YOLOv5_kicker_Training.ipynb

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  "metadata": {
    "colab": {
      "name": "YOLOv5-kicker-Training.ipynb",
      "provenance": [],
      "collapsed_sections": []
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      "name": "python3",
      "display_name": "Python 3"
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    "language_info": {
      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hrsaDfdVHzxt"
      },
      "source": [
        "# Custom Training with YOLOv5\n",
        "\n",
        "In this tutorial, we assemble a dataset and train a custom YOLOv5 model to recognize the objects in our dataset. To do so we will take the following steps:\n",
        "\n",
        "* Gather a dataset of images and label our dataset\n",
        "* Export our dataset to YOLOv5\n",
        "* Train YOLOv5 to recognize the objects in our dataset\n",
        "* Evaluate our YOLOv5 model's performance\n",
        "* Run test inference to view our model at work\n",
        "\n",
        "\n",
        "\n",
        "![](https://uploads-ssl.webflow.com/5f6bc60e665f54545a1e52a5/615627e5824c9c6195abfda9_computer-vision-cycle.png)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yNveqeA1KXGy"
      },
      "source": [
        "# Step 1: Install Requirements"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kTvDNSILZoN9",
        "outputId": "8cf275d9-de82-49c1-df33-4f222e3870fb"
      },
      "source": [
        "#clone YOLOv5 and \n",
        "!git clone https://github.com/ultralytics/yolov5  # clone repo\n",
        "%cd yolov5\n",
        "%pip install -qr requirements.txt # install dependencies\n",
        "%pip install wandb\n",
        "import torch\n",
        "import os\n",
        "from IPython.display import Image, clear_output  # to display images\n",
        "\n",
        "print(f\"Setup complete. Using torch {torch.__version__} ({torch.cuda.get_device_properties(0).name if torch.cuda.is_available() else 'CPU'})\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'yolov5'...\n",
            "remote: Enumerating objects: 9876, done.\u001b[K\n",
            "remote: Counting objects: 100% (142/142), done.\u001b[K\n",
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            "remote: Total 9876 (delta 93), reused 138 (delta 91), pack-reused 9734\u001b[K\n",
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            "Resolving deltas: 100% (6867/6867), done.\n",
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            "Successfully installed GitPython-3.1.24 configparser-5.0.2 docker-pycreds-0.4.0 gitdb-4.0.7 pathtools-0.1.2 sentry-sdk-1.4.3 shortuuid-1.0.1 smmap-4.0.0 subprocess32-3.5.4 wandb-0.12.5 yaspin-2.1.0\n",
            "Setup complete. Using torch 1.9.0+cu111 (Tesla K80)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        },
        "id": "_THIoBg3yKcx",
        "outputId": "bdfc3ce9-7351-4d78-d859-5457a015b4d7"
      },
      "source": [
        "import wandb\n",
        "\n",
        "wandb.login()\n",
        "from google.colab import drive \n",
        "drive.mount('/content/drive',force_remount=False)\n",
        "# set up environment\n",
        "os.environ[\"DATASET_DIRECTORY\"] = \"/content/drive/MyDrive/kickercamds\""
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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              "                container: iframe,\n",
              "                url: 'https://wandb.ai/authorize'\n",
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              "            const timeout = setTimeout(() => reject(\"Couldn't auto authenticate\"), 5000)\n",
              "            handshake.then(function(child) {\n",
              "                child.on('authorize', data => {\n",
              "                    clearTimeout(timeout)\n",
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        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: Appending key for api.wandb.ai to your netrc file: /root/.netrc\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QGKtOcZg74qJ"
      },
      "source": [
        "#!curl -L \"https://app.roboflow.com/ds/x8MCxicjGs?key=RPrzNjhFFB\" > roboflow.zip; unzip roboflow.zip -d /content/drive/MyDrive/kickercamds; rm roboflow.zip"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X7yAi9hd-T4B"
      },
      "source": [
        "# Step 3: Train Our Custom YOLOv5 model\n",
        "\n",
        "Here, we are able to pass a number of arguments:\n",
        "- **img:** define input image size\n",
        "- **batch:** determine batch size\n",
        "- **epochs:** define the number of training epochs. (Note: often, 3000+ are common here!)\n",
        "- **data:** Our dataset locaiton is saved in the `dataset.location`\n",
        "- **weights:** specify a path to weights to start transfer learning from. Here we choose the generic COCO pretrained checkpoint.\n",
        "- **cache:** cache images for faster training"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "eaFNnxLJbq4J",
        "outputId": "f5ab808a-9e93-4ff6-ce37-121fe4d6832c"
      },
      "source": [
        "!python train.py --noautoanchor --img 428 --rect --batch 128 --epochs 150 --data /content/drive/MyDrive/kickercamds/data.yaml --weights yolov5s.pt --cache"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[34m\u001b[1mtrain: \u001b[0mCaching images (0.0GB ram):   1% 70/6265 [00:25<37:58,  2.72it/s]\n",
            "Error in sys.excepthook:\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/lib/python3.7/linecache.py\", line 47, in getlines\n",
            "    return updatecache(filename, module_globals)\n",
            "  File \"/usr/lib/python3.7/linecache.py\", line 137, in updatecache\n",
            "    lines = fp.readlines()\n",
            "  File \"/usr/lib/python3.7/codecs.py\", line 319, in decode\n",
            "    def decode(self, input, final=False):\n",
            "KeyboardInterrupt\n",
            "\n",
            "Original exception was:\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/lib/python3.7/multiprocessing/pool.py\", line 733, in next\n",
            "    item = self._items.popleft()\n",
            "IndexError: pop from an empty deque\n",
            "\n",
            "During handling of the above exception, another exception occurred:\n",
            "\n",
            "Traceback (most recent call last):\n",
            "  File \"train.py\", line 622, in <module>\n",
            "    main(opt)\n",
            "  File \"train.py\", line 519, in main\n",
            "    train(opt.hyp, opt, device, callbacks)\n",
            "  File \"train.py\", line 213, in train\n",
            "    prefix=colorstr('train: '))\n",
            "  File \"/content/yolov5/utils/datasets.py\", line 108, in create_dataloader\n",
            "    prefix=prefix)\n",
            "  File \"/content/yolov5/utils/datasets.py\", line 496, in __init__\n",
            "    for i, x in pbar:\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/tqdm/std.py\", line 1180, in __iter__\n",
            "    for obj in iterable:\n",
            "  File \"/usr/lib/python3.7/multiprocessing/pool.py\", line 737, in next\n",
            "    self._cond.wait(timeout)\n",
            "  File \"/usr/lib/python3.7/threading.py\", line 296, in wait\n",
            "    waiter.acquire()\n",
            "KeyboardInterrupt\n",
            "Process wandb_internal:\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/lib/python3.7/multiprocessing/process.py\", line 297, in _bootstrap\n",
            "    self.run()\n",
            "  File \"/usr/lib/python3.7/multiprocessing/process.py\", line 99, in run\n",
            "    self._target(*self._args, **self._kwargs)\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/wandb/sdk/internal/internal.py\", line 154, in wandb_internal\n",
            "    thread.join()\n",
            "  File \"/usr/lib/python3.7/threading.py\", line 1044, in join\n",
            "    self._wait_for_tstate_lock()\n",
            "  File \"/usr/lib/python3.7/threading.py\", line 1060, in _wait_for_tstate_lock\n",
            "    elif lock.acquire(block, timeout):\n",
            "KeyboardInterrupt\n"
          ]
        },
        {
          "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-9-122263af5ff1>\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'python train.py --noautoanchor --img 428 --rect --batch 128 --epochs 150 --data /content/drive/MyDrive/kickercamds/data.yaml --weights yolov5s.pt --cache'\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/python3.7/dist-packages/google/colab/_shell.py\u001b[0m in \u001b[0;36msystem\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    100\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    101\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 102\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    103\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    104\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/python3.7/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    445\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    446\u001b[0m   result = _run_command(\n\u001b[0;32m--> 447\u001b[0;31m       shell.var_expand(cmd, depth=2), clear_streamed_output=False)\n\u001b[0m\u001b[1;32m    448\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    449\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/python3.7/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    197\u001b[0m       \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mchild_pty\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    198\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 199\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0m_monitor_process\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparent_pty\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepoll\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcmd\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mupdate_stdin_widget\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    200\u001b[0m   \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    201\u001b[0m     \u001b[0mepoll\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose\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/python3.7/dist-packages/google/colab/_system_commands.py\u001b[0m in \u001b[0;36m_monitor_process\u001b[0;34m(parent_pty, epoll, p, cmd, update_stdin_widget)\u001b[0m\n\u001b[1;32m    227\u001b[0m   \u001b[0;32mwhile\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    228\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--> 229\u001b[0;31m       \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_poll_process\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparent_pty\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepoll\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcmd\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdecoder\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstate\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    230\u001b[0m       \u001b[0;32mif\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    231\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/google/colab/_system_commands.py\u001b[0m in \u001b[0;36m_poll_process\u001b[0;34m(parent_pty, epoll, p, cmd, decoder, state)\u001b[0m\n\u001b[1;32m    274\u001b[0m   \u001b[0moutput_available\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    275\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 276\u001b[0;31m   \u001b[0mevents\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mepoll\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpoll\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[0m\u001b[1;32m    277\u001b[0m   \u001b[0minput_events\u001b[0m \u001b[0;34m=\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    278\u001b[0m   \u001b[0;32mfor\u001b[0m \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mevent\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mevents\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jtmS7_TXFsT3"
      },
      "source": [
        "#Run Inference  With Trained Weights\n",
        "Run inference with a pretrained checkpoint on contents of `test/images` folder downloaded from Roboflow."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TWjjiBcic3Vz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2e52fa7e-e216-452a-c91f-fc22e2f86a2b"
      },
      "source": [
        "!python detect.py --weights runs/train/exp2/weights/best.pt --img 428 --conf 0.1 --source /content/drive/MyDrive/kickercamsmall/test/images"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[34m\u001b[1mdetect: \u001b[0mweights=['runs/train/exp2/weights/best.pt'], source=/content/drive/MyDrive/kickercamsmall/test/images, imgsz=[428, 428], conf_thres=0.1, iou_thres=0.45, max_det=1000, device=, view_img=False, save_txt=False, save_conf=False, save_crop=False, nosave=False, classes=None, agnostic_nms=False, augment=False, visualize=False, update=False, project=runs/detect, name=exp, exist_ok=False, line_thickness=3, hide_labels=False, hide_conf=False, half=False, dnn=False\n",
            "YOLOv5 🚀 v6.0-25-g15e8c4c torch 1.9.0+cu111 CUDA:0 (Tesla K80, 11441.1875MB)\n",
            "\n",
            "Traceback (most recent call last):\n",
            "  File \"detect.py\", line 307, in <module>\n",
            "    main(opt)\n",
            "  File \"detect.py\", line 302, in main\n",
            "    run(**vars(opt))\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/autograd/grad_mode.py\", line 28, in decorate_context\n",
            "    return func(*args, **kwargs)\n",
            "  File \"detect.py\", line 82, in run\n",
            "    model = torch.jit.load(w) if 'torchscript' in w else attempt_load(weights, map_location=device)\n",
            "  File \"/content/yolov5/models/experimental.py\", line 94, in attempt_load\n",
            "    ckpt = torch.load(attempt_download(w), map_location=map_location)  # load\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/serialization.py\", line 600, in load\n",
            "    with _open_zipfile_reader(opened_file) as opened_zipfile:\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/torch/serialization.py\", line 242, in __init__\n",
            "    super(_open_zipfile_reader, self).__init__(torch._C.PyTorchFileReader(name_or_buffer))\n",
            "RuntimeError: PytorchStreamReader failed reading zip archive: failed finding central directory\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZbUn4_b9GCKO"
      },
      "source": [
        "#display inference on ALL test images\n",
        "\n",
        "import glob\n",
        "from IPython.display import Image, display\n",
        "\n",
        "for imageName in glob.glob('runs/detect/exp2/*.jpg'): #assuming JPG\n",
        "    display(Image(filename=imageName))\n",
        "    print(\"\\n\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g8dHcni6CJYt"
      },
      "source": [
        "# Conclusion and Next Steps\n",
        "\n",
        "Congratulations! You've trained a custom YOLOv5 model to recognize your custom objects.\n",
        "\n",
        "To improve you model's performance, we recommend first interating on your datasets coverage and quality. See this guide for [model performance improvement](https://github.com/ultralytics/yolov5/wiki/Tips-for-Best-Training-Results).\n",
        "\n",
        "To deploy your model to an application, see this guide on [exporting your model to deployment destinations](https://github.com/ultralytics/yolov5/issues/251).\n",
        "\n",
        "Once your model is in production, you will want to continually iterate and improve on your dataset and model via [active learning](https://blog.roboflow.com/what-is-active-learning/)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7iiObB2WCMh6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 340
        },
        "outputId": "c11201ed-22bc-473b-c72d-b6e9e47787e0"
      },
      "source": [
        "#export your model's weights for future use\n",
        "from google.colab import files\n",
        "files.download('./runs/train/exp/weights/best.pt')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "FileNotFoundError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-4-f6a33f923fca>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m#export your model's weights for future use\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mgoogle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolab\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mfiles\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mfiles\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdownload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'./runs/train/exp/weights/best.pt'\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/python3.7/dist-packages/google/colab/files.py\u001b[0m in \u001b[0;36mdownload\u001b[0;34m(filename)\u001b[0m\n\u001b[1;32m    141\u001b[0m       \u001b[0;32mraise\u001b[0m \u001b[0mOSError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    142\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 143\u001b[0;31m       \u001b[0;32mraise\u001b[0m \u001b[0mFileNotFoundError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# pylint: disable=undefined-variable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    144\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    145\u001b[0m   \u001b[0mcomm_manager\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[0mkernel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcomm_manager\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mFileNotFoundError\u001b[0m: Cannot find file: ./runs/train/exp/weights/best.pt"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rNn-obvOGITm"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}