
            "\n",
            " (next mAP calculation at 2200 iterations) \n",
            " Last accuracy mAP@0.5 = 98.73 % \n",
            " 2152: 0.360494, 0.356001 avg loss, 0.001000 rate, 0.674886 seconds, 137728 images\n",
            "Loaded: 1.397129 seconds\n",
            "\n",
            " (next mAP calculation at 2200 iterations) \n",
            " Last accuracy mAP@0.5 = 98.73 % \n",
            " 2153: 0.219153, 0.342316 avg loss, 0.001000 rate, 0.645653 seconds, 137792 images\n",
            "Loaded: 1.420852 seconds\n",
            "\n",
            " (next mAP calculation at 2200 iterations) \n",
            " Last accuracy mAP@0.5 = 98.73 % \n",
            " 2154: 0.335163, 0.341601 avg loss, 0.001000 rate, 0.659222 seconds, 137856 images\n",
            "Loaded: 1.474647 seconds\n",
            "\n",
            " (next mAP calculation at 2200 iterations) \n",
            " Last accuracy mAP@0.5 = 98.73 % \n",
            " 2155: 0.357014, 0.343142 avg loss, 0.001000 rate, 0.712514 seconds, 137920 images\n",
            "Loaded: 1.331269 seconds\n",
            "\n",
            " (next mAP calculation at 2200 iterations) \n",
            " Last accuracy mAP@0.5 = 98.73 % \n",
            " 2156: 0.372332, 0.346061 avg loss, 0.001000 rate, 0.689442 seconds, 137984 images\n",
            "Loaded: 1.356256 seconds\n",
            "\n",
            " (next mAP calculation at 2200 iterations) \n",
            " Last accuracy mAP@0.5 = 98.73 % \n",
            " 2157: 0.421375, 0.353593 avg loss, 0.001000 rate, 0.702069 seconds, 138048 images\n",
            "Loaded: 1.388968 seconds\n",
            "\n",
            " (next mAP calculation at 2200 iterations) \n",
            " Last accuracy mAP@0.5 = 98.73 % \n",
            " 2158: 0.344662, 0.352699 avg loss, 0.001000 rate, 0.655307 seconds, 138112 images\n",
            "Loaded: 1.445015 seconds\n",
            "\n",
            " (next mAP calculation at 2200 iterations) \n",
            " Last accuracy mAP@0.5 = 98.73 % \n",
            " 2159: 0.303268, 0.347756 avg loss, 0.001000 rate, 0.731142 seconds, 138176 images\n",
            "Loaded: 1.288639 seconds\n",
            "\n",
            " (next mAP calculation at 2200 iterations) \n",
            " Last accuracy mAP@0.5 = 98.73 % \n",
            " 2160: 0.369679, 0.349949 avg loss, 0.001000 rate, 0.730051 seconds, 138240 images\n",
            "Resizing\n",
            "384 x 384 \n",
            " try to allocate additional workspace_size = 1302.33 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.000033 seconds\n",
            "\n",
            " (next mAP calculation at 2200 iterations) \n",
            " Last accuracy mAP@0.5 = 98.73 % \n",
            " 2161: 0.324461, 0.347400 avg loss, 0.001000 rate, 0.456013 seconds, 138304 images\n",
            "Loaded: 1.252262 seconds\n",
            "\n",
            " (next mAP calculation at 2200 iterations) \n",
            " Last accuracy mAP@0.5 = 98.73 % \n",
            " 2162: 0.314121, 0.344072 avg loss, 0.001000 rate, 0.354219 seconds, 138368 images\n",
            "Loaded: 1.488724 seconds\n",
            "\n",
            " (next mAP calculation at 2200 iterations) \n",
            " Last accuracy mAP@0.5 = 98.73 % \n",
            " 2163: 0.288685, 0.338533 avg loss, 0.001000 rate, 0.407235 seconds, 138432 images\n",
            "Loaded: 1.503696 seconds\n",
            "\n",
            " (next mAP calculation at 2200 iterations) \n",
            " Last accuracy mAP@0.5 = 98.73 % \n",
            " 2164: 0.343397, 0.339020 avg loss, 0.001000 rate, 0.392779 seconds, 138496 images\n",
            "Loaded: 1.431907 seconds\n",
            "\n",
            " (next mAP calculation at 2200 iterations) \n",
            " Last accuracy mAP@0.5 = 98.73 % \n",
            " 2165: 0.309808, 0.336098 avg loss, 0.001000 rate, 0.348009 seconds, 138560 images\n",
            "Loaded: 1.506642 seconds\n",
            "\n",
            " (next mAP calculation at 2200 iterations) \n",
            " Last accuracy mAP@0.5 = 98.73 % \n",
            " 2166: 0.332820, 0.335771 avg loss, 0.001000 rate, 0.354570 seconds, 138624 images\n",
            "Loaded: 1.477011 seconds\n",
            "\n",
            " (next mAP calculation at 2200 iterations) \n",
            " Last accuracy mAP@0.5 = 98.73 % \n",
            " 2167: 0.319820, 0.334176 avg loss, 0.001000 rate, 0.367130 seconds, 138688 images\n",
            "Loaded: 1.448580 seconds\n",
            "\n",
            " (next mAP calculation at 2200 iterations) \n",
            " Last accuracy mAP@0.5 = 98.73 % \n",
            " 2168: 0.378360, 0.338594 avg loss, 0.001000 rate, 0.367459 seconds, 138752 images\n",
            "Loaded: 1.460598 seconds\n",
            "\n",
            " (next mAP calculation at 2200 iterations) \n",
            " Last accuracy mAP@0.5 = 98.73 % \n",
            " 2169: 0.368090, 0.341544 avg loss, 0.001000 rate, 0.351760 seconds, 138816 images\n",
            "Loaded: 1.461744 seconds\n",
            "\n",
            " (next mAP calculation at 2200 iterations) \n",
            " Last accuracy mAP@0.5 = 98.73 % \n",
            " 2170: 0.297380, 0.337127 avg loss, 0.001000 rate, 0.354963 seconds, 138880 images\n",
            "Resizing\n",
            "352 x 352 \n",
            " try to allocate additional workspace_size = 1264.58 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 1.708069 seconds\n",
            "\n",
            " (next mAP calculation at 2200 iterations) \n",
            " Last accuracy mAP@0.5 = 98.73 % \n",
            " 2171: 0.286935, 0.332108 avg loss, 0.001000 rate, 0.353008 seconds, 138944 images\n",
            "Loaded: 1.442199 seconds\n",
            "\n",
            " (next mAP calculation at 2200 iterations) \n",
            " Last accuracy mAP@0.5 = 98.73 % \n",
            " 2172: 0.391624, 0.338060 avg loss, 0.001000 rate, 0.300296 seconds, 139008 images\n",
            "Loaded: 1.549271 seconds\n",
            "\n",
            " (next mAP calculation at 2200 iterations) \n",
            " Last accuracy mAP@0.5 = 98.73 % \n",
            " 2173: 0.283892, 0.332643 avg loss, 0.001000 rate, 0.288838 seconds, 139072 images\n",
            "Loaded: 1.574324 seconds\n",
            "\n",
            " (next mAP calculation at 2200 iterations) \n",
            " Last accuracy mAP@0.5 = 98.73 % \n",
            " 2174: 0.679206, 0.367299 avg loss, 0.001000 rate, 0.308154 seconds, 139136 images\n",
            "Loaded: 1.626679 seconds\n",
            "\n",
            " (next mAP calculation at 2200 iterations) \n",
            " Last accuracy mAP@0.5 = 98.73 % \n",
            " 2175: 0.252568, 0.355826 avg loss, 0.001000 rate, 0.304906 seconds, 139200 images\n",
            "Loaded: 1.560745 seconds\n",
            "\n",
            " (next mAP calculation at 2200 iterations) \n",
            " Last accuracy mAP@0.5 = 98.73 % \n",
            " 2176: 0.345651, 0.354809 avg loss, 0.001000 rate, 0.307456 seconds, 139264 images\n",
            "Loaded: 1.718236 seconds\n",
            "\n",
            " (next mAP calculation at 2200 iterations) \n",
            " Last accuracy mAP@0.5 = 98.73 % \n",
            " 2177: 0.384024, 0.357730 avg loss, 0.001000 rate, 0.288251 seconds, 139328 images\n",
            "Loaded: 1.461295 seconds\n",
            "\n",
            " (next mAP calculation at 2200 iterations) \n",
            " Last accuracy mAP@0.5 = 98.73 % \n",
            " 2178: 0.349382, 0.356895 avg loss, 0.001000 rate, 0.330245 seconds, 139392 images\n",
            "Loaded: 1.475901 seconds\n",
            "\n",
            " (next mAP calculation at 2200 iterations) \n",
            " Last accuracy mAP@0.5 = 98.73 % \n",
            " 2179: 0.298968, 0.351103 avg loss, 0.001000 rate, 0.314749 seconds, 139456 images\n",
            "Loaded: 1.523537 seconds\n",
            "\n",
            " (next mAP calculation at 2200 iterations) \n",
            " Last accuracy mAP@0.5 = 98.73 % \n",
            " 2180: 0.362038, 0.352196 avg loss, 0.001000 rate, 0.300340 seconds, 139520 images\n",
            "Resizing\n",
            "352 x 352 \n",
            " try to allocate additional workspace_size = 1264.58 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 1.572251 seconds\n",
            "\n",
            " (next mAP calculation at 2200 iterations) \n",
            " Last accuracy mAP@0.5 = 98.73 % \n",
            " 2181: 0.202578, 0.337234 avg loss, 0.001000 rate, 0.407848 seconds, 139584 images\n",
            "Loaded: 1.383969 seconds\n",
            "\n",
            " (next mAP calculation at 2200 iterations) \n",
            " Last accuracy mAP@0.5 = 98.73 % \n",
            " 2182: 0.325372, 0.336048 avg loss, 0.001000 rate, 0.302106 seconds, 139648 images\n",
            "Loaded: 1.523001 seconds\n",
            "\n",
            " (next mAP calculation at 2200 iterations) \n",
            " Last accuracy mAP@0.5 = 98.73 % \n",
            " 2183: 0.304719, 0.332915 avg loss, 0.001000 rate, 0.308863 seconds, 139712 images\n",
            "Loaded: 1.555834 seconds\n",
            "\n",
            " (next mAP calculation at 2200 iterations) \n",
            " Last accuracy mAP@0.5 = 98.73 % \n",
            " 2184: 0.251915, 0.324815 avg loss, 0.001000 rate, 0.310714 seconds, 139776 images\n",
            "Loaded: 1.457864 seconds\n",
            "\n",
            " (next mAP calculation at 2200 iterations) \n",
            " Last accuracy mAP@0.5 = 98.73 % \n",
            " 2185: 0.449947, 0.337328 avg loss, 0.001000 rate, 0.309395 seconds, 139840 images\n",
            "Loaded: 1.460202 seconds\n",
            "\n",
            " (next mAP calculation at 2200 iterations) \n",
            " Last accuracy mAP@0.5 = 98.73 % \n",
            " 2186: 0.293609, 0.332956 avg loss, 0.001000 rate, 0.304041 seconds, 139904 images\n",
            "Loaded: 1.477075 seconds\n",
            "\n",
            " (next mAP calculation at 2200 iterations) \n",
            " Last accuracy mAP@0.5 = 98.73 % \n",
            " 2187: 0.297413, 0.329402 avg loss, 0.001000 rate, 0.293512 seconds, 139968 images\n",
            "Loaded: 1.587648 seconds\n",
            "\n",
            " (next mAP calculation at 2200 iterations) \n",
            " Last accuracy mAP@0.5 = 98.73 % \n",
            " 2188: 0.324315, 0.328893 avg loss, 0.001000 rate, 0.283463 seconds, 140032 images\n",
            "Loaded: 1.476918 seconds\n",
            "\n",
            " (next mAP calculation at 2200 iterations) \n",
            " Last accuracy mAP@0.5 = 98.73 % \n",
            " 2189: 0.376147, 0.333619 avg loss, 0.001000 rate, 0.330903 seconds, 140096 images\n",
            "Loaded: 1.483808 seconds\n",
            "\n",
            " (next mAP calculation at 2200 iterations) \n",
            " Last accuracy mAP@0.5 = 98.73 % \n",
            " 2190: 0.320118, 0.332269 avg loss, 0.001000 rate, 0.305380 seconds, 140160 images\n",
            "Resizing\n",
            "448 x 448 \n",
            " try to allocate additional workspace_size = 1233.65 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.000032 seconds\n",
            "\n",
            " (next mAP calculation at 2200 iterations) \n",
            " Last accuracy mAP@0.5 = 98.73 % \n",
            " 2191: 0.334616, 0.332503 avg loss, 0.001000 rate, 0.536799 seconds, 140224 images\n",
            "Loaded: 1.583741 seconds\n",
            "\n",
            " (next mAP calculation at 2200 iterations) \n",
            " Last accuracy mAP@0.5 = 98.73 % \n",
            " 2192: 0.316443, 0.330897 avg loss, 0.001000 rate, 0.541028 seconds, 140288 images\n",
            "Loaded: 1.328546 seconds\n",
            "\n",
            " (next mAP calculation at 2200 iterations) \n",
            " Last accuracy mAP@0.5 = 98.73 % \n",
            " 2193: 0.305778, 0.328386 avg loss, 0.001000 rate, 0.485378 seconds, 140352 images\n",
            "Loaded: 1.485553 seconds\n",
            "\n",
            " (next mAP calculation at 2200 iterations) \n",
            " Last accuracy mAP@0.5 = 98.73 % \n",
            " 2194: 0.288349, 0.324382 avg loss, 0.001000 rate, 0.542015 seconds, 140416 images\n",
            "Loaded: 1.460557 seconds\n",
            "\n",
            " (next mAP calculation at 2200 iterations) \n",
            " Last accuracy mAP@0.5 = 98.73 % \n",
            " 2195: 0.334058, 0.325350 avg loss, 0.001000 rate, 0.572946 seconds, 140480 images\n",
            "Loaded: 1.404447 seconds\n",
            "\n",
            " (next mAP calculation at 2200 iterations) \n",
            " Last accuracy mAP@0.5 = 98.73 % \n",
            " 2196: 0.297482, 0.322563 avg loss, 0.001000 rate, 0.498457 seconds, 140544 images\n",
            "Loaded: 1.523226 seconds\n",
            "\n",
            " (next mAP calculation at 2200 iterations) \n",
            " Last accuracy mAP@0.5 = 98.73 % \n",
            " 2197: 0.274013, 0.317708 avg loss, 0.001000 rate, 0.542357 seconds, 140608 images\n",
            "Loaded: 1.434253 seconds\n",
            "\n",
            " (next mAP calculation at 2200 iterations) \n",
            " Last accuracy mAP@0.5 = 98.73 % \n",
            " 2198: 0.444660, 0.330403 avg loss, 0.001000 rate, 0.534275 seconds, 140672 images\n",
            "Loaded: 1.431168 seconds\n",
            "\n",
            " (next mAP calculation at 2200 iterations) \n",
            " Last accuracy mAP@0.5 = 98.73 % \n",
            " 2199: 0.312570, 0.328620 avg loss, 0.001000 rate, 0.572398 seconds, 140736 images\n",
            "Loaded: 1.514883 seconds\n",
            "\n",
            " (next mAP calculation at 2200 iterations) \n",
            " Last accuracy mAP@0.5 = 98.73 % \n",
            " 2200: 0.369928, 0.332750 avg loss, 0.001000 rate, 0.483918 seconds, 140800 images\n",
            "Resizing to initial size: 416 x 416 \n",
            " try to allocate additional workspace_size = 1302.33 MB \n",
            " CUDA allocate done! \n",
            "\n",
            " calculation mAP (mean average precision)...\n",
            "368\n",
            " detections_count = 546, unique_truth_count = 374  \n",
            "class_id = 0, name = banana, ap = 98.96%   \t (TP = 363, FP = 8) \n",
            "\n",
            " for thresh = 0.25, precision = 0.98, recall = 0.97, F1-score = 0.97 \n",
            " for thresh = 0.25, TP = 363, FP = 8, FN = 11, average IoU = 78.02 % \n",
            "\n",
            " IoU threshold = 50 %, used Area-Under-Curve for each unique Recall \n",
            " mean average precision (mAP@0.50) = 0.989626, or 98.96 % \n",
            "Total Detection Time: 16.000000 Seconds\n",
            "\n",
            "Set -points flag:\n",
            " `-points 101` for MS COCO \n",
            " `-points 11` for PascalVOC 2007 (uncomment `difficult` in voc.data) \n",
            " `-points 0` (AUC) for ImageNet, PascalVOC 2010-2012, your custom dataset\n",
            "\n",
            " mean_average_precision (mAP@0.5) = 0.989626 \n",
            "Saving weights to /content/gdrive/My Drive/darknet/backup/yolov3-tiny-280120_last.weights\n",
            "Resizing\n",
            "384 x 384 \n",
            " try to allocate additional workspace_size = 1302.33 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 1.614333 seconds\n",
            "\n",
            " (next mAP calculation at 2300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.96 % \n",
            " 2201: 0.327300, 0.332205 avg loss, 0.001000 rate, 0.452404 seconds, 140864 images\n",
            "Loaded: 1.419076 seconds\n",
            "\n",
            " (next mAP calculation at 2300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.96 % \n",
            " 2202: 0.282484, 0.327233 avg loss, 0.001000 rate, 0.380508 seconds, 140928 images\n",
            "Loaded: 1.470584 seconds\n",
            "\n",
            " (next mAP calculation at 2300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.96 % \n",
            " 2203: 0.322357, 0.326746 avg loss, 0.001000 rate, 0.371579 seconds, 140992 images\n",
            "Loaded: 1.470361 seconds\n",
            "\n",
            " (next mAP calculation at 2300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.96 % \n",
            " 2204: 0.300394, 0.324110 avg loss, 0.001000 rate, 0.362468 seconds, 141056 images\n",
            "Loaded: 1.539185 seconds\n",
            "\n",
            " (next mAP calculation at 2300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.96 % \n",
            " 2205: 0.293193, 0.321019 avg loss, 0.001000 rate, 0.375093 seconds, 141120 images\n",
            "Loaded: 1.560098 seconds\n",
            "\n",
            " (next mAP calculation at 2300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.96 % \n",
            " 2206: 0.403009, 0.329218 avg loss, 0.001000 rate, 0.359967 seconds, 141184 images\n",
            "Loaded: 1.468869 seconds\n",
            "\n",
            " (next mAP calculation at 2300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.96 % \n",
            " 2207: 0.320892, 0.328385 avg loss, 0.001000 rate, 0.369472 seconds, 141248 images\n",
            "Loaded: 1.493017 seconds\n",
            "\n",
            " (next mAP calculation at 2300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.96 % \n",
            " 2208: 0.295493, 0.325096 avg loss, 0.001000 rate, 0.349977 seconds, 141312 images\n",
            "Loaded: 1.488435 seconds\n",
            "\n",
            " (next mAP calculation at 2300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.96 % \n",
            " 2209: 0.407295, 0.333316 avg loss, 0.001000 rate, 0.385522 seconds, 141376 images\n",
            "Loaded: 1.441965 seconds\n",
            "\n",
            " (next mAP calculation at 2300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.96 % \n",
            " 2210: 0.374672, 0.337451 avg loss, 0.001000 rate, 0.367465 seconds, 141440 images\n",
            "Resizing\n",
            "512 x 512 \n",
            " try to allocate additional workspace_size = 4596.96 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.000046 seconds\n",
            "\n",
            " (next mAP calculation at 2300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.96 % \n",
            " 2211: 0.460155, 0.349722 avg loss, 0.001000 rate, 0.749637 seconds, 141504 images\n",
            "Loaded: 1.290442 seconds\n",
            "\n",
            " (next mAP calculation at 2300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.96 % \n",
            " 2212: 0.261879, 0.340937 avg loss, 0.001000 rate, 0.728000 seconds, 141568 images\n",
            "Loaded: 1.390990 seconds\n",
            "\n",
            " (next mAP calculation at 2300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.96 % \n",
            " 2213: 0.417710, 0.348615 avg loss, 0.001000 rate, 0.655700 seconds, 141632 images\n",
            "Loaded: 1.396106 seconds\n",
            "\n",
            " (next mAP calculation at 2300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.96 % \n",
            " 2214: 0.321773, 0.345931 avg loss, 0.001000 rate, 0.723910 seconds, 141696 images\n",
            "Loaded: 1.349235 seconds\n",
            "\n",
            " (next mAP calculation at 2300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.96 % \n",
            " 2215: 0.323983, 0.343736 avg loss, 0.001000 rate, 0.719311 seconds, 141760 images\n",
            "Loaded: 1.288618 seconds\n",
            "\n",
            " (next mAP calculation at 2300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.96 % \n",
            " 2216: 0.294221, 0.338784 avg loss, 0.001000 rate, 0.767272 seconds, 141824 images\n",
            "Loaded: 1.344888 seconds\n",
            "\n",
            " (next mAP calculation at 2300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.96 % \n",
            " 2217: 0.358929, 0.340799 avg loss, 0.001000 rate, 0.654557 seconds, 141888 images\n",
            "Loaded: 1.484248 seconds\n",
            "\n",
            " (next mAP calculation at 2300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.96 % \n",
            " 2218: 0.303838, 0.337103 avg loss, 0.001000 rate, 0.756598 seconds, 141952 images\n",
            "Loaded: 1.384434 seconds\n",
            "\n",
            " (next mAP calculation at 2300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.96 % \n",
            " 2219: 0.344143, 0.337807 avg loss, 0.001000 rate, 0.672067 seconds, 142016 images\n",
            "Loaded: 1.401027 seconds\n",
            "\n",
            " (next mAP calculation at 2300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.96 % \n",
            " 2220: 0.334794, 0.337505 avg loss, 0.001000 rate, 0.656075 seconds, 142080 images\n",
            "Resizing\n",
            "384 x 384 \n",
            " try to allocate additional workspace_size = 1302.33 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 1.536725 seconds\n",
            "\n",
            " (next mAP calculation at 2300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.96 % \n",
            " 2221: 0.623961, 0.366151 avg loss, 0.001000 rate, 0.487246 seconds, 142144 images\n",
            "Loaded: 1.440587 seconds\n",
            "\n",
            " (next mAP calculation at 2300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.96 % \n",
            " 2222: 0.357132, 0.365249 avg loss, 0.001000 rate, 0.353896 seconds, 142208 images\n",
            "Loaded: 1.411498 seconds\n",
            "\n",
            " (next mAP calculation at 2300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.96 % \n",
            " 2223: 0.415477, 0.370272 avg loss, 0.001000 rate, 0.344419 seconds, 142272 images\n",
            "Loaded: 1.558926 seconds\n",
            "\n",
            " (next mAP calculation at 2300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.96 % \n",
            " 2224: 0.350707, 0.368315 avg loss, 0.001000 rate, 0.391274 seconds, 142336 images\n",
            "Loaded: 1.453787 seconds\n",
            "\n",
            " (next mAP calculation at 2300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.96 % \n",
            " 2225: 0.325043, 0.363988 avg loss, 0.001000 rate, 0.373688 seconds, 142400 images\n",
            "Loaded: 1.380291 seconds\n",
            "\n",
            " (next mAP calculation at 2300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.96 % \n",
            " 2226: 0.269300, 0.354519 avg loss, 0.001000 rate, 0.351398 seconds, 142464 images\n",
            "Loaded: 1.473381 seconds\n",
            "\n",
            " (next mAP calculation at 2300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.96 % \n",
            " 2227: 0.343437, 0.353411 avg loss, 0.001000 rate, 0.328957 seconds, 142528 images\n",
            "Loaded: 1.527640 seconds\n",
            "\n",
            " (next mAP calculation at 2300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.96 % \n",
            " 2228: 0.344068, 0.352477 avg loss, 0.001000 rate, 0.387769 seconds, 142592 images\n",
            "Loaded: 1.416675 seconds\n",
            "\n",
            " (next mAP calculation at 2300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.96 % \n",
            " 2229: 0.362783, 0.353507 avg loss, 0.001000 rate, 0.368298 seconds, 142656 images\n",
            "Loaded: 1.409755 seconds\n",
            "\n",
            " (next mAP calculation at 2300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.96 % \n",
            " 2230: 0.364360, 0.354593 avg loss, 0.001000 rate, 0.362072 seconds, 142720 images\n",
            "Resizing\n",
            "512 x 512 \n",
            " try to allocate additional workspace_size = 4596.96 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.000049 seconds\n",
            "\n",
            " (next mAP calculation at 2300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.96 % \n",
            " 2231: 0.314557, 0.350589 avg loss, 0.001000 rate, 0.701136 seconds, 142784 images\n",
            "Loaded: 1.332884 seconds\n",
            "\n",
            " (next mAP calculation at 2300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.96 % \n",
            " 2232: 0.373485, 0.352879 avg loss, 0.001000 rate, 0.718741 seconds, 142848 images\n",
            "Loaded: 1.371092 seconds\n",
            "\n",
            " (next mAP calculation at 2300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.96 % \n",
            " 2233: 0.339979, 0.351589 avg loss, 0.001000 rate, 0.670918 seconds, 142912 images\n",
            "Loaded: 1.402345 seconds\n",
            "\n",
            " (next mAP calculation at 2300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.96 % \n",
            " 2234: 0.323550, 0.348785 avg loss, 0.001000 rate, 0.646446 seconds, 142976 images\n",
            "Loaded: 1.385353 seconds\n",
            "\n",
            " (next mAP calculation at 2300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.96 % \n",
            " 2235: 0.292017, 0.343108 avg loss, 0.001000 rate, 0.638813 seconds, 143040 images\n",
            "Loaded: 1.336810 seconds\n",
            "\n",
            " (next mAP calculation at 2300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.96 % \n",
            " 2236: 0.292819, 0.338079 avg loss, 0.001000 rate, 0.724483 seconds, 143104 images\n",
            "Loaded: 1.307412 seconds\n",
            "\n",
            " (next mAP calculation at 2300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.96 % \n",
            " 2237: 0.324897, 0.336761 avg loss, 0.001000 rate, 0.698727 seconds, 143168 images\n",
            "Loaded: 1.398891 seconds\n",
            "\n",
            " (next mAP calculation at 2300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.96 % \n",
            " 2238: 0.301946, 0.333279 avg loss, 0.001000 rate, 0.695388 seconds, 143232 images\n",
            "Loaded: 1.439384 seconds\n",
            "\n",
            " (next mAP calculation at 2300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.96 % \n",
            " 2239: 0.304237, 0.330375 avg loss, 0.001000 rate, 0.682810 seconds, 143296 images\n",
            "Loaded: 1.359893 seconds\n",
            "\n",
            " (next mAP calculation at 2300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.96 % \n",
            " 2240: 0.339698, 0.331307 avg loss, 0.001000 rate, 0.686198 seconds, 143360 images\n",
            "Resizing\n",
            "416 x 416 \n",
            " try to allocate additional workspace_size = 1302.33 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 1.447539 seconds\n",
            "\n",
            " (next mAP calculation at 2300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.96 % \n",
            " 2241: 0.307342, 0.328911 avg loss, 0.001000 rate, 0.507658 seconds, 143424 images\n",
            "Loaded: 1.372768 seconds\n",
            "\n",
            " (next mAP calculation at 2300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.96 % \n",
            " 2242: 0.326002, 0.328620 avg loss, 0.001000 rate, 0.466994 seconds, 143488 images\n",
            "Loaded: 1.384214 seconds\n",
            "\n",
            " (next mAP calculation at 2300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.96 % \n",
            " 2243: 0.287918, 0.324550 avg loss, 0.001000 rate, 0.419055 seconds, 143552 images\n",
            "Loaded: 1.459034 seconds\n",
            "\n",
            " (next mAP calculation at 2300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.96 % \n",
            " 2244: 0.255959, 0.317691 avg loss, 0.001000 rate, 0.429968 seconds, 143616 images\n",
            "Loaded: 1.566686 seconds\n",
            "\n",
            " (next mAP calculation at 2300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.96 % \n",
            " 2245: 0.351022, 0.321024 avg loss, 0.001000 rate, 0.459867 seconds, 143680 images\n",
            "Loaded: 1.423883 seconds\n",
            "\n",
            " (next mAP calculation at 2300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.96 % \n",
            " 2246: 0.407687, 0.329690 avg loss, 0.001000 rate, 0.475396 seconds, 143744 images\n",
            "Loaded: 1.367087 seconds\n",
            "\n",
            " (next mAP calculation at 2300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.96 % \n",
            " 2247: 0.348644, 0.331585 avg loss, 0.001000 rate, 0.433466 seconds, 143808 images\n",
            "Loaded: 1.391879 seconds\n",
            "\n",
            " (next mAP calculation at 2300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.96 % \n",
            " 2248: 0.401302, 0.338557 avg loss, 0.001000 rate, 0.417575 seconds, 143872 images\n",
            "Loaded: 1.585479 seconds\n",
            "\n",
            " (next mAP calculation at 2300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.96 % \n",
            " 2249: 0.280521, 0.332754 avg loss, 0.001000 rate, 0.421926 seconds, 143936 images\n",
            "Loaded: 1.508818 seconds\n",
            "\n",
            " (next mAP calculation at 2300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.96 % \n",
            " 2250: 0.284545, 0.327933 avg loss, 0.001000 rate, 0.422146 seconds, 144000 images\n",
            "Resizing\n",
            "512 x 512 \n",
            " try to allocate additional workspace_size = 4596.96 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.000173 seconds\n",
            "\n",
            " (next mAP calculation at 2300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.96 % \n",
            " 2251: 0.270881, 0.322227 avg loss, 0.001000 rate, 0.695915 seconds, 144064 images\n",
            "Loaded: 1.332999 seconds\n",
            "\n",
            " (next mAP calculation at 2300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.96 % \n",
            " 2252: 0.334722, 0.323477 avg loss, 0.001000 rate, 0.630890 seconds, 144128 images\n",
            "Loaded: 1.465382 seconds\n",
            "\n",
            " (next mAP calculation at 2300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.96 % \n",
            " 2253: 0.252802, 0.316409 avg loss, 0.001000 rate, 0.632796 seconds, 144192 images\n",
            "Loaded: 1.477068 seconds\n",
            "\n",
            " (next mAP calculation at 2300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.96 % \n",
            " 2254: 0.312305, 0.315999 avg loss, 0.001000 rate, 0.771320 seconds, 144256 images\n",
            "Loaded: 1.370266 seconds\n",
            "\n",
            " (next mAP calculation at 2300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.96 % \n",
            " 2255: 0.298288, 0.314228 avg loss, 0.001000 rate, 0.645599 seconds, 144320 images\n",
            "Loaded: 1.377846 seconds\n",
            "\n",
            " (next mAP calculation at 2300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.96 % \n",
            " 2256: 0.363748, 0.319180 avg loss, 0.001000 rate, 0.755331 seconds, 144384 images\n",
            "Loaded: 1.323514 seconds\n",
            "\n",
            " (next mAP calculation at 2300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.96 % \n",
            " 2257: 0.308809, 0.318143 avg loss, 0.001000 rate, 0.664042 seconds, 144448 images\n",
            "Loaded: 1.330423 seconds\n",
            "\n",
            " (next mAP calculation at 2300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.96 % \n",
            " 2258: 0.367273, 0.323056 avg loss, 0.001000 rate, 0.651509 seconds, 144512 images\n",
            "Loaded: 1.440277 seconds\n",
            "\n",
            " (next mAP calculation at 2300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.96 % \n",
            " 2259: 0.246619, 0.315412 avg loss, 0.001000 rate, 0.655517 seconds, 144576 images\n",
            "Loaded: 1.446249 seconds\n",
            "\n",
            " (next mAP calculation at 2300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.96 % \n",
            " 2260: 0.361576, 0.320029 avg loss, 0.001000 rate, 0.688232 seconds, 144640 images\n",
            "Resizing\n",
            "384 x 384 \n",
            " try to allocate additional workspace_size = 1302.33 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 1.387252 seconds\n",
            "\n",
            " (next mAP calculation at 2300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.96 % \n",
            " 2261: 0.254663, 0.313492 avg loss, 0.001000 rate, 0.351528 seconds, 144704 images\n",
            "Loaded: 1.451386 seconds\n",
            "\n",
            " (next mAP calculation at 2300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.96 % \n",
            " 2262: 0.295941, 0.311737 avg loss, 0.001000 rate, 0.374763 seconds, 144768 images\n",
            "Loaded: 1.438561 seconds\n",
            "\n",
            " (next mAP calculation at 2300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.96 % \n",
            " 2263: 0.261533, 0.306717 avg loss, 0.001000 rate, 0.381283 seconds, 144832 images\n",
            "Loaded: 1.384931 seconds\n",
            "\n",
            " (next mAP calculation at 2300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.96 % \n",
            " 2264: 0.306341, 0.306679 avg loss, 0.001000 rate, 0.358797 seconds, 144896 images\n",
            "Loaded: 1.431200 seconds\n",
            "\n",
            " (next mAP calculation at 2300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.96 % \n",
            " 2265: 0.322306, 0.308242 avg loss, 0.001000 rate, 0.432341 seconds, 144960 images\n",
            "Loaded: 1.503909 seconds\n",
            "\n",
            " (next mAP calculation at 2300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.96 % \n",
            " 2266: 0.275150, 0.304932 avg loss, 0.001000 rate, 0.350436 seconds, 145024 images\n",
            "Loaded: 1.452038 seconds\n",
            "\n",
            " (next mAP calculation at 2300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.96 % \n",
            " 2267: 0.258311, 0.300270 avg loss, 0.001000 rate, 0.373221 seconds, 145088 images\n",
            "Loaded: 1.471564 seconds\n",
            "\n",
            " (next mAP calculation at 2300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.96 % \n",
            " 2268: 0.261309, 0.296374 avg loss, 0.001000 rate, 0.332679 seconds, 145152 images\n",
            "Loaded: 1.367463 seconds\n",
            "\n",
            " (next mAP calculation at 2300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.96 % \n",
            " 2269: 0.295332, 0.296270 avg loss, 0.001000 rate, 0.350590 seconds, 145216 images\n",
            "Loaded: 1.431426 seconds\n",
            "\n",
            " (next mAP calculation at 2300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.96 % \n",
            " 2270: 0.327506, 0.299394 avg loss, 0.001000 rate, 0.338120 seconds, 145280 images\n",
            "Resizing\n",
            "544 x 544 \n",
            " try to allocate additional workspace_size = 4601.28 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.000173 seconds\n",
            "\n",
            " (next mAP calculation at 2300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.96 % \n",
            " 2271: 0.374135, 0.306868 avg loss, 0.001000 rate, 0.808640 seconds, 145344 images\n",
            "Loaded: 1.413750 seconds\n",
            "\n",
            " (next mAP calculation at 2300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.96 % \n",
            " 2272: 0.282808, 0.304462 avg loss, 0.001000 rate, 0.834292 seconds, 145408 images\n",
            "Loaded: 1.302034 seconds\n",
            "\n",
            " (next mAP calculation at 2300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.96 % \n",
            " 2273: 0.343345, 0.308350 avg loss, 0.001000 rate, 0.787862 seconds, 145472 images\n",
            "Loaded: 1.473254 seconds\n",
            "\n",
            " (next mAP calculation at 2300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.96 % \n",
            " 2274: 0.323378, 0.309853 avg loss, 0.001000 rate, 0.745398 seconds, 145536 images\n",
            "Loaded: 1.386976 seconds\n",
            "\n",
            " (next mAP calculation at 2300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.96 % \n",
            " 2275: 0.225415, 0.301409 avg loss, 0.001000 rate, 0.796807 seconds, 145600 images\n",
            "Loaded: 1.432134 seconds\n",
            "\n",
            " (next mAP calculation at 2300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.96 % \n",
            " 2276: 0.371486, 0.308417 avg loss, 0.001000 rate, 0.793639 seconds, 145664 images\n",
            "Loaded: 1.272708 seconds\n",
            "\n",
            " (next mAP calculation at 2300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.96 % \n",
            " 2277: 0.331496, 0.310725 avg loss, 0.001000 rate, 0.763804 seconds, 145728 images\n",
            "Loaded: 1.407644 seconds\n",
            "\n",
            " (next mAP calculation at 2300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.96 % \n",
            " 2278: 0.264971, 0.306149 avg loss, 0.001000 rate, 0.810748 seconds, 145792 images\n",
            "Loaded: 1.344863 seconds\n",
            "\n",
            " (next mAP calculation at 2300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.96 % \n",
            " 2279: 0.282357, 0.303770 avg loss, 0.001000 rate, 0.748194 seconds, 145856 images\n",
            "Loaded: 1.287492 seconds\n",
            "\n",
            " (next mAP calculation at 2300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.96 % \n",
            " 2280: 0.362276, 0.309621 avg loss, 0.001000 rate, 0.883930 seconds, 145920 images\n",
            "Resizing\n",
            "352 x 352 \n",
            " try to allocate additional workspace_size = 1264.58 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.000034 seconds\n",
            "\n",
            " (next mAP calculation at 2300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.96 % \n",
            " 2281: 0.342985, 0.312957 avg loss, 0.001000 rate, 0.474637 seconds, 145984 images\n",
            "Loaded: 1.286811 seconds\n",
            "\n",
            " (next mAP calculation at 2300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.96 % \n",
            " 2282: 0.339963, 0.315658 avg loss, 0.001000 rate, 0.318238 seconds, 146048 images\n",
            "Loaded: 1.352664 seconds\n",
            "\n",
            " (next mAP calculation at 2300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.96 % \n",
            " 2283: 0.383356, 0.322428 avg loss, 0.001000 rate, 0.327326 seconds, 146112 images\n",
            "Loaded: 1.487995 seconds\n",
            "\n",
            " (next mAP calculation at 2300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.96 % \n",
            " 2284: 0.335977, 0.323782 avg loss, 0.001000 rate, 0.308745 seconds, 146176 images\n",
            "Loaded: 1.426604 seconds\n",
            "\n",
            " (next mAP calculation at 2300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.96 % \n",
            " 2285: 0.396832, 0.331087 avg loss, 0.001000 rate, 0.328065 seconds, 146240 images\n",
            "Loaded: 1.484845 seconds\n",
            "\n",
            " (next mAP calculation at 2300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.96 % \n",
            " 2286: 0.314568, 0.329435 avg loss, 0.001000 rate, 0.328307 seconds, 146304 images\n",
            "Loaded: 1.396933 seconds\n",
            "\n",
            " (next mAP calculation at 2300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.96 % \n",
            " 2287: 0.326026, 0.329095 avg loss, 0.001000 rate, 0.310564 seconds, 146368 images\n",
            "Loaded: 1.440752 seconds\n",
            "\n",
            " (next mAP calculation at 2300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.96 % \n",
            " 2288: 0.317260, 0.327911 avg loss, 0.001000 rate, 0.291194 seconds, 146432 images\n",
            "Loaded: 1.445047 seconds\n",
            "\n",
            " (next mAP calculation at 2300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.96 % \n",
            " 2289: 0.385809, 0.333701 avg loss, 0.001000 rate, 0.295568 seconds, 146496 images\n",
            "Loaded: 1.396599 seconds\n",
            "\n",
            " (next mAP calculation at 2300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.96 % \n",
            " 2290: 0.431629, 0.343494 avg loss, 0.001000 rate, 0.298154 seconds, 146560 images\n",
            "Resizing\n",
            "512 x 512 \n",
            " try to allocate additional workspace_size = 4596.96 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.000036 seconds\n",
            "\n",
            " (next mAP calculation at 2300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.96 % \n",
            " 2291: 0.314205, 0.340565 avg loss, 0.001000 rate, 0.765835 seconds, 146624 images\n",
            "Loaded: 1.216702 seconds\n",
            "\n",
            " (next mAP calculation at 2300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.96 % \n",
            " 2292: 0.381366, 0.344645 avg loss, 0.001000 rate, 0.741115 seconds, 146688 images\n",
            "Loaded: 1.338529 seconds\n",
            "\n",
            " (next mAP calculation at 2300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.96 % \n",
            " 2293: 0.372302, 0.347411 avg loss, 0.001000 rate, 0.656776 seconds, 146752 images\n",
            "Loaded: 1.404978 seconds\n",
            "\n",
            " (next mAP calculation at 2300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.96 % \n",
            " 2294: 0.288258, 0.341495 avg loss, 0.001000 rate, 0.729177 seconds, 146816 images\n",
            "Loaded: 1.240193 seconds\n",
            "\n",
            " (next mAP calculation at 2300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.96 % \n",
            " 2295: 0.359303, 0.343276 avg loss, 0.001000 rate, 0.753687 seconds, 146880 images\n",
            "Loaded: 1.243456 seconds\n",
            "\n",
            " (next mAP calculation at 2300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.96 % \n",
            " 2296: 0.378782, 0.346827 avg loss, 0.001000 rate, 0.711852 seconds, 146944 images\n",
            "Loaded: 1.312518 seconds\n",
            "\n",
            " (next mAP calculation at 2300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.96 % \n",
            " 2297: 0.307753, 0.342919 avg loss, 0.001000 rate, 0.641430 seconds, 147008 images\n",
            "Loaded: 1.520574 seconds\n",
            "\n",
            " (next mAP calculation at 2300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.96 % \n",
            " 2298: 0.385285, 0.347156 avg loss, 0.001000 rate, 0.637831 seconds, 147072 images\n",
            "Loaded: 1.396891 seconds\n",
            "\n",
            " (next mAP calculation at 2300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.96 % \n",
            " 2299: 0.370609, 0.349501 avg loss, 0.001000 rate, 0.673058 seconds, 147136 images\n",
            "Loaded: 1.362236 seconds\n",
            "\n",
            " (next mAP calculation at 2300 iterations) \n",
            " Last accuracy mAP@0.5 = 98.96 % \n",
            " 2300: 0.294940, 0.344045 avg loss, 0.001000 rate, 0.651050 seconds, 147200 images\n",
            "Resizing to initial size: 416 x 416 \n",
            " try to allocate additional workspace_size = 1302.33 MB \n",
            " CUDA allocate done! \n",
            "\n",
            " calculation mAP (mean average precision)...\n",
            "368\n",
            " detections_count = 527, unique_truth_count = 374  \n",
            "class_id = 0, name = banana, ap = 98.11%   \t (TP = 364, FP = 8) \n",
            "\n",
            " for thresh = 0.25, precision = 0.98, recall = 0.97, F1-score = 0.98 \n",
            " for thresh = 0.25, TP = 364, FP = 8, FN = 10, average IoU = 76.08 % \n",
            "\n",
            " IoU threshold = 50 %, used Area-Under-Curve for each unique Recall \n",
            " mean average precision (mAP@0.50) = 0.981140, or 98.11 % \n",
            "Total Detection Time: 16.000000 Seconds\n",
            "\n",
            "Set -points flag:\n",
            " `-points 101` for MS COCO \n",
            " `-points 11` for PascalVOC 2007 (uncomment `difficult` in voc.data) \n",
            " `-points 0` (AUC) for ImageNet, PascalVOC 2010-2012, your custom dataset\n",
            "\n",
            " mean_average_precision (mAP@0.5) = 0.981140 \n",
            "Saving weights to /content/gdrive/My Drive/darknet/backup/yolov3-tiny-280120_last.weights\n",
            "Resizing\n",
            "320 x 320 \n",
            " try to allocate additional workspace_size = 868.75 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.073839 seconds\n",
            "\n",
            " (next mAP calculation at 2400 iterations) \n",
            " Last accuracy mAP@0.5 = 98.11 % \n",
            " 2301: 0.448039, 0.354445 avg loss, 0.001000 rate, 0.343143 seconds, 147264 images\n",
            "Loaded: 1.344569 seconds\n",
            "\n",
            " (next mAP calculation at 2400 iterations) \n",
            " Last accuracy mAP@0.5 = 98.11 % \n",
            " 2302: 0.525804, 0.371580 avg loss, 0.001000 rate, 0.256925 seconds, 147328 images\n",
            "Loaded: 1.441985 seconds\n",
            "\n",
            " (next mAP calculation at 2400 iterations) \n",
            " Last accuracy mAP@0.5 = 98.11 % \n",
            " 2303: 0.368813, 0.371304 avg loss, 0.001000 rate, 0.257232 seconds, 147392 images\n",
            "Loaded: 1.437528 seconds\n",
            "\n",
            " (next mAP calculation at 2400 iterations) \n",
            " Last accuracy mAP@0.5 = 98.11 % \n",
            " 2304: 0.401041, 0.374277 avg loss, 0.001000 rate, 0.258721 seconds, 147456 images\n",
            "Loaded: 1.491800 seconds\n",
            "\n",
            " (next mAP calculation at 2400 iterations) \n",
            " Last accuracy mAP@0.5 = 98.11 % \n",
            " 2305: 0.314020, 0.368252 avg loss, 0.001000 rate, 0.271797 seconds, 147520 images\n",
            "Loaded: 1.411267 seconds\n",
            "\n",
            " (next mAP calculation at 2400 iterations) \n",
            " Last accuracy mAP@0.5 = 98.11 % \n",
            " 2306: 0.367189, 0.368145 avg loss, 0.001000 rate, 0.257706 seconds, 147584 images\n",
            "Loaded: 1.422418 seconds\n",
            "\n",
            " (next mAP calculation at 2400 iterations) \n",
            " Last accuracy mAP@0.5 = 98.11 % \n",
            " 2307: 0.347593, 0.366090 avg loss, 0.001000 rate, 0.253556 seconds, 147648 images\n",
            "Loaded: 1.478135 seconds\n",
            "\n",
            " (next mAP calculation at 2400 iterations) \n",
            " Last accuracy mAP@0.5 = 98.11 % \n",
            " 2308: 0.278290, 0.357310 avg loss, 0.001000 rate, 0.261249 seconds, 147712 images\n",
            "Loaded: 1.371406 seconds\n",
            "\n",
            " (next mAP calculation at 2400 iterations) \n",
            " Last accuracy mAP@0.5 = 98.11 % \n",
            " 2309: 0.363319, 0.357911 avg loss, 0.001000 rate, 0.245809 seconds, 147776 images\n",
            "Loaded: 1.393629 seconds\n",
            "\n",
            " (next mAP calculation at 2400 iterations) \n",
            " Last accuracy mAP@0.5 = 98.11 % \n",
            " 2310: 0.360727, 0.358193 avg loss, 0.001000 rate, 0.266877 seconds, 147840 images\n",
            "Resizing\n",
            "480 x 480 \n",
            " try to allocate additional workspace_size = 4592.89 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 0.000040 seconds\n",
            "\n",
            " (next mAP calculation at 2400 iterations) \n",
            " Last accuracy mAP@0.5 = 98.11 % \n",
            " 2311: 0.280070, 0.350380 avg loss, 0.001000 rate, 0.601946 seconds, 147904 images\n",
            "Loaded: 1.401967 seconds\n",
            "\n",
            " (next mAP calculation at 2400 iterations) \n",
            " Last accuracy mAP@0.5 = 98.11 % \n",
            " 2312: 0.428384, 0.358181 avg loss, 0.001000 rate, 0.600262 seconds, 147968 images\n",
            "Loaded: 1.415209 seconds\n",
            "\n",
            " (next mAP calculation at 2400 iterations) \n",
            " Last accuracy mAP@0.5 = 98.11 % \n",
            " 2313: 0.274901, 0.349853 avg loss, 0.001000 rate, 0.566602 seconds, 148032 images\n",
            "Loaded: 1.412902 seconds\n",
            "\n",
            " (next mAP calculation at 2400 iterations) \n",
            " Last accuracy mAP@0.5 = 98.11 % \n",
            " 2314: 0.281428, 0.343010 avg loss, 0.001000 rate, 0.623238 seconds, 148096 images\n",
            "Loaded: 1.368123 seconds\n",
            "\n",
            " (next mAP calculation at 2400 iterations) \n",
            " Last accuracy mAP@0.5 = 98.11 % \n",
            " 2315: 0.320374, 0.340747 avg loss, 0.001000 rate, 0.671094 seconds, 148160 images\n",
            "Loaded: 1.292471 seconds\n",
            "\n",
            " (next mAP calculation at 2400 iterations) \n",
            " Last accuracy mAP@0.5 = 98.11 % \n",
            " 2316: 0.306234, 0.337295 avg loss, 0.001000 rate, 0.637803 seconds, 148224 images\n",
            "Loaded: 2.452148 seconds\n",
            "\n",
            " (next mAP calculation at 2400 iterations) \n",
            " Last accuracy mAP@0.5 = 98.11 % \n",
            " 2317: 0.348041, 0.338370 avg loss, 0.001000 rate, 0.760218 seconds, 148288 images\n",
            "Loaded: 1.382739 seconds\n",
            "\n",
            " (next mAP calculation at 2400 iterations) \n",
            " Last accuracy mAP@0.5 = 98.11 % \n",
            " 2318: 0.357697, 0.340303 avg loss, 0.001000 rate, 0.669542 seconds, 148352 images\n",
            "Loaded: 1.373636 seconds\n",
            "\n",
            " (next mAP calculation at 2400 iterations) \n",
            " Last accuracy mAP@0.5 = 98.11 % \n",
            " 2319: 0.269348, 0.333207 avg loss, 0.001000 rate, 0.651820 seconds, 148416 images\n",
            "Loaded: 1.367908 seconds\n",
            "\n",
            " (next mAP calculation at 2400 iterations) \n",
            " Last accuracy mAP@0.5 = 98.11 % \n",
            " 2320: 0.295885, 0.329475 avg loss, 0.001000 rate, 0.667907 seconds, 148480 images\n",
            "Resizing\n",
            "352 x 352 \n",
            " try to allocate additional workspace_size = 1264.58 MB \n",
            " CUDA allocate done! \n",
            "Loaded: 1.548234 seconds\n",
            "\n",
            " (next mAP calculation at 2400 iterations) \n",
            " Last accuracy mAP@0.5 = 98.11 % \n",
            " 2321: 0.310377, 0.327565 avg loss, 0.001000 rate, 0.304451 seconds, 148544 images\n",
            "Loaded: 1.461992 seconds\n",
            "\n",
            " (next mAP calculation at 2400 iterations) \n",
            " Last accuracy mAP@0.5 = 98.11 % \n",
            " 2322: 0.462203, 0.341029 avg loss, 0.001000 rate, 0.309227 seconds, 148608 images\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "htGMudGvL0o6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!./darknet detector train \"/content/gdrive/My Drive/darknet/obj.data\" \"/content/gdrive/My Drive/darknet/cfg/yolov3-tiny-280120.cfg\" \"/content/gdrive/My Drive/darknet/weights/yolov3-tiny.conv.15\" -dont_show -map\n",
        "# comment the first command and uncomment the second for the further training sessions to start from the generated weights in backup folder \n",
        "#!./darknet detector train \"/content/gdrive/My Drive/darknet/obj.data\" \"/content/gdrive/My Drive/darknet/cfg/yolov3-tiny.cfg\" \"/content/gdrive/My Drive/darknet/backup/yolov3-tiny_last.weights\" -dont_show"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oDuuokOydikw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}