Yolo Package Detection Tutorial.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Install and Import Dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting torch==1.8.1\n",
      "  Using cached torch-1.8.1-cp39-none-macosx_10_9_x86_64.whl (119.6 MB)\n",
      "Collecting torchvision==0.9.1\n",
      "  Using cached torchvision-0.9.1-cp39-cp39-macosx_10_9_x86_64.whl (13.1 MB)\n",
      "Collecting torchaudio==0.8.1\n",
      "  Using cached torchaudio-0.8.1-cp39-cp39-macosx_10_9_x86_64.whl (1.5 MB)\n",
      "Collecting typing-extensions\n",
      "  Using cached typing_extensions-4.1.1-py3-none-any.whl (26 kB)\n",
      "Collecting numpy\n",
      "  Using cached numpy-1.22.3-cp39-cp39-macosx_10_14_x86_64.whl (17.6 MB)\n",
      "Collecting pillow>=4.1.1\n",
      "  Using cached Pillow-9.0.1-cp39-cp39-macosx_10_10_x86_64.whl (3.0 MB)\n",
      "Installing collected packages: typing-extensions, pillow, numpy, torch, torchvision, torchaudio\n",
      "Successfully installed numpy-1.22.3 pillow-9.0.1 torch-1.8.1 torchaudio-0.8.1 torchvision-0.9.1 typing-extensions-4.1.1\n"
     ]
    }
   ],
   "source": [
    "!pip install torch==1.8.1 torchvision==0.9.1 torchaudio==0.8.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cloning into 'yolov5'...\n",
      "remote: Enumerating objects: 11525, done.\u001b[K\n",
      "remote: Total 11525 (delta 0), reused 0 (delta 0), pack-reused 11525\u001b[K\n",
      "Receiving objects: 100% (11525/11525), 11.32 MiB | 3.33 MiB/s, done.\n",
      "Resolving deltas: 100% (7966/7966), done.\n"
     ]
    }
   ],
   "source": [
    "!git clone https://github.com/ultralytics/yolov5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting matplotlib>=3.2.2\n",
      "  Using cached matplotlib-3.5.1-cp39-cp39-macosx_10_9_x86_64.whl (7.3 MB)\n",
      "Requirement already satisfied: numpy>=1.18.5 in /Users/jonathanli/Dev/YOLO-Package-Detection/ypd/lib/python3.9/site-packages (from -r requirements.txt (line 5)) (1.22.3)\n",
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      "  Using cached opencv_python-4.5.5.64-cp36-abi3-macosx_10_15_x86_64.whl (46.3 MB)\n",
      "Requirement already satisfied: Pillow>=7.1.2 in /Users/jonathanli/Dev/YOLO-Package-Detection/ypd/lib/python3.9/site-packages (from -r requirements.txt (line 7)) (9.0.1)\n",
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      "Requirement already satisfied: torchvision>=0.8.1 in /Users/jonathanli/Dev/YOLO-Package-Detection/ypd/lib/python3.9/site-packages (from -r requirements.txt (line 12)) (0.9.1)\n",
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      "Collecting packaging>=20.0\n",
      "  Using cached packaging-21.3-py3-none-any.whl (40 kB)\n",
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      "Requirement already satisfied: setuptools>=41.0.0 in /Users/jonathanli/Dev/YOLO-Package-Detection/ypd/lib/python3.9/site-packages (from tensorboard>=2.4.1->-r requirements.txt (line 16)) (60.5.0)\n",
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      "  Using cached tensorboard_data_server-0.6.1-py3-none-macosx_10_9_x86_64.whl (3.5 MB)\n",
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      "Requirement already satisfied: six in /Users/jonathanli/Dev/YOLO-Package-Detection/ypd/lib/python3.9/site-packages (from absl-py>=0.4->tensorboard>=2.4.1->-r requirements.txt (line 16)) (1.16.0)\n",
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      "Collecting pyasn1<0.5.0,>=0.4.6\n",
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      "Collecting oauthlib>=3.0.0\n",
      "  Using cached oauthlib-3.2.0-py3-none-any.whl (151 kB)\n",
      "Installing collected packages: tensorboard-plugin-wit, pytz, pyasn1, certifi, zipp, wheel, werkzeug, urllib3, tqdm, tensorboard-data-server, scipy, rsa, PyYAML, pyparsing, pyasn1-modules, protobuf, opencv-python, oauthlib, kiwisolver, idna, grpcio, fonttools, cycler, charset-normalizer, cachetools, absl-py, thop, requests, pandas, packaging, importlib-metadata, google-auth, requests-oauthlib, matplotlib, markdown, seaborn, google-auth-oauthlib, tensorboard\n",
      "Successfully installed PyYAML-6.0 absl-py-1.0.0 cachetools-5.0.0 certifi-2021.10.8 charset-normalizer-2.0.12 cycler-0.11.0 fonttools-4.31.1 google-auth-2.6.2 google-auth-oauthlib-0.4.6 grpcio-1.44.0 idna-3.3 importlib-metadata-4.11.3 kiwisolver-1.4.0 markdown-3.3.6 matplotlib-3.5.1 oauthlib-3.2.0 opencv-python-4.5.5.64 packaging-21.3 pandas-1.4.1 protobuf-3.19.4 pyasn1-0.4.8 pyasn1-modules-0.2.8 pyparsing-3.0.7 pytz-2022.1 requests-2.27.1 requests-oauthlib-1.3.1 rsa-4.8 scipy-1.8.0 seaborn-0.11.2 tensorboard-2.8.0 tensorboard-data-server-0.6.1 tensorboard-plugin-wit-1.8.1 thop-0.0.31.post2005241907 tqdm-4.63.0 urllib3-1.26.9 werkzeug-2.0.3 wheel-0.37.1 zipp-3.7.0\n"
     ]
    }
   ],
   "source": [
    "!cd yolov5 && pip install -r requirements.txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jonathanli/Dev/YOLO-Package-Detection/ypd/lib/python3.9/site-packages/tqdm/auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "from matplotlib import pyplot as plt\n",
    "import numpy as np\n",
    "import cv2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Load Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using cache found in /Users/jonathanli/.cache/torch/hub/ultralytics_yolov5_master\n",
      "fatal: not a git repository (or any of the parent directories): .git\n",
      "YOLOv5 🚀 2022-3-21 torch 1.8.1 CPU\n",
      "\n",
      "Fusing layers... \n",
      "Model Summary: 213 layers, 7225885 parameters, 0 gradients, 16.5 GFLOPs\n",
      "Adding AutoShape... \n"
     ]
    }
   ],
   "source": [
    "model = torch.hub.load('ultralytics/yolov5', 'yolov5s')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AutoShape(\n",
       "  (model): DetectMultiBackend(\n",
       "    (model): Model(\n",
       "      (model): Sequential(\n",
       "        (0): Conv(\n",
       "          (conv): Conv2d(3, 32, kernel_size=(6, 6), stride=(2, 2), padding=(2, 2))\n",
       "          (act): SiLU(inplace=True)\n",
       "        )\n",
       "        (1): Conv(\n",
       "          (conv): Conv2d(32, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n",
       "          (act): SiLU(inplace=True)\n",
       "        )\n",
       "        (2): C3(\n",
       "          (cv1): Conv(\n",
       "            (conv): Conv2d(64, 32, kernel_size=(1, 1), stride=(1, 1))\n",
       "            (act): SiLU(inplace=True)\n",
       "          )\n",
       "          (cv2): Conv(\n",
       "            (conv): Conv2d(64, 32, kernel_size=(1, 1), stride=(1, 1))\n",
       "            (act): SiLU(inplace=True)\n",
       "          )\n",
       "          (cv3): Conv(\n",
       "            (conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1))\n",
       "            (act): SiLU(inplace=True)\n",
       "          )\n",
       "          (m): Sequential(\n",
       "            (0): Bottleneck(\n",
       "              (cv1): Conv(\n",
       "                (conv): Conv2d(32, 32, kernel_size=(1, 1), stride=(1, 1))\n",
       "                (act): SiLU(inplace=True)\n",
       "              )\n",
       "              (cv2): Conv(\n",
       "                (conv): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "                (act): SiLU(inplace=True)\n",
       "              )\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "        (3): Conv(\n",
       "          (conv): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n",
       "          (act): SiLU(inplace=True)\n",
       "        )\n",
       "        (4): C3(\n",
       "          (cv1): Conv(\n",
       "            (conv): Conv2d(128, 64, kernel_size=(1, 1), stride=(1, 1))\n",
       "            (act): SiLU(inplace=True)\n",
       "          )\n",
       "          (cv2): Conv(\n",
       "            (conv): Conv2d(128, 64, kernel_size=(1, 1), stride=(1, 1))\n",
       "            (act): SiLU(inplace=True)\n",
       "          )\n",
       "          (cv3): Conv(\n",
       "            (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))\n",
       "            (act): SiLU(inplace=True)\n",
       "          )\n",
       "          (m): Sequential(\n",
       "            (0): Bottleneck(\n",
       "              (cv1): Conv(\n",
       "                (conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1))\n",
       "                (act): SiLU(inplace=True)\n",
       "              )\n",
       "              (cv2): Conv(\n",
       "                (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "                (act): SiLU(inplace=True)\n",
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       "            )\n",
       "            (1): Bottleneck(\n",
       "              (cv1): Conv(\n",
       "                (conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1))\n",
       "                (act): SiLU(inplace=True)\n",
       "              )\n",
       "              (cv2): Conv(\n",
       "                (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "                (act): SiLU(inplace=True)\n",
       "              )\n",
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       "        )\n",
       "        (5): Conv(\n",
       "          (conv): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n",
       "          (act): SiLU(inplace=True)\n",
       "        )\n",
       "        (6): C3(\n",
       "          (cv1): Conv(\n",
       "            (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1))\n",
       "            (act): SiLU(inplace=True)\n",
       "          )\n",
       "          (cv2): Conv(\n",
       "            (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1))\n",
       "            (act): SiLU(inplace=True)\n",
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       "          (cv3): Conv(\n",
       "            (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))\n",
       "            (act): SiLU(inplace=True)\n",
       "          )\n",
       "          (m): Sequential(\n",
       "            (0): Bottleneck(\n",
       "              (cv1): Conv(\n",
       "                (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))\n",
       "                (act): SiLU(inplace=True)\n",
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       "              (cv2): Conv(\n",
       "                (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
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       "            (1): Bottleneck(\n",
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       "                (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))\n",
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       "              (cv2): Conv(\n",
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       "            )\n",
       "            (2): Bottleneck(\n",
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       "              (cv2): Conv(\n",
       "                (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "                (act): SiLU(inplace=True)\n",
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       "            )\n",
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       "        )\n",
       "        (7): Conv(\n",
       "          (conv): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n",
       "          (act): SiLU(inplace=True)\n",
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       "        (8): C3(\n",
       "          (cv1): Conv(\n",
       "            (conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1))\n",
       "            (act): SiLU(inplace=True)\n",
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       "          (cv2): Conv(\n",
       "            (conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1))\n",
       "            (act): SiLU(inplace=True)\n",
       "          )\n",
       "          (cv3): Conv(\n",
       "            (conv): Conv2d(512, 512, kernel_size=(1, 1), stride=(1, 1))\n",
       "            (act): SiLU(inplace=True)\n",
       "          )\n",
       "          (m): Sequential(\n",
       "            (0): Bottleneck(\n",
       "              (cv1): Conv(\n",
       "                (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))\n",
       "                (act): SiLU(inplace=True)\n",
       "              )\n",
       "              (cv2): Conv(\n",
       "                (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "                (act): SiLU(inplace=True)\n",
       "              )\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "        (9): SPPF(\n",
       "          (cv1): Conv(\n",
       "            (conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1))\n",
       "            (act): SiLU(inplace=True)\n",
       "          )\n",
       "          (cv2): Conv(\n",
       "            (conv): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1))\n",
       "            (act): SiLU(inplace=True)\n",
       "          )\n",
       "          (m): MaxPool2d(kernel_size=5, stride=1, padding=2, dilation=1, ceil_mode=False)\n",
       "        )\n",
       "        (10): Conv(\n",
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       "          (act): SiLU(inplace=True)\n",
       "        )\n",
       "        (11): Upsample(scale_factor=2.0, mode=nearest)\n",
       "        (12): Concat()\n",
       "        (13): C3(\n",
       "          (cv1): Conv(\n",
       "            (conv): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1))\n",
       "            (act): SiLU(inplace=True)\n",
       "          )\n",
       "          (cv2): Conv(\n",
       "            (conv): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1))\n",
       "            (act): SiLU(inplace=True)\n",
       "          )\n",
       "          (cv3): Conv(\n",
       "            (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))\n",
       "            (act): SiLU(inplace=True)\n",
       "          )\n",
       "          (m): Sequential(\n",
       "            (0): Bottleneck(\n",
       "              (cv1): Conv(\n",
       "                (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))\n",
       "                (act): SiLU(inplace=True)\n",
       "              )\n",
       "              (cv2): Conv(\n",
       "                (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "                (act): SiLU(inplace=True)\n",
       "              )\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "        (14): Conv(\n",
       "          (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1))\n",
       "          (act): SiLU(inplace=True)\n",
       "        )\n",
       "        (15): Upsample(scale_factor=2.0, mode=nearest)\n",
       "        (16): Concat()\n",
       "        (17): C3(\n",
       "          (cv1): Conv(\n",
       "            (conv): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1))\n",
       "            (act): SiLU(inplace=True)\n",
       "          )\n",
       "          (cv2): Conv(\n",
       "            (conv): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1))\n",
       "            (act): SiLU(inplace=True)\n",
       "          )\n",
       "          (cv3): Conv(\n",
       "            (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))\n",
       "            (act): SiLU(inplace=True)\n",
       "          )\n",
       "          (m): Sequential(\n",
       "            (0): Bottleneck(\n",
       "              (cv1): Conv(\n",
       "                (conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1))\n",
       "                (act): SiLU(inplace=True)\n",
       "              )\n",
       "              (cv2): Conv(\n",
       "                (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "                (act): SiLU(inplace=True)\n",
       "              )\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "        (18): Conv(\n",
       "          (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n",
       "          (act): SiLU(inplace=True)\n",
       "        )\n",
       "        (19): Concat()\n",
       "        (20): C3(\n",
       "          (cv1): Conv(\n",
       "            (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1))\n",
       "            (act): SiLU(inplace=True)\n",
       "          )\n",
       "          (cv2): Conv(\n",
       "            (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1))\n",
       "            (act): SiLU(inplace=True)\n",
       "          )\n",
       "          (cv3): Conv(\n",
       "            (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))\n",
       "            (act): SiLU(inplace=True)\n",
       "          )\n",
       "          (m): Sequential(\n",
       "            (0): Bottleneck(\n",
       "              (cv1): Conv(\n",
       "                (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))\n",
       "                (act): SiLU(inplace=True)\n",
       "              )\n",
       "              (cv2): Conv(\n",
       "                (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "                (act): SiLU(inplace=True)\n",
       "              )\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "        (21): Conv(\n",
       "          (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n",
       "          (act): SiLU(inplace=True)\n",
       "        )\n",
       "        (22): Concat()\n",
       "        (23): C3(\n",
       "          (cv1): Conv(\n",
       "            (conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1))\n",
       "            (act): SiLU(inplace=True)\n",
       "          )\n",
       "          (cv2): Conv(\n",
       "            (conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1))\n",
       "            (act): SiLU(inplace=True)\n",
       "          )\n",
       "          (cv3): Conv(\n",
       "            (conv): Conv2d(512, 512, kernel_size=(1, 1), stride=(1, 1))\n",
       "            (act): SiLU(inplace=True)\n",
       "          )\n",
       "          (m): Sequential(\n",
       "            (0): Bottleneck(\n",
       "              (cv1): Conv(\n",
       "                (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))\n",
       "                (act): SiLU(inplace=True)\n",
       "              )\n",
       "              (cv2): Conv(\n",
       "                (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "                (act): SiLU(inplace=True)\n",
       "              )\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "        (24): Detect(\n",
       "          (m): ModuleList(\n",
       "            (0): Conv2d(128, 255, kernel_size=(1, 1), stride=(1, 1))\n",
       "            (1): Conv2d(256, 255, kernel_size=(1, 1), stride=(1, 1))\n",
       "            (2): Conv2d(512, 255, kernel_size=(1, 1), stride=(1, 1))\n",
       "          )\n",
       "        )\n",
       "      )\n",
       "    )\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Make Detections with Images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2022-03-20 18:30:08--  https://upload.wikimedia.org/wikipedia/commons/thumb/e/e4/Cars_in_traffic_in_Auckland%2C_New_Zealand_-_copyright-free_photo_released_to_public_domain.jpg/800px-Cars_in_traffic_in_Auckland%2C_New_Zealand_-_copyright-free_photo_released_to_public_domain.jpg\n",
      "Resolving upload.wikimedia.org (upload.wikimedia.org)... 198.35.26.112, 2620:0:863:ed1a::2:b\n",
      "Connecting to upload.wikimedia.org (upload.wikimedia.org)|198.35.26.112|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 138573 (135K) [image/jpeg]\n",
      "Saving to: ‘800px-Cars_in_traffic_in_Auckland,_New_Zealand_-_copyright-free_photo_released_to_public_domain.jpg’\n",
      "\n",
      "800px-Cars_in_traff 100%[===================>] 135.33K  --.-KB/s    in 0.09s   \n",
      "\n",
      "2022-03-20 18:30:09 (1.44 MB/s) - ‘800px-Cars_in_traffic_in_Auckland,_New_Zealand_-_copyright-free_photo_released_to_public_domain.jpg’ saved [138573/138573]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!wget 'https://upload.wikimedia.org/wikipedia/commons/thumb/e/e4/Cars_in_traffic_in_Auckland%2C_New_Zealand_-_copyright-free_photo_released_to_public_domain.jpg/800px-Cars_in_traffic_in_Auckland%2C_New_Zealand_-_copyright-free_photo_released_to_public_domain.jpg'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "img = '800px-Cars_in_traffic_in_Auckland,_New_Zealand_-_copyright-free_photo_released_to_public_domain.jpg'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "image 1/1: 533x800 2 persons, 28 cars, 5 trucks\n",
      "Speed: 26.0ms pre-process, 251.2ms inference, 3.3ms NMS per image at shape (1, 3, 448, 640)\n"
     ]
    }
   ],
   "source": [
    "results = model(img)\n",
    "results.print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline \n",
    "plt.imshow(np.squeeze(results.render()))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([[[251, 216, 174],\n",
       "         [254, 220, 175],\n",
       "         [246, 213, 168],\n",
       "         ...,\n",
       "         [127, 149, 146],\n",
       "         [ 67,  79,  55],\n",
       "         [ 64,  70,  34]],\n",
       " \n",
       "        [[235, 201, 156],\n",
       "         [233, 200, 155],\n",
       "         [221, 189, 142],\n",
       "         ...,\n",
       "         [120, 142, 140],\n",
       "         [ 82,  92,  67],\n",
       "         [ 46,  52,  16]],\n",
       " \n",
       "        [[252, 220, 171],\n",
       "         [245, 213, 164],\n",
       "         [212, 180, 131],\n",
       "         ...,\n",
       "         [135, 155, 154],\n",
       "         [ 66,  74,  50],\n",
       "         [ 58,  62,  25]],\n",
       " \n",
       "        ...,\n",
       " \n",
       "        [[ 61,  69,  80],\n",
       "         [ 64,  72,  83],\n",
       "         [ 61,  69,  80],\n",
       "         ...,\n",
       "         [ 47,  52,  56],\n",
       "         [ 48,  53,  57],\n",
       "         [ 49,  54,  58]],\n",
       " \n",
       "        [[ 56,  64,  75],\n",
       "         [ 72,  80,  91],\n",
       "         [ 70,  78,  89],\n",
       "         ...,\n",
       "         [ 48,  53,  57],\n",
       "         [ 48,  53,  57],\n",
       "         [ 50,  55,  59]],\n",
       " \n",
       "        [[ 64,  72,  83],\n",
       "         [ 76,  84,  95],\n",
       "         [ 63,  71,  82],\n",
       "         ...,\n",
       "         [ 49,  54,  58],\n",
       "         [ 49,  54,  58],\n",
       "         [ 50,  55,  59]]], dtype=uint8)]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.render()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Real Time Detections"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# cap = cv2.VideoCapture(0)\n",
    "# while cap.isOpened():\n",
    "#     ret, frame = cap.read()\n",
    "    \n",
    "#     # Make detections \n",
    "#     results = model(frame)\n",
    "    \n",
    "#     cv2.imshow('YOLO', np.squeeze(results.render()))\n",
    "    \n",
    "#     if cv2.waitKey(10) & 0xFF == ord('q'):\n",
    "#         break\n",
    "# cap.release()\n",
    "# cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. Train from scratch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import uuid   # Unique identifier\n",
    "import os\n",
    "import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "!mkdir -p data/images\n",
    "!mkdir -p data/labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "IMAGES_PATH = os.path.join('data', 'images') #/data/images\n",
    "labels = ['package']\n",
    "number_imgs = 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting images for package\n",
      "Collecting images for package, image number 0\n",
      "Collecting images for package, image number 1\n",
      "Collecting images for package, image number 2\n",
      "Collecting images for package, image number 3\n",
      "Collecting images for package, image number 4\n"
     ]
    }
   ],
   "source": [
    "cap = cv2.VideoCapture(0)\n",
    "# Loop through labels\n",
    "for label in labels:\n",
    "    print('Collecting images for {}'.format(label))\n",
    "    time.sleep(5)\n",
    "    \n",
    "    # Loop through image range\n",
    "    for img_num in range(number_imgs):\n",
    "        print('Collecting images for {}, image number {}'.format(label, img_num))\n",
    "        \n",
    "        # Webcam feed\n",
    "        ret, frame = cap.read()\n",
    "        \n",
    "        # Naming out image path\n",
    "        imgname = os.path.join(IMAGES_PATH, label+'.'+str(uuid.uuid1())+'.jpg')\n",
    "        \n",
    "        # Writes out image to file \n",
    "        cv2.imwrite(imgname, frame)\n",
    "        \n",
    "        # Render to the screen\n",
    "        cv2.imshow('Image Collection', frame)\n",
    "        \n",
    "        # 2 second delay between captures\n",
    "        time.sleep(2)\n",
    "        \n",
    "        if cv2.waitKey(10) & 0xFF == ord('q'):\n",
    "            break\n",
    "cap.release()\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data/images/package.d42a93d4-a8b6-11ec-8bcb-34363b16023a.jpg\n"
     ]
    }
   ],
   "source": [
    "print(os.path.join(IMAGES_PATH, labels[0]+'.'+str(uuid.uuid1())+'.jpg'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting images for package\n",
      "Collecting images for package, image number 0\n",
      "data/images/package.1b8463ea-a8b7-11ec-8bcb-34363b16023a.jpg\n",
      "Collecting images for package, image number 1\n",
      "data/images/package.1b846a70-a8b7-11ec-8bcb-34363b16023a.jpg\n",
      "Collecting images for package, image number 2\n",
      "data/images/package.1b846ca0-a8b7-11ec-8bcb-34363b16023a.jpg\n",
      "Collecting images for package, image number 3\n",
      "data/images/package.1b846e94-a8b7-11ec-8bcb-34363b16023a.jpg\n",
      "Collecting images for package, image number 4\n",
      "data/images/package.1b84707e-a8b7-11ec-8bcb-34363b16023a.jpg\n"
     ]
    }
   ],
   "source": [
    "for label in labels:\n",
    "    print('Collecting images for {}'.format(label))\n",
    "    for img_num in range(number_imgs):\n",
    "        print('Collecting images for {}, image number {}'.format(label, img_num))\n",
    "        imgname = os.path.join(IMAGES_PATH, label+'.'+str(uuid.uuid1())+'.jpg')\n",
    "        print(imgname)   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cloning into 'labelImg'...\n",
      "remote: Enumerating objects: 1975, done.\u001b[K\n",
      "remote: Counting objects: 100% (214/214), done.\u001b[K\n",
      "remote: Compressing objects: 100% (144/144), done.\u001b[K\n",
      "remote: Total 1975 (delta 115), reused 134 (delta 62), pack-reused 1761\u001b[K\n",
      "Receiving objects: 100% (1975/1975), 232.85 MiB | 1.77 MiB/s, done.\n",
      "Resolving deltas: 100% (1162/1162), done.\n"
     ]
    }
   ],
   "source": [
    "!git clone https://github.com/tzutalin/labelImg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting pyqt5\n",
      "  Using cached PyQt5-5.15.6-cp36-abi3-macosx_10_13_x86_64.whl (7.0 MB)\n",
      "Collecting lxml\n",
      "  Using cached lxml-4.8.0-cp39-cp39-macosx_10_15_x86_64.whl (4.6 MB)\n",
      "Collecting PyQt5-sip<13,>=12.8\n",
      "  Using cached PyQt5_sip-12.9.1-cp39-cp39-macosx_10_9_x86_64.whl (83 kB)\n",
      "Collecting PyQt5-Qt5>=5.15.2\n",
      "  Using cached PyQt5_Qt5-5.15.2-py3-none-macosx_10_13_intel.whl (40.5 MB)\n",
      "Installing collected packages: PyQt5-Qt5, PyQt5-sip, lxml, pyqt5\n",
      "Successfully installed PyQt5-Qt5-5.15.2 PyQt5-sip-12.9.1 lxml-4.8.0 pyqt5-5.15.6\n"
     ]
    }
   ],
   "source": [
    "!pip install pyqt5 lxml --upgrade\n",
    "!cd labelImg && pyrcc5 -o libs/resources.py resources.qrc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Image:/Users/jonathanli/Desktop/train/IMG_6817_jpg.rf.126874422a2e4c8d3b78bbeddea58455.jpg -> Annotation:/Users/jonathanli/Dev/Xilinx Package Detection/YOLO-Package-Detection/data/labels/IMG_6817_jpg.rf.126874422a2e4c8d3b78bbeddea58455.txt\r\n"
     ]
    }
   ],
   "source": [
    "!cd labelImg && python3 labelImg.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mtrain: \u001b[0mweights=yolov5s.pt, cfg=, data=dataset.yml, hyp=data/hyps/hyp.scratch-low.yaml, epochs=500, batch_size=16, imgsz=320, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, evolve=None, bucket=, cache=None, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=2, project=runs/train, name=exp, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=-1, artifact_alias=latest\n",
      "\u001b[34m\u001b[1mgithub: \u001b[0mup to date with https://github.com/ultralytics/yolov5 ✅\n",
      "YOLOv5 🚀 v6.1-51-g9cd89b7 torch 1.8.1 CPU\n",
      "\n",
      "\u001b[34m\u001b[1mhyperparameters: \u001b[0mlr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0\n",
      "\u001b[34m\u001b[1mWeights & Biases: \u001b[0mrun 'pip install wandb' to automatically track and visualize YOLOv5 🚀 runs (RECOMMENDED)\n",
      "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/train', view at http://localhost:6006/\n",
      "Overriding model.yaml nc=80 with nc=1\n",
      "\n",
      "                 from  n    params  module                                  arguments                     \n",
      "  0                -1  1      3520  models.common.Conv                      [3, 32, 6, 2, 2]              \n",
      "  1                -1  1     18560  models.common.Conv                      [32, 64, 3, 2]                \n",
      "  2                -1  1     18816  models.common.C3                        [64, 64, 1]                   \n",
      "  3                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               \n",
      "  4                -1  2    115712  models.common.C3                        [128, 128, 2]                 \n",
      "  5                -1  1    295424  models.common.Conv                      [128, 256, 3, 2]              \n",
      "  6                -1  3    625152  models.common.C3                        [256, 256, 3]                 \n",
      "  7                -1  1   1180672  models.common.Conv                      [256, 512, 3, 2]              \n",
      "  8                -1  1   1182720  models.common.C3                        [512, 512, 1]                 \n",
      "  9                -1  1    656896  models.common.SPPF                      [512, 512, 5]                 \n",
      " 10                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
      " 11                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 12           [-1, 6]  1         0  models.common.Concat                    [1]                           \n",
      " 13                -1  1    361984  models.common.C3                        [512, 256, 1, False]          \n",
      " 14                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n",
      " 15                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 16           [-1, 4]  1         0  models.common.Concat                    [1]                           \n",
      " 17                -1  1     90880  models.common.C3                        [256, 128, 1, False]          \n",
      " 18                -1  1    147712  models.common.Conv                      [128, 128, 3, 2]              \n",
      " 19          [-1, 14]  1         0  models.common.Concat                    [1]                           \n",
      " 20                -1  1    296448  models.common.C3                        [256, 256, 1, False]          \n",
      " 21                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]              \n",
      " 22          [-1, 10]  1         0  models.common.Concat                    [1]                           \n",
      " 23                -1  1   1182720  models.common.C3                        [512, 512, 1, False]          \n",
      " 24      [17, 20, 23]  1     16182  models.yolo.Detect                      [1, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]\n",
      "Model Summary: 270 layers, 7022326 parameters, 7022326 gradients, 15.8 GFLOPs\n",
      "\n",
      "Transferred 343/349 items from yolov5s.pt\n",
      "Scaled weight_decay = 0.0005\n",
      "\u001b[34m\u001b[1moptimizer:\u001b[0m SGD with parameter groups 57 weight (no decay), 60 weight, 60 bias\n",
      "\u001b[34m\u001b[1mtrain: \u001b[0mScanning '/Users/jonathanli/Dev/YOLO-Package-Detection/data/train/labels'\u001b[0m\n",
      "\u001b[34m\u001b[1mtrain: \u001b[0mNew cache created: /Users/jonathanli/Dev/YOLO-Package-Detection/data/train/labels.cache\n",
      "\u001b[34m\u001b[1mval: \u001b[0mScanning '/Users/jonathanli/Dev/YOLO-Package-Detection/data/val/labels' ima\u001b[0m\n",
      "\u001b[34m\u001b[1mval: \u001b[0mNew cache created: /Users/jonathanli/Dev/YOLO-Package-Detection/data/val/labels.cache\n",
      "Plotting labels to runs/train/exp6/labels.jpg... \n",
      "\n",
      "\u001b[34m\u001b[1mAutoAnchor: \u001b[0m5.54 anchors/target, 1.000 Best Possible Recall (BPR). Current anchors are a good fit to dataset ✅\n",
      "Image sizes 320 train, 320 val\n",
      "Using 2 dataloader workers\n",
      "Logging results to \u001b[1mruns/train/exp6\u001b[0m\n",
      "Starting training for 500 epochs...\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "     0/499        0G    0.1233   0.01391         0        24       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5     0.0904        0.2     0.0193    0.00746\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "     1/499        0G    0.1203   0.01458         0        23       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5      0.099        0.2     0.0211    0.00814\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "     2/499        0G    0.1202    0.0145         0        20       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5     0.0908        0.2     0.0195    0.00381\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "     3/499        0G    0.1156   0.01652         0        24       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5     0.0615        0.2     0.0147    0.00165\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "     4/499        0G    0.1151   0.01522         0        17       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5     0.0939        0.2     0.0239    0.00264\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "     5/499        0G    0.1124   0.01473         0        20       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5    0.00518        0.4    0.00363    0.00077\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "     6/499        0G    0.1052   0.01672         0        20       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5      0.996        0.2        0.2     0.0788\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "     7/499        0G    0.1042   0.01832         0        23       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5      0.997        0.2      0.198     0.0785\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "     8/499        0G   0.09588   0.02138         0        20       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5      0.996        0.2      0.199      0.059\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "     9/499        0G   0.09993   0.02048         0        23       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5      0.993        0.2      0.232     0.0657\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "    10/499        0G   0.09695   0.01943         0        19       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5      0.995        0.2      0.243     0.0682\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "    11/499        0G   0.09484   0.02254         0        25       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5       0.99        0.2      0.244     0.0638\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "    12/499        0G   0.09283   0.01908         0        20       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5      0.974        0.2      0.207       0.06\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "    13/499        0G   0.09276   0.02287         0        28       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5      0.985        0.2      0.202     0.0406\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "    14/499        0G   0.09053   0.02007         0        20       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5      0.983        0.2      0.219     0.0452\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "    15/499        0G   0.08811   0.02319         0        21       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5      0.972        0.2      0.288      0.092\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "    16/499        0G   0.08434   0.02172         0        21       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1      0.593      0.598      0.159\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "    17/499        0G   0.09125   0.02014         0        20       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5      0.489        0.4      0.228      0.052\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "    18/499        0G   0.08506   0.01956         0        19       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5       0.75      0.599      0.553     0.0911\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "    19/499        0G   0.08316   0.02577         0        27       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5        0.6        0.6      0.534      0.158\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "    20/499        0G   0.07802   0.01833         0        15       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1        0.4      0.466      0.174\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "    21/499        0G   0.08158   0.01938         0        17       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5      0.998        0.4       0.55      0.214\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "    22/499        0G   0.08178   0.02158         0        22       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5      0.998        0.4      0.512      0.141\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "    23/499        0G   0.07612   0.01943         0        19       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1        0.4      0.552      0.146\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "    24/499        0G   0.07709   0.02476         0        24       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1        0.4      0.533       0.14\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "    25/499        0G   0.07317     0.025         0        23       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5      0.665      0.398      0.396      0.097\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "    26/499        0G   0.07153   0.02338         0        29       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5      0.999        0.4      0.489       0.14\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "    27/499        0G   0.07483   0.01939         0        20       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5      0.999        0.4      0.489       0.14\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "    28/499        0G   0.06962    0.0197         0        23       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5      0.998        0.4      0.548      0.165\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "    29/499        0G   0.07043   0.01739         0        16       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5      0.997        0.4      0.595      0.192\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "    30/499        0G   0.07695    0.0214         0        25       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5      0.997        0.4      0.595      0.192\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "    31/499        0G   0.07124   0.02136         0        24       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1      0.398      0.524      0.178\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "    32/499        0G   0.06945     0.023         0        23       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5      0.623          1      0.787      0.345\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "    33/499        0G   0.06949    0.0177         0        19       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5      0.623          1      0.787      0.345\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "    34/499        0G   0.06583   0.01911         0        19       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5      0.664      0.792       0.75      0.201\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "    35/499        0G   0.06334   0.02002         0        17       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5      0.995        0.8      0.895      0.159\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "    36/499        0G   0.06595   0.01633         0        15       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5      0.995        0.8      0.895      0.159\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "    37/499        0G   0.06509   0.01911         0        24       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1        0.4      0.583      0.134\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "    38/499        0G   0.06233   0.02002         0        20       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5      0.833          1      0.928      0.243\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "    39/499        0G   0.06423   0.01716         0        25       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5      0.833          1      0.928      0.243\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "    40/499        0G   0.06914    0.0176         0        21       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5      0.333          1      0.364     0.0608\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "    41/499        0G   0.06813    0.0194         0        23       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5      0.624          1       0.77      0.155\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "    42/499        0G   0.06958   0.01906         0        26       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5      0.624          1       0.77      0.155\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "    43/499        0G   0.06975   0.01628         0        19       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5      0.231      0.599      0.367     0.0495\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "    44/499        0G   0.07257   0.01744         0        24       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5      0.231      0.599      0.367     0.0495\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "    45/499        0G   0.07454    0.0181         0        24       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5      0.571        0.8      0.521      0.205\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "    46/499        0G   0.06274   0.01701         0        20       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5      0.571        0.8      0.521      0.205\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "    47/499        0G    0.0662   0.01512         0        19       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5      0.999        0.4      0.616      0.272\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "    48/499        0G   0.06503   0.01878         0        22       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5      0.999        0.4      0.616      0.272\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "    49/499        0G   0.06599   0.02005         0        22       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5       0.25        0.8      0.369     0.0576\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "    50/499        0G   0.05987     0.021         0        18       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5       0.25        0.8      0.369     0.0576\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "    51/499        0G   0.05991   0.01982         0        19       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5      0.496        0.8      0.668      0.287\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "    52/499        0G    0.0584   0.02187         0        18       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5      0.496        0.8      0.668      0.287\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "    53/499        0G   0.05474   0.01712         0        16       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5      0.996        0.4      0.562      0.238\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "    54/499        0G   0.04773   0.02114         0        21       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5      0.996        0.4      0.562      0.238\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "    55/499        0G    0.0482   0.02035         0        20       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5        0.4      0.799       0.52       0.17\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "    56/499        0G   0.05546   0.02275         0        23       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5        0.4      0.799       0.52       0.17\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "    57/499        0G   0.05392   0.02093         0        23       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5        0.8        0.8      0.814      0.438\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "    58/499        0G   0.05429   0.02447         0        27       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5        0.8        0.8      0.814      0.438\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "    59/499        0G   0.05409   0.01977         0        18       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5      0.625      0.998      0.853      0.141\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "    60/499        0G   0.05141   0.01687         0        13       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5      0.625      0.998      0.853      0.141\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "    61/499        0G   0.05145   0.01919         0        19       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5      0.571        0.8      0.714      0.281\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "    62/499        0G   0.05482   0.01772         0        22       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5      0.571        0.8      0.714      0.281\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "    63/499        0G   0.05353   0.01764         0        22       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5      0.454      0.997      0.565      0.267\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "    64/499        0G   0.05311   0.01764         0        18       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5      0.454      0.997      0.565      0.267\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "    65/499        0G   0.05628   0.01791         0        20       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5      0.747        0.6      0.628      0.316\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "    66/499        0G   0.05204   0.02062         0        22       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5      0.747        0.6      0.628      0.316\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "    67/499        0G   0.04648   0.02007         0        22       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5      0.998        0.6      0.817      0.371\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "    68/499        0G   0.04485   0.01878         0        23       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5      0.998        0.6      0.817      0.371\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "    69/499        0G   0.04314   0.02123         0        19       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995        0.3\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "    70/499        0G   0.04805   0.01786         0        21       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995        0.3\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "    71/499        0G   0.04857   0.01872         0        18       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5      0.996        0.8       0.92      0.377\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "    72/499        0G   0.04699   0.01704         0        18       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5      0.996        0.8       0.92      0.377\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "    73/499        0G   0.04858   0.01865         0        28       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5      0.833          1      0.962       0.22\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "    74/499        0G   0.04872   0.01838         0        25       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5      0.833          1      0.962       0.22\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "    75/499        0G   0.04712   0.01825         0        25       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5      0.714          1      0.898      0.512\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "    76/499        0G   0.04339   0.01756         0        20       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5      0.714          1      0.898      0.512\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "    77/499        0G    0.0441   0.01685         0        23       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.248\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "    78/499        0G   0.04761   0.01584         0        19       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.248\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "    79/499        0G   0.04542    0.0195         0        24       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5      0.999        0.8      0.938      0.387\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "    80/499        0G    0.0423   0.01547         0        19       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5      0.999        0.8      0.938      0.387\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "    81/499        0G   0.04275   0.01527         0        19       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5      0.833          1      0.928      0.301\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "    82/499        0G   0.04842   0.01352         0        17       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5      0.833          1      0.928      0.301\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "    83/499        0G   0.04634   0.01641         0        25       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5      0.623          1      0.853       0.33\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "    84/499        0G   0.04367    0.0135         0        14       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5      0.623          1      0.853       0.33\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "    85/499        0G   0.04259   0.01579         0        21       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1      0.999      0.995      0.603\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "    86/499        0G   0.03709   0.01571         0        18       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1      0.999      0.995      0.603\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "    87/499        0G    0.0394   0.01536         0        20       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5        0.8      0.799       0.88      0.352\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "    88/499        0G   0.03762   0.01645         0        22       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5        0.8      0.799       0.88      0.352\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "    89/499        0G   0.04135   0.01601         0        25       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5      0.799        0.8       0.88      0.609\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "    90/499        0G   0.04219   0.01662         0        23       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5      0.799        0.8       0.88      0.609\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "    91/499        0G   0.04379   0.01403         0        18       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5      0.833          1      0.962      0.194\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "    92/499        0G   0.04254   0.01327         0        21       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5      0.833          1      0.962      0.194\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "    93/499        0G   0.04053   0.01537         0        21       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5      0.714          1      0.858      0.468\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "    94/499        0G    0.0384    0.0143         0        20       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5      0.714          1      0.858      0.468\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "    95/499        0G   0.03734    0.0193         0        31       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.504\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "    96/499        0G   0.03791   0.01674         0        25       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.504\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "    97/499        0G   0.03842   0.01455         0        16       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5      0.833      0.999      0.928       0.51\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "    98/499        0G   0.03741   0.01612         0        26       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5      0.833      0.999      0.928       0.51\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "    99/499        0G   0.03676     0.015         0        21       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.376\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   100/499        0G   0.03934   0.01366         0        20       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.376\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   101/499        0G   0.03672   0.01492         0        17       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5      0.999        0.8      0.938      0.533\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   102/499        0G   0.03942   0.01178         0        18       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5      0.999        0.8      0.938      0.533\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   103/499        0G   0.03953   0.01488         0        24       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.321\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   104/499        0G   0.03623   0.01565         0        28       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.321\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   105/499        0G   0.03639   0.01356         0        19       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.651\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   106/499        0G     0.041   0.01141         0        15       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.651\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   107/499        0G   0.03981    0.0113         0        16       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5      0.833          1      0.962      0.446\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   108/499        0G   0.03737   0.01564         0        28       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5      0.833          1      0.962      0.446\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   109/499        0G   0.03992   0.01492         0        26       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.736\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   110/499        0G   0.02975   0.01353         0        21       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.736\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   111/499        0G   0.03283   0.01164         0        14       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5      0.833          1      0.962      0.573\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   112/499        0G   0.03522   0.01184         0        18       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5      0.833          1      0.962      0.573\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   113/499        0G   0.03526   0.01459         0        24       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995       0.57\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   114/499        0G   0.03422   0.01267         0        22       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995       0.57\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   115/499        0G   0.03069    0.0134         0        17       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5      0.833          1      0.962      0.556\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   116/499        0G   0.03681     0.012         0        21       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5      0.833          1      0.962      0.556\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   117/499        0G   0.03749   0.01051         0        16       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5      0.832          1      0.962      0.502\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   118/499        0G    0.0308   0.01193         0        23       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5      0.832          1      0.962      0.502\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   119/499        0G   0.03246   0.01439         0        26       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5      0.833          1      0.962      0.629\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   120/499        0G   0.03307   0.01227         0        24       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5      0.833          1      0.962      0.629\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   121/499        0G   0.03181   0.01122         0        24       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.706\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   122/499        0G   0.03757   0.01181         0        20       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.706\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   123/499        0G   0.03169   0.01379         0        24       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.617\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   124/499        0G   0.03594   0.01233         0        25       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.617\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   125/499        0G   0.03378    0.0122         0        22       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.741\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   126/499        0G    0.0318   0.01129         0        20       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.741\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   127/499        0G   0.02922   0.01081         0        19       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.404\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   128/499        0G   0.03548   0.01101         0        17       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.404\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   129/499        0G   0.04121   0.01051         0        17       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.692\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   130/499        0G   0.02841   0.01089         0        22       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.692\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   131/499        0G   0.03009   0.01107         0        18       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5      0.833          1      0.962       0.51\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   132/499        0G   0.03291   0.01143         0        21       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5      0.833          1      0.962       0.51\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   133/499        0G   0.03489   0.01208         0        25       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.688\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   134/499        0G   0.03423  0.009458         0        18       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.688\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   135/499        0G   0.03247  0.009661         0        21       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5      0.833          1      0.928       0.53\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   136/499        0G   0.03394   0.01214         0        27       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5      0.833          1      0.928       0.53\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   137/499        0G   0.03409  0.009943         0        20       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.544\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   138/499        0G   0.03752   0.01015         0        22       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.544\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   139/499        0G   0.03503   0.01261         0        24       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.731\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   140/499        0G   0.02708    0.0104         0        20       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.731\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   141/499        0G   0.02789   0.01141         0        23       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995       0.87\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   142/499        0G   0.02471   0.01411         0        29       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995       0.87\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   143/499        0G   0.02444   0.01122         0        23       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.812\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   144/499        0G   0.02982    0.0116         0        27       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.812\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   145/499        0G   0.02517   0.01075         0        25       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5      0.833          1      0.962      0.586\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   146/499        0G   0.03233  0.009368         0        22       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5      0.833          1      0.962      0.586\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   147/499        0G   0.03324  0.009492         0        15       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.693\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   148/499        0G   0.02737  0.009667         0        16       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.693\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   149/499        0G   0.02562   0.00962         0        21       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.829\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   150/499        0G    0.0269   0.01094         0        22       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.829\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   151/499        0G   0.02668  0.009566         0        22       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995       0.71\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   152/499        0G   0.02903  0.009854         0        22       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995       0.71\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   153/499        0G   0.02918  0.008916         0        19       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.659\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   154/499        0G   0.02791  0.007857         0        12       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.659\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   155/499        0G   0.02544   0.01008         0        19       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5      0.833          1      0.962      0.749\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   156/499        0G   0.02347  0.008789         0        19       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5      0.833          1      0.962      0.749\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   157/499        0G   0.02658   0.01023         0        21       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.606\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   158/499        0G   0.03121   0.01036         0        16       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.606\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   159/499        0G   0.03056   0.00827         0        15       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.788\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   160/499        0G   0.02673  0.008576         0        19       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.788\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   161/499        0G   0.02534  0.008589         0        17       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.623\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   162/499        0G   0.02771  0.008112         0        17       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.623\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   163/499        0G   0.02718   0.00862         0        20       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.739\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   164/499        0G   0.02628  0.009494         0        22       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.739\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   165/499        0G   0.02564   0.01224         0        26       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995       0.84\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   166/499        0G    0.0254  0.009523         0        19       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995       0.84\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   167/499        0G   0.02551   0.01045         0        25       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.768\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   168/499        0G   0.02519  0.007605         0        18       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.768\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   169/499        0G   0.02496  0.007988         0        20       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.746\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   170/499        0G   0.02737   0.01033         0        21       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.746\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   171/499        0G   0.03006   0.01057         0        22       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.763\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   172/499        0G   0.03162  0.008112         0        19       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.763\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   173/499        0G   0.03061  0.008541         0        17       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.597\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   174/499        0G   0.03047  0.007486         0        18       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.597\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   175/499        0G   0.02689  0.008848         0        19       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.796\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   176/499        0G   0.02614  0.008022         0        13       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.796\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   177/499        0G   0.02746   0.00824         0        20       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.808\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   178/499        0G   0.02805  0.009221         0        19       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.808\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   179/499        0G   0.02833  0.006979         0        13       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.716\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   180/499        0G   0.02549  0.007488         0        15       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.716\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   181/499        0G   0.02455  0.009328         0        25       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.706\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   182/499        0G    0.0262  0.007946         0        22       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.706\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   183/499        0G    0.0258  0.009123         0        20       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995       0.87\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   184/499        0G   0.02239    0.0102         0        26       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995       0.87\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   185/499        0G   0.02163   0.01077         0        27       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.851\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   186/499        0G   0.02186  0.009331         0        24       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.851\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   187/499        0G   0.02317  0.009284         0        25       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995       0.72\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   188/499        0G   0.02886   0.01033         0        27       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995       0.72\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   189/499        0G   0.03014  0.009842         0        23       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.728\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   190/499        0G   0.02558  0.008751         0        22       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.728\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   191/499        0G   0.02446  0.009406         0        16       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.864\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   192/499        0G   0.02393  0.009744         0        24       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.864\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   193/499        0G   0.02398   0.00994         0        27       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.781\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   194/499        0G   0.02821  0.007775         0        18       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.781\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   195/499        0G   0.02522  0.008974         0        22       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.666\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   196/499        0G   0.02893  0.007723         0        18       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.666\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   197/499        0G   0.02735  0.009189         0        21       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.652\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   198/499        0G   0.03488   0.01001         0        25       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.652\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   199/499        0G   0.02985  0.007935         0        17       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995       0.74\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   200/499        0G   0.02168  0.008018         0        19       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995       0.74\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   201/499        0G   0.02249    0.0114         0        27       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.772\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   202/499        0G   0.02143   0.00788         0        16       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.772\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   203/499        0G   0.02132  0.008724         0        19       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.875\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   204/499        0G   0.02423  0.008499         0        21       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.875\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   205/499        0G   0.02252  0.008968         0        24       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.824\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   206/499        0G   0.02126  0.007759         0        19       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.824\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   207/499        0G   0.02139  0.007834         0        15       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.671\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   208/499        0G   0.02316  0.008972         0        19       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.671\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   209/499        0G   0.02307  0.007878         0        18       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.832\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   210/499        0G   0.02467  0.008981         0        21       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.832\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   211/499        0G   0.02455  0.008629         0        20       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.583\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   212/499        0G   0.02524  0.007112         0        17       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.583\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   213/499        0G   0.02634    0.0093         0        21       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.816\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   214/499        0G   0.02579  0.008596         0        20       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.816\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   215/499        0G   0.02512   0.00906         0        22       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995       0.65\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   216/499        0G   0.02599  0.009191         0        21       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995       0.65\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   217/499        0G   0.02512  0.009309         0        21       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.756\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   218/499        0G   0.02458  0.007335         0        17       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.756\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   219/499        0G    0.0231  0.007194         0        15       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.671\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   220/499        0G   0.02595  0.009271         0        23       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.671\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   221/499        0G   0.02584  0.008781         0        17       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.804\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   222/499        0G   0.02111   0.01188         0        30       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.804\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   223/499        0G   0.02106  0.009047         0        26       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.826\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   224/499        0G   0.02125  0.008499         0        19       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.826\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   225/499        0G   0.01992  0.007373         0        16       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.838\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   226/499        0G   0.01898  0.009576         0        22       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.838\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   227/499        0G   0.02029  0.008555         0        20       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.841\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   228/499        0G   0.01909  0.007886         0        20       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.841\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   229/499        0G   0.01909  0.007504         0        17       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.684\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   230/499        0G   0.02052  0.008479         0        22       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.684\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   231/499        0G    0.0224  0.007032         0        18       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.841\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   232/499        0G   0.02268  0.007704         0        23       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.841\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   233/499        0G   0.02123  0.007441         0        21       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.875\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   234/499        0G   0.02013  0.008038         0        28       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.875\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   235/499        0G   0.02081  0.008844         0        26       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995       0.86\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   236/499        0G   0.02198  0.007258         0        17       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995       0.86\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   237/499        0G   0.02298  0.006559         0        19       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.684\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   238/499        0G   0.03068  0.007726         0        21       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.684\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   239/499        0G   0.02998   0.00835         0        26       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.865\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   240/499        0G   0.02216  0.009652         0        23       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.865\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   241/499        0G   0.02192  0.007193         0        18       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.806\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   242/499        0G   0.02042  0.008411         0        22       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.806\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   243/499        0G   0.02279  0.008494         0        19       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.806\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   244/499        0G   0.02497  0.008068         0        21       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.806\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   245/499        0G   0.02091  0.007631         0        21       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.864\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   246/499        0G   0.01872  0.008614         0        20       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.864\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   247/499        0G   0.02042  0.007103         0        20       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.829\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   248/499        0G   0.01969  0.008909         0        20       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.829\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   249/499        0G   0.02163  0.008229         0        22       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.577\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   250/499        0G     0.025  0.008319         0        22       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.577\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   251/499        0G   0.02421  0.007092         0        20       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.794\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   252/499        0G    0.0223  0.008815         0        20       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.794\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   253/499        0G   0.02039   0.00725         0        18       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.784\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   254/499        0G   0.01991   0.00781         0        23       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.784\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   255/499        0G   0.01977   0.00775         0        21       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.881\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   256/499        0G   0.01988  0.009294         0        20       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.881\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   257/499        0G   0.01735  0.006895         0        20       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.915\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   258/499        0G   0.01644  0.007212         0        26       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.915\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   259/499        0G   0.01673  0.007043         0        18       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.915\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   260/499        0G   0.01825  0.007375         0        20       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.915\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   261/499        0G   0.01799  0.007621         0        23       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.872\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   262/499        0G    0.0187  0.006626         0        19       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.872\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   263/499        0G   0.02178  0.008399         0        20       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.812\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   264/499        0G   0.01805  0.008649         0        26       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.812\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   265/499        0G   0.01884  0.009624         0        29       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.801\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   266/499        0G    0.0215  0.006531         0        22       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.801\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   267/499        0G   0.02153  0.007848         0        17       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.832\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   268/499        0G   0.02088  0.007396         0        21       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.832\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   269/499        0G   0.02142  0.008146         0        22       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.851\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   270/499        0G   0.02042  0.007857         0        17       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.851\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   271/499        0G   0.01981  0.006765         0        19       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.812\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   272/499        0G   0.01957  0.006876         0        16       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.812\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   273/499        0G   0.01719  0.007723         0        24       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995       0.87\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   274/499        0G   0.01866  0.007053         0        22       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995       0.87\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   275/499        0G   0.01894  0.008699         0        21       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.808\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   276/499        0G   0.02376  0.007317         0        21       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.808\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   277/499        0G   0.02075  0.006681         0        13       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995       0.87\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   278/499        0G   0.01762  0.008204         0        23       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995       0.87\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   279/499        0G   0.01772  0.006898         0        15       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.895\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   280/499        0G   0.01621  0.006622         0        18       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.895\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   281/499        0G   0.01812  0.007153         0        20       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.895\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   282/499        0G   0.01956  0.008637         0        27       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.895\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   283/499        0G   0.01858  0.007032         0        19       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.902\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   284/499        0G   0.01616  0.008099         0        29       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.902\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   285/499        0G   0.01727  0.008012         0        27       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.902\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   286/499        0G   0.01803  0.006808         0        17       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.902\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   287/499        0G    0.0167  0.006665         0        18       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.832\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   288/499        0G   0.02108  0.007783         0        26       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.832\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   289/499        0G   0.01918  0.007527         0        20       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.864\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   290/499        0G   0.01784   0.00623         0        16       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.864\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   291/499        0G   0.01731  0.007596         0        24       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.929\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   292/499        0G     0.019  0.006275         0        17       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.929\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   293/499        0G   0.01992  0.007332         0        20       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.907\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   294/499        0G   0.01936  0.006793         0        17       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.907\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   295/499        0G   0.01768  0.007241         0        19       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.887\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   296/499        0G    0.0181  0.007075         0        22       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.887\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   297/499        0G   0.01719  0.008408         0        30       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.914\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   298/499        0G   0.01641  0.007112         0        21       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.914\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   299/499        0G   0.01664  0.007913         0        25       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995       0.86\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   300/499        0G   0.01762  0.007142         0        22       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995       0.86\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   301/499        0G   0.01857  0.007222         0        23       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.925\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   302/499        0G   0.01674  0.007443         0        23       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.925\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   303/499        0G   0.01653  0.008454         0        23       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.841\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   304/499        0G   0.01892  0.007415         0        24       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.841\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   305/499        0G   0.01697  0.006324         0        16       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.915\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   306/499        0G   0.01663  0.007363         0        25       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.915\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   307/499        0G   0.01628  0.007402         0        20       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.955\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   308/499        0G   0.01607  0.005702         0        21       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.955\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   309/499        0G   0.02081  0.007121         0        17       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.895\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   310/499        0G   0.01661   0.00683         0        17       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.895\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   311/499        0G    0.0166   0.00745         0        24       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.895\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   312/499        0G   0.02103  0.006715         0        20       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.895\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   313/499        0G   0.02046  0.007395         0        14       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.806\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   314/499        0G   0.01792  0.006506         0        18       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.806\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   315/499        0G   0.01991   0.00705         0        21       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.959\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   316/499        0G   0.01623  0.006769         0        23       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.959\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   317/499        0G    0.0153  0.006664         0        25       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995        0.9\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   318/499        0G   0.01619  0.008006         0        26       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995        0.9\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   319/499        0G   0.01564  0.006514         0        17       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995       0.94\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   320/499        0G   0.01658  0.007105         0        21       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995       0.94\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   321/499        0G   0.01504  0.006417         0        26       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.816\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   322/499        0G   0.01669  0.006298         0        19       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.816\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   323/499        0G   0.01803  0.006958         0        20       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.921\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   324/499        0G    0.0177   0.00727         0        22       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.921\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   325/499        0G   0.01821  0.006622         0        20       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.816\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   326/499        0G   0.01654  0.006639         0        20       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.816\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   327/499        0G   0.01839  0.006635         0        20       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.955\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   328/499        0G   0.02002  0.008412         0        24       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.955\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   329/499        0G   0.02006  0.007632         0        24       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.845\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   330/499        0G   0.01623  0.006466         0        18       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.845\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   331/499        0G   0.01668  0.006256         0        21       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995       0.94\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   332/499        0G   0.01472  0.007465         0        21       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995       0.94\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   333/499        0G    0.0148  0.007434         0        26       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.845\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   334/499        0G   0.01727  0.007392         0        27       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.845\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   335/499        0G   0.01438  0.006623         0        19       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.905\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   336/499        0G   0.01459  0.006501         0        20       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.905\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   337/499        0G   0.01506  0.007335         0        23       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.915\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   338/499        0G   0.01512  0.005904         0        19       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.915\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   339/499        0G   0.01497  0.007064         0        21       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.922\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   340/499        0G    0.0128   0.00714         0        22       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.922\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   341/499        0G   0.01493  0.007789         0        25       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.895\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   342/499        0G   0.01486  0.006322         0        13       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.895\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   343/499        0G    0.0153  0.006457         0        22       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.814\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   344/499        0G   0.01846  0.007265         0        22       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.814\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   345/499        0G   0.01677  0.007627         0        28       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.945\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   346/499        0G   0.01485  0.006334         0        18       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.945\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   347/499        0G   0.01595  0.005444         0        20       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995       0.87\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   348/499        0G   0.01531   0.00551         0        15       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995       0.87\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   349/499        0G   0.01478  0.006011         0        20       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.912\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   350/499        0G   0.01576  0.006155         0        19       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.912\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   351/499        0G   0.01685  0.007642         0        25       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.922\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   352/499        0G   0.01694  0.006353         0        17       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.922\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   353/499        0G   0.01436  0.007335         0        20       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.916\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   354/499        0G   0.01691  0.006519         0        17       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.916\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   355/499        0G   0.01458   0.00711         0        27       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.895\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   356/499        0G    0.0147   0.00672         0        22       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.895\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   357/499        0G   0.01455  0.006371         0        25       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.929\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   358/499        0G    0.0133  0.005782         0        20       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.929\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   359/499        0G   0.01311   0.00614         0        21       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.947\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   360/499        0G   0.01467  0.005812         0        15       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.947\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   361/499        0G   0.01474  0.006159         0        19       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995        0.9\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   362/499        0G   0.01515  0.005575         0        19       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995        0.9\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   363/499        0G   0.01438  0.006506         0        20       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.912\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   364/499        0G   0.01601  0.008903         0        28       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.912\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   365/499        0G   0.01271  0.006276         0        19       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.963\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   366/499        0G   0.01234  0.006813         0        22       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.963\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   367/499        0G   0.01269  0.006252         0        21       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.995\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   368/499        0G   0.01515  0.006079         0        22       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.995\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   369/499        0G   0.01461  0.006068         0        18       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.912\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   370/499        0G   0.01308  0.006637         0        23       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.912\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   371/499        0G   0.01506  0.006403         0        24       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.959\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   372/499        0G   0.01365  0.008232         0        27       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.959\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   373/499        0G   0.01294  0.006362         0        25       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.905\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   374/499        0G   0.01209  0.005358         0        15       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.905\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   375/499        0G   0.01445  0.007071         0        27       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995       0.94\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   376/499        0G   0.01332  0.007047         0        27       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995       0.94\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   377/499        0G   0.01287  0.006615         0        26       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995        0.9\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   378/499        0G   0.01411  0.005386         0        23       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995        0.9\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   379/499        0G   0.01226  0.006413         0        28       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995       0.94\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   380/499        0G   0.01311  0.007449         0        25       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995       0.94\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   381/499        0G   0.01341   0.00705         0        17       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995       0.94\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   382/499        0G   0.01404  0.005215         0        16       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995       0.94\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   383/499        0G   0.01315  0.007302         0        26       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.975\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   384/499        0G   0.01536  0.004811         0        14       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.975\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   385/499        0G   0.01462  0.006536         0        20       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.916\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   386/499        0G   0.01491  0.006225         0        19       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.916\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   387/499        0G    0.0132  0.006041         0        20       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.975\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   388/499        0G   0.01295  0.007004         0        24       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.975\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   389/499        0G   0.01415  0.006602         0        19       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.975\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   390/499        0G    0.0126  0.006167         0        18       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.975\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   391/499        0G   0.01458  0.005765         0        16       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995       0.94\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   392/499        0G   0.01273  0.005402         0        21       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995       0.94\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   393/499        0G   0.01339  0.006947         0        25       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.975\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   394/499        0G   0.01376   0.00704         0        30       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.975\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   395/499        0G   0.01458  0.005626         0        19       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.895\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   396/499        0G    0.0145  0.005646         0        18       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.895\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   397/499        0G   0.01581  0.005814         0        21       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.967\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   398/499        0G   0.01396  0.005779         0        19       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.967\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   399/499        0G   0.01429  0.008056         0        23       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.912\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   400/499        0G   0.01369  0.006237         0        21       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.912\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   401/499        0G   0.01527  0.006916         0        21       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995       0.95\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   402/499        0G   0.01437   0.00574         0        21       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995       0.95\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   403/499        0G   0.01428  0.006853         0        19       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.912\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   404/499        0G   0.01341   0.00621         0        21       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.912\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   405/499        0G   0.01438  0.007718         0        30       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.995\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   406/499        0G   0.01168   0.00644         0        24       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.995\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   407/499        0G   0.01352  0.007293         0        23       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.934\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   408/499        0G   0.01224  0.006266         0        23       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.934\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   409/499        0G   0.01245  0.006214         0        22       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.995\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   410/499        0G   0.01317  0.006156         0        18       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.995\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   411/499        0G   0.01257  0.005414         0        19       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.934\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   412/499        0G    0.0124  0.006286         0        23       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.934\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   413/499        0G   0.01293  0.006467         0        22       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.995\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   414/499        0G    0.0129    0.0058         0        21       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.995\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   415/499        0G   0.01198  0.006185         0        26       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.995\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   416/499        0G    0.0111  0.006029         0        25       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.995\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   417/499        0G   0.01197  0.007392         0        28       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.995\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   418/499        0G   0.01158  0.006359         0        22       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.995\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   419/499        0G   0.01143  0.006562         0        19       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.934\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   420/499        0G   0.01147  0.006314         0        20       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.934\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   421/499        0G    0.0106  0.005495         0        18       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.995\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   422/499        0G   0.01276  0.005563         0        15       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.995\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   423/499        0G   0.01109  0.005994         0        22       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.995\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   424/499        0G   0.01273  0.006239         0        21       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.995\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   425/499        0G   0.01167  0.005591         0        21       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.995\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   426/499        0G   0.01261   0.00617         0        19       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.995\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   427/499        0G   0.01051  0.005673         0        19       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.971\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   428/499        0G   0.01144  0.006066         0        20       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.971\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   429/499        0G   0.01154  0.005571         0        16       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.935\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   430/499        0G   0.01286  0.005655         0        17       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.935\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   431/499        0G    0.0111  0.005509         0        21       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.995\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   432/499        0G   0.01037   0.00544         0        17       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.995\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   433/499        0G   0.01339  0.006977         0        34       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.959\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   434/499        0G   0.01284  0.006213         0        19       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.959\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   435/499        0G   0.01251   0.00549         0        22       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.913\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   436/499        0G   0.01275  0.006767         0        26       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.913\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   437/499        0G    0.0123  0.005832         0        23       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.934\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   438/499        0G   0.01027  0.005689         0        22       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.934\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   439/499        0G   0.01275  0.005796         0        21       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995       0.94\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   440/499        0G   0.01199  0.005943         0        19       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995       0.94\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   441/499        0G   0.01083  0.006534         0        24       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.959\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   442/499        0G   0.01025  0.006113         0        24       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.959\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   443/499        0G   0.01142  0.005942         0        20       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.959\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   444/499        0G   0.01225   0.00656         0        23       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.959\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   445/499        0G   0.01111  0.005111         0        20       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.941\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   446/499        0G   0.01147  0.005908         0        22       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.941\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   447/499        0G  0.009673  0.006052         0        23       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.959\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   448/499        0G    0.0101  0.005448         0        21       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.959\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   449/499        0G   0.01077  0.006483         0        23       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.995\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   450/499        0G    0.0106  0.006052         0        26       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.995\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   451/499        0G   0.01033    0.0048         0        16       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.995\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   452/499        0G   0.01192   0.00585         0        19       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.995\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   453/499        0G    0.0128  0.007932         0        26       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.995\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   454/499        0G   0.01077  0.006088         0        25       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.995\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   455/499        0G   0.01075  0.005073         0        17       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.967\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   456/499        0G   0.01076  0.005722         0        20       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.967\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   457/499        0G    0.0116  0.005923         0        20       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.995\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   458/499        0G    0.0116  0.006598         0        23       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.995\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   459/499        0G   0.01088   0.00569         0        25       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.995\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   460/499        0G   0.01212  0.006715         0        26       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.995\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   461/499        0G   0.01149  0.005109         0        15       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.995\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   462/499        0G   0.01223  0.005569         0        22       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.995\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   463/499        0G  0.009924  0.005775         0        19       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.995\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   464/499        0G   0.01064   0.00585         0        20       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.995\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   465/499        0G   0.01082  0.005533         0        19       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.995\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   466/499        0G   0.01068  0.005643         0        20       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.995\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   467/499        0G   0.01199  0.004632         0        17       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.912\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   468/499        0G   0.01164  0.005475         0        15       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.912\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   469/499        0G   0.01215  0.007002         0        20       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.901\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   470/499        0G    0.0106  0.005996         0        21       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.901\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   471/499        0G   0.01069  0.005537         0        20       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.931\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   472/499        0G   0.01191  0.006173         0        21       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.931\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   473/499        0G   0.00986  0.005473         0        14       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.931\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   474/499        0G   0.01013   0.00581         0        19       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.931\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   475/499        0G   0.01016  0.005418         0        20       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.912\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   476/499        0G   0.01067  0.005947         0        23       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.912\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   477/499        0G   0.01058  0.006045         0        21       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.995\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   478/499        0G   0.01061  0.005808         0        23       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.995\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   479/499        0G   0.01084   0.00584         0        22       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.995\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   480/499        0G   0.01188  0.007055         0        23       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.995\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   481/499        0G  0.009218  0.005826         0        25       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.995\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   482/499        0G   0.01182  0.006215         0        22       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.995\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   483/499        0G  0.009898  0.005724         0        19       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.963\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   484/499        0G   0.01175  0.005907         0        20       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.963\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   485/499        0G   0.01092  0.004766         0        16       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.995\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   486/499        0G  0.009454  0.005148         0        19       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.995\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   487/499        0G   0.01067  0.005889         0        20       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.995\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   488/499        0G   0.01137  0.007034         0        26       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.995\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   489/499        0G    0.0103  0.005539         0        19       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.995\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   490/499        0G  0.009518  0.005817         0        18       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.995\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   491/499        0G   0.01059  0.006766         0        25       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.995\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   492/499        0G  0.009575  0.005949         0        23       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.995\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   493/499        0G  0.009961  0.005231         0        19       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.995\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   494/499        0G  0.009766  0.006123         0        23       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.995\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   495/499        0G  0.009342  0.005583         0        23       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.995\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   496/499        0G   0.01061    0.0059         0        22       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.995\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   497/499        0G   0.01192  0.006841         0        23       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.995\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   498/499        0G    0.0122  0.005699         0        25       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.995\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "   499/499        0G   0.01135  0.005004         0        22       320: 100%|███\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.995\n",
      "\n",
      "500 epochs completed in 1.328 hours.\n",
      "Optimizer stripped from runs/train/exp6/weights/last.pt, 14.3MB\n",
      "Optimizer stripped from runs/train/exp6/weights/best.pt, 14.3MB\n",
      "\n",
      "Validating runs/train/exp6/weights/best.pt...\n",
      "Fusing layers... \n",
      "Model Summary: 213 layers, 7012822 parameters, 0 gradients, 15.8 GFLOPs\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@\n",
      "                 all          5          5          1          1      0.995      0.995\n",
      "Results saved to \u001b[1mruns/train/exp6\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "!cd yolov5 && python train.py --img 320 --batch 16 --epochs 500 --data dataset.yml --weights yolov5s.pt --workers 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6. Load Custom Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Downloading: \"https://github.com/ultralytics/yolov5/archive/master.zip\" to /Users/jonathanli/.cache/torch/hub/master.zip\n",
      "fatal: not a git repository (or any of the parent directories): .git\n",
      "YOLOv5 🚀 2022-3-22 torch 1.8.1 CPU\n",
      "\n",
      "Fusing layers... \n",
      "Model Summary: 213 layers, 7012822 parameters, 0 gradients, 15.8 GFLOPs\n",
      "Adding AutoShape... \n"
     ]
    }
   ],
   "source": [
    "model = torch.hub.load('ultralytics/yolov5', 'custom', path='yolov5/runs/train/exp6/weights/best.pt', force_reload=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# img = os.path.join('data', 'images', 'awake.c9a24d48-e1f6-11eb-bbef-5cf3709bbcc6.jpg')\n",
    "img = os.path.join('data','val','images','package.47720b24-6901-11ec-80a1-84ad8d5ec72a.jpg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "results = model(img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "image 1/1: 720x1280 1 package\n",
      "Speed: 28.5ms pre-process, 203.1ms inference, 1.3ms NMS per image at shape (1, 3, 384, 640)\n"
     ]
    }
   ],
   "source": [
    "results.print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline \n",
    "plt.imshow(np.squeeze(results.render()))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "cap = cv2.VideoCapture(0)\n",
    "while cap.isOpened():\n",
    "    ret, frame = cap.read()\n",
    "    \n",
    "    # Make detections \n",
    "    results = model(frame)\n",
    "    \n",
    "    cv2.imshow('YOLO', np.squeeze(results.render()))\n",
    "    \n",
    "    if cv2.waitKey(10) & 0xFF == ord('q'):\n",
    "        break\n",
    "cap.release()\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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