Lesson 11, YOLOv5.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8007eab2",
   "metadata": {
    "id": "8007eab2"
   },
   "outputs": [],
   "source": [
    " import torch\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import numpy as np\n",
    "import cv2\n",
    "import os\n",
    "\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f235827a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.11.2+cu113\n",
      "1.10.1+cpu\n"
     ]
    }
   ],
   "source": [
    "import torchvision\n",
    "\n",
    "print(torchvision.__version__)\n",
    "print(torch.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3e7451b9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[notice] A new release of pip available: 22.1.2 -> 22.2.1\n",
      "[notice] To update, run: python.exe -m pip install --upgrade pip\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: Ignoring invalid distribution -rpcio (d:\\ml-learning\\ml_env\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -rotobuf (d:\\ml-learning\\ml_env\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -otebook (d:\\ml-learning\\ml_env\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -orch (d:\\ml-learning\\ml_env\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -ip (d:\\ml-learning\\ml_env\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -illow (d:\\ml-learning\\ml_env\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -adelpy (d:\\ml-learning\\ml_env\\lib\\site-packages)\n",
      "ERROR: Invalid requirement: 'torchvision=0.11.2'\n",
      "Hint: = is not a valid operator. Did you mean == ?\n",
      "WARNING: Ignoring invalid distribution -rpcio (d:\\ml-learning\\ml_env\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -rotobuf (d:\\ml-learning\\ml_env\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -otebook (d:\\ml-learning\\ml_env\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -orch (d:\\ml-learning\\ml_env\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -ip (d:\\ml-learning\\ml_env\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -illow (d:\\ml-learning\\ml_env\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -adelpy (d:\\ml-learning\\ml_env\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -rpcio (d:\\ml-learning\\ml_env\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -rotobuf (d:\\ml-learning\\ml_env\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -otebook (d:\\ml-learning\\ml_env\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -orch (d:\\ml-learning\\ml_env\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -ip (d:\\ml-learning\\ml_env\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -illow (d:\\ml-learning\\ml_env\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -adelpy (d:\\ml-learning\\ml_env\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -rpcio (d:\\ml-learning\\ml_env\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -rotobuf (d:\\ml-learning\\ml_env\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -otebook (d:\\ml-learning\\ml_env\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -orch (d:\\ml-learning\\ml_env\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -ip (d:\\ml-learning\\ml_env\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -illow (d:\\ml-learning\\ml_env\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -adelpy (d:\\ml-learning\\ml_env\\lib\\site-packages)\n"
     ]
    }
   ],
   "source": [
    "!python -m pip install torchvision=0.11.2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c331dd8c",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "c331dd8c",
    "outputId": "5300ef51-0204-4c09-b3dc-a3f38b7a947d"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using cache found in C:\\Users\\User/.cache\\torch\\hub\\ultralytics_yolov5_master\n",
      "\u001b[31m\u001b[1mrequirements:\u001b[0m PyYAML>=5.3.1 not found and is required by YOLOv5, attempting auto-update...\n",
      "\u001b[31m\u001b[1mrequirements:\u001b[0m Command 'pip install \"PyYAML>=5.3.1\" ' returned non-zero exit status 1.\n",
      "YOLOv5  2022-8-3 Python-3.7.0 torch-1.10.1+cpu CPU\n",
      "\n",
      "Fusing layers... \n",
      "YOLOv5n summary: 213 layers, 1867405 parameters, 0 gradients, 4.5 GFLOPs\n",
      "Adding AutoShape... \n"
     ]
    }
   ],
   "source": [
    "model = torch.hub.load('ultralytics/yolov5', 'yolov5n')\n",
    "model.conf = 0.35\n",
    "model.amp = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5c7fa4f0",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "5c7fa4f0",
    "outputId": "76668508-ec0c-4eaa-e554-4bde342e9a24"
   },
   "outputs": [
    {
     "ename": "RuntimeError",
     "evalue": "Couldn't load custom C++ ops. This can happen if your PyTorch and torchvision versions are incompatible, or if you had errors while compiling torchvision from source. For further information on the compatible versions, check https://github.com/pytorch/vision#installation for the compatibility matrix. Please check your PyTorch version with torch.__version__ and your torchvision version with torchvision.__version__ and verify if they are compatible, and if not please reinstall torchvision so that it matches your PyTorch install.",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_1080/27975972.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mimg\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'https://assets.weforum.org/article/image/large__ju_jiQgUhNmA7API0Oh-cAxz4asL6RteSi8VrOUiYM.jpg'\u001b[0m  \u001b[1;31m# or file, Path, PIL, OpenCV, numpy, list\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mresults\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m \u001b[0mresults\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m  \u001b[1;31m# or .show(), .save(), .crop(), .pandas(), etc.\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32md:\\ml-learning\\ml_env\\lib\\site-packages\\torch\\nn\\modules\\module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[1;34m(self, *input, **kwargs)\u001b[0m\n\u001b[0;32m   1100\u001b[0m         if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks\n\u001b[0;32m   1101\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[1;32m-> 1102\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1103\u001b[0m         \u001b[1;31m# Do not call functions when jit is used\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1104\u001b[0m         \u001b[0mfull_backward_hooks\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32md:\\ml-learning\\ml_env\\lib\\site-packages\\torch\\autograd\\grad_mode.py\u001b[0m in \u001b[0;36mdecorate_context\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     26\u001b[0m         \u001b[1;32mdef\u001b[0m \u001b[0mdecorate_context\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     27\u001b[0m             \u001b[1;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__class__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 28\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     29\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mcast\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mF\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdecorate_context\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     30\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~/.cache\\torch\\hub\\ultralytics_yolov5_master\\models\\common.py\u001b[0m in \u001b[0;36mforward\u001b[1;34m(self, imgs, size, augment, profile)\u001b[0m\n\u001b[0;32m    633\u001b[0m                                     \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0magnostic\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    634\u001b[0m                                     \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmulti_label\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 635\u001b[1;33m                                     max_det=self.max_det)  # NMS\n\u001b[0m\u001b[0;32m    636\u001b[0m             \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mn\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    637\u001b[0m                 \u001b[0mscale_coords\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mshape1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m:\u001b[0m\u001b[1;36m4\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mshape0\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~/.cache\\torch\\hub\\ultralytics_yolov5_master\\utils\\general.py\u001b[0m in \u001b[0;36mnon_max_suppression\u001b[1;34m(prediction, conf_thres, iou_thres, classes, agnostic, multi_label, labels, max_det)\u001b[0m\n\u001b[0;32m    878\u001b[0m         \u001b[0mc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m5\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m6\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m*\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0magnostic\u001b[0m \u001b[1;32melse\u001b[0m \u001b[0mmax_wh\u001b[0m\u001b[1;33m)\u001b[0m  \u001b[1;31m# classes\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    879\u001b[0m         \u001b[0mboxes\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mscores\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m:\u001b[0m\u001b[1;36m4\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m4\u001b[0m\u001b[1;33m]\u001b[0m  \u001b[1;31m# boxes (offset by class), scores\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 880\u001b[1;33m         \u001b[0mi\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtorchvision\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mops\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnms\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mboxes\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mscores\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0miou_thres\u001b[0m\u001b[1;33m)\u001b[0m  \u001b[1;31m# NMS\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    881\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mi\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m>\u001b[0m \u001b[0mmax_det\u001b[0m\u001b[1;33m:\u001b[0m  \u001b[1;31m# limit detections\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    882\u001b[0m             \u001b[0mi\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mi\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[0mmax_det\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32md:\\ml-learning\\ml_env\\lib\\site-packages\\torchvision\\ops\\boxes.py\u001b[0m in \u001b[0;36mnms\u001b[1;34m(boxes, scores, iou_threshold)\u001b[0m\n\u001b[0;32m     32\u001b[0m         \u001b[0mby\u001b[0m \u001b[0mNMS\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msorted\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mdecreasing\u001b[0m \u001b[0morder\u001b[0m \u001b[0mof\u001b[0m \u001b[0mscores\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     33\u001b[0m     \"\"\"\n\u001b[1;32m---> 34\u001b[1;33m     \u001b[0m_assert_has_ops\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     35\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mtorch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mops\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtorchvision\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnms\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mboxes\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mscores\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0miou_threshold\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     36\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32md:\\ml-learning\\ml_env\\lib\\site-packages\\torchvision\\extension.py\u001b[0m in \u001b[0;36m_assert_has_ops\u001b[1;34m()\u001b[0m\n\u001b[0;32m     30\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0m_has_ops\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     31\u001b[0m         raise RuntimeError(\n\u001b[1;32m---> 32\u001b[1;33m             \u001b[1;34m\"Couldn't load custom C++ ops. This can happen if your PyTorch and \"\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     33\u001b[0m             \u001b[1;34m\"torchvision versions are incompatible, or if you had errors while compiling \"\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     34\u001b[0m             \u001b[1;34m\"torchvision from source. For further information on the compatible versions, check \"\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mRuntimeError\u001b[0m: Couldn't load custom C++ ops. This can happen if your PyTorch and torchvision versions are incompatible, or if you had errors while compiling torchvision from source. For further information on the compatible versions, check https://github.com/pytorch/vision#installation for the compatibility matrix. Please check your PyTorch version with torch.__version__ and your torchvision version with torchvision.__version__ and verify if they are compatible, and if not please reinstall torchvision so that it matches your PyTorch install."
     ]
    }
   ],
   "source": [
    "img = 'https://assets.weforum.org/article/image/large__ju_jiQgUhNmA7API0Oh-cAxz4asL6RteSi8VrOUiYM.jpg'  # or file, Path, PIL, OpenCV, numpy, list\n",
    "results = model(img)\n",
    "results.print()  # or .show(), .save(), .crop(), .pandas(), etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "486f32e3",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "486f32e3",
    "outputId": "528f4118-d598-4625-cc66-36d29fd198e4"
   },
   "outputs": [],
   "source": [
    "results.render()[0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "60c9d967",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 396
    },
    "id": "60c9d967",
    "outputId": "af260960-9b96-44d8-c9c6-e6278c61bff8"
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(16, 6))\n",
    "ax.imshow(results.render()[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d6650d2a",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 396
    },
    "id": "d6650d2a",
    "outputId": "357e63d7-2468-4715-aa7d-9bfe85705dfc"
   },
   "outputs": [],
   "source": [
    "import PIL\n",
    "import urllib\n",
    "%matplotlib inline\n",
    "\n",
    "plt.figure(figsize=(16, 6))\n",
    "plt.imshow(PIL.Image.open(urllib.request.urlopen(img)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "13c351cc",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 71
    },
    "id": "13c351cc",
    "outputId": "bdbfeb1d-a1b3-42d2-aa63-e0e341212149"
   },
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "#cap = cv2.VideoCapture(0)\n",
    "cap = cv2.VideoCapture('tesla.mp4')\n",
    "while cap.isOpened():\n",
    "    ret, frame = cap.read()\n",
    "    \n",
    "    # Make detections \n",
    "    results = model(frame)\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()\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc35a90e",
   "metadata": {
    "id": "fc35a90e"
   },
   "source": [
    "# Public Datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4d7377a5",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "4d7377a5",
    "outputId": "23072c28-a71e-47f8-c570-696960858574"
   },
   "outputs": [],
   "source": [
    "!git clone https://github.com/ultralytics/yolov5\n",
    "!python3 -m pip install -r yolov5/requirements.txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "428326a5",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "428326a5",
    "outputId": "79c4cfe9-d643-4d4a-f9bd-0ccde88da7b6",
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "!python3 -m pip install opencv-python-headless roboflow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f41b660b",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "f41b660b",
    "outputId": "313c54f8-17ab-4fc3-fb4b-86a08e5d63c3"
   },
   "outputs": [],
   "source": [
    "from roboflow import Roboflow\n",
    "rf = Roboflow(api_key=\"LTOMlcYHj1Jefjve8EY4\")\n",
    "project = rf.workspace(\"test-cnn\").project(\"trafficsignals-zgvyv\")\n",
    "dataset = project.version(4).download(\"yolov5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a3226d2d",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "a3226d2d",
    "outputId": "2581be7f-a745-4e69-9040-d2dbc725a60c"
   },
   "outputs": [],
   "source": [
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ae0e8b6d",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "id": "ae0e8b6d",
    "outputId": "bc86dc7e-9ac0-4eb9-a564-5c46b2c5e959"
   },
   "outputs": [],
   "source": [
    "dataset.location"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3371ccd6",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "3371ccd6",
    "outputId": "23c871c8-9d2e-40f7-f5e9-45c328407c14"
   },
   "outputs": [],
   "source": [
    "%cat {dataset.location}/data.yaml"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b21e0240",
   "metadata": {
    "id": "b21e0240"
   },
   "outputs": [],
   "source": [
    "import yaml\n",
    "config = {'path': dataset.location, # root dir\n",
    "         'train': 'train/images', # relative to path\n",
    "         'val': 'valid/images',\n",
    "         'nc': 1,\n",
    "         'names': ['signal']}\n",
    " \n",
    "with open(os.path.join(dataset.location, \"data.yaml\"), \"w\") as file:\n",
    "    yaml.dump(config, file, default_flow_style=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "092d4e17",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "092d4e17",
    "outputId": "a8256b3b-7e8a-462c-ba92-1a7c842d35f0"
   },
   "outputs": [],
   "source": [
    "%cat {dataset.location}/data.yaml"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "781f1b4d",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "781f1b4d",
    "outputId": "8e6a368a-1e45-4185-8859-4cdfd7888cb8"
   },
   "outputs": [],
   "source": [
    "%cd yolov5\n",
    "!python3 train.py --batch -1 --epochs 10 --data {dataset.location}/data.yaml --weights yolov5m.pt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b4dbe96",
   "metadata": {
    "id": "1b4dbe96"
   },
   "source": [
    "# Custom Data Collection and Preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "q_ToAvGHzFPg",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "q_ToAvGHzFPg",
    "outputId": "2c5982c3-0107-4cc3-e226-a6e5d6c2fcbf"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/content\n"
     ]
    }
   ],
   "source": [
    "%cd .."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4c77f1b2",
   "metadata": {
    "id": "4c77f1b2"
   },
   "outputs": [],
   "source": [
    "IMAGES_PATH = os.path.join('drive-data', 'images') #/data/images\n",
    "LABELS_PATH = os.path.join('drive-data', 'labels') #/data/labels\n",
    "\n",
    "labels = ['crossing', 'parking', 'wrong way', 'no vehicles', 'traffic light']\n",
    "\n",
    "classnames = {\n",
    "    'crossing' : 0, \n",
    "    'parking' : 1, \n",
    "    'wrong way' : 2, \n",
    "    'no vehicles' : 3, \n",
    "    'traffic light' : 4\n",
    "    \n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "177e0f27",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "177e0f27",
    "outputId": "99ab776c-4b1b-40c7-d7e1-9d89a8c7d95b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mkdir: cannot create directory ‘drive-data’: File exists\n",
      "mkdir: cannot create directory ‘drive-data/images’: File exists\n",
      "mkdir: cannot create directory ‘drive-data/labels’: File exists\n",
      "mkdir: cannot create directory ‘drive-data/images/train’: File exists\n",
      "mkdir: cannot create directory ‘drive-data/images/test’: File exists\n",
      "mkdir: cannot create directory ‘drive-data/images/valid’: File exists\n",
      "mkdir: cannot create directory ‘drive-data/labels/train’: File exists\n",
      "mkdir: cannot create directory ‘drive-data/labels/test’: File exists\n",
      "mkdir: cannot create directory ‘drive-data/labels/valid’: File exists\n"
     ]
    }
   ],
   "source": [
    "# Windows\n",
    "#!mkdir yolov5\\yolov5-data\n",
    "#!mkdir yolov5\\yolov5-data\\temp\n",
    "#!mkdir yolov5\\yolov5-data\\images\\train\n",
    "#!mkdir yolov5\\yolov5-data\\images\\test\n",
    "#!mkdir yolov5\\yolov5-data\\images\\valid\n",
    "\n",
    "#!mkdir yolov5\\yolov5-data\\labels\\train\n",
    "#!mkdir yolov5\\yolov5-data\\labels\\test\n",
    "#!mkdir yolov5\\yolov5-data\\labels\\valid\n",
    "\n",
    "\n",
    "# Linux and MacOS\n",
    "!mkdir drive-data\n",
    "!mkdir drive-data/images\n",
    "!mkdir drive-data/labels\n",
    "\n",
    "!mkdir drive-data/images/train\n",
    "!mkdir drive-data/images/test\n",
    "!mkdir drive-data/images/valid\n",
    "\n",
    "!mkdir drive-data/labels/train\n",
    "!mkdir drive-data/labels/test\n",
    "!mkdir drive-data/labels/valid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8d39697b",
   "metadata": {
    "id": "8d39697b"
   },
   "outputs": [],
   "source": [
    "def save_labels_as_classfile():\n",
    "    # Where LabelImg will store labels\n",
    "    with open(f'{LABELS_PATH}/classes.txt', 'w+') as f:\n",
    "        for label in labels:\n",
    "            f.write(f'{label}\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5b7d3798",
   "metadata": {
    "id": "5b7d3798"
   },
   "outputs": [],
   "source": [
    "save_labels_as_classfile()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1c5c743d",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 125
    },
    "id": "1c5c743d",
    "outputId": "6bbccc64-b80c-4a4a-b21b-49e78d2713bd"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "string"
      },
      "text/plain": [
       "'\\nimport time\\n\\ncap = cv2.VideoCapture(0)\\nfor label in labels:\\n    print(\\'Collecting images for {}\\'.format(label))\\n    time.sleep(5)\\n    \\n    for img_num in range(10):\\n        print(f\"Collecting images for label \\'{label}\\', image number {img_num}\")\\n        ret, frame = cap.read()        \\n        imgname = os.path.join(IMAGES_PATH, f\"{label}-{img_num}.jpg\")\\n        cv2.imwrite(imgname, frame)\\n        cv2.imshow(\\'Image Collection\\', frame)\\n        time.sleep(2)\\n        \\n        if cv2.waitKey(10) & 0xFF == ord(\\'q\\'):\\n            break\\ncap.release()\\ncv2.destroyAllWindows()\\ncv2.waitKey(1)\\n'"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To collect images from, say, the webcam\n",
    "\"\"\"\n",
    "import time\n",
    "\n",
    "cap = cv2.VideoCapture(0)\n",
    "for label in labels:\n",
    "    print('Collecting images for {}'.format(label))\n",
    "    time.sleep(5)\n",
    "    \n",
    "    for img_num in range(10):\n",
    "        print(f\"Collecting images for label '{label}', image number {img_num}\")\n",
    "        ret, frame = cap.read()        \n",
    "        imgname = os.path.join(IMAGES_PATH, f\"{label}-{img_num}.jpg\")\n",
    "        cv2.imwrite(imgname, frame)\n",
    "        cv2.imshow('Image Collection', frame)\n",
    "        time.sleep(2)\n",
    "        \n",
    "        if cv2.waitKey(10) & 0xFF == ord('q'):\n",
    "            break\n",
    "cap.release()\n",
    "cv2.destroyAllWindows()\n",
    "cv2.waitKey(1)\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "OT3nZKpTY4PS",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "OT3nZKpTY4PS",
    "outputId": "043ac285-7e27-4af3-b2e3-a1d57def6792"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing video madrid_drive.mp4... \n",
      "FPS: 29.97002997002997. \n",
      "Frame Count: 4498 \n",
      "Duration: 150s \n",
      "Sampling rate: 30\n",
      "Failed to process frames: [147, 148, 149]\n",
      "Processing done.\n"
     ]
    }
   ],
   "source": [
    "import cv2\n",
    "video_path = 'madrid_drive.mp4'\n",
    "\n",
    "# Path, take every 30th frame (1s), \n",
    "def video_to_frames(path, sampling_rate=30, verbose=True):\n",
    "    cap = cv2.VideoCapture(path)\n",
    "    # Get FPS, frame count, and calc duration\n",
    "    fps = cap.get(cv2.CAP_PROP_FPS)\n",
    "    frame_count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))\n",
    "    duration = int(frame_count/fps)\n",
    "\n",
    "    ret, frame = cap.read()\n",
    "    img_num = 0\n",
    "    failed = []\n",
    "    if verbose:\n",
    "        print(f'Processing video {video_path}... \\nFPS: {fps}. \\nFrame Count: {frame_count} \\nDuration: {duration}s \\nSampling rate: {sampling_rate}')\n",
    "    for img_num in range(frame_count+1):\n",
    "        ret, frame = cap.read()\n",
    "        if img_num%30==0:\n",
    "            if ret:\n",
    "                imgname = os.path.join(IMAGES_PATH, f\"{int(img_num/sampling_rate)}.jpg\")\n",
    "                cv2.imwrite(imgname, frame)    \n",
    "                if verbose:\n",
    "                    print(f'Processed frame {int(img_num/sampling_rate)}/{duration}', end='\\r')\n",
    "            else:\n",
    "                failed.append(int(img_num/sampling_rate))\n",
    "        img_num += 1\n",
    "    if verbose:\n",
    "        print(f'Failed to process frames: {failed}')\n",
    "    print('Processing done.')\n",
    "\n",
    "    return int(img_num/sampling_rate)-len(failed)\n",
    "        \n",
    "img_num = video_to_frames(video_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7a484861",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 71
    },
    "id": "7a484861",
    "outputId": "35a02583-833c-47fe-b08c-e5f7b220215a"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "string"
      },
      "text/plain": [
       "\"\\nRoboflow - Recommended by Ultralytics, web-based, but requires sign in and public workspaces for free (paid private)\\nLabelImg - Desktop-based, simple, intuitive, buy finicky and bug-prone, doesn't work with python >3.9\\nLabel Studio - Web-based\\n\""
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "Roboflow - Recommended by Ultralytics, web-based, but requires sign in and public workspaces for free (paid private)\n",
    "LabelImg - Desktop-based, simple, intuitive, buy finicky and bug-prone, doesn't work with python >3.9\n",
    "Label Studio - Web-based\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e96d2616",
   "metadata": {
    "id": "e96d2616"
   },
   "outputs": [],
   "source": [
    "# ! labelimg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "73269dba",
   "metadata": {
    "id": "73269dba"
   },
   "outputs": [],
   "source": [
    "image_text_mapping = {}\n",
    "\n",
    "for label in labels:\n",
    "    for n in range(img_num):\n",
    "        imgname = os.path.join(IMAGES_PATH, f\"{n}.jpg\")\n",
    "        textname = os.path.join(LABELS_PATH, f\"{n}.txt\")\n",
    "        image_text_mapping[imgname] = textname\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e97358ae",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 363
    },
    "id": "e97358ae",
    "outputId": "011bd27b-11c6-4993-c809-d1421d7af9ad"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "  <div id=\"df-2f33dd5a-0aae-4509-bdcc-20a2eb73e0ae\">\n",
       "    <div class=\"colab-df-container\">\n",
       "      <div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Img Path</th>\n",
       "      <th>Text Path</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>drive-data/images/0.jpg</td>\n",
       "      <td>drive-data/labels/0.txt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>drive-data/images/1.jpg</td>\n",
       "      <td>drive-data/labels/1.txt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>drive-data/images/2.jpg</td>\n",
       "      <td>drive-data/labels/2.txt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>drive-data/images/3.jpg</td>\n",
       "      <td>drive-data/labels/3.txt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>drive-data/images/4.jpg</td>\n",
       "      <td>drive-data/labels/4.txt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>drive-data/images/5.jpg</td>\n",
       "      <td>drive-data/labels/5.txt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>drive-data/images/6.jpg</td>\n",
       "      <td>drive-data/labels/6.txt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>drive-data/images/7.jpg</td>\n",
       "      <td>drive-data/labels/7.txt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>drive-data/images/8.jpg</td>\n",
       "      <td>drive-data/labels/8.txt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>drive-data/images/9.jpg</td>\n",
       "      <td>drive-data/labels/9.txt</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2f33dd5a-0aae-4509-bdcc-20a2eb73e0ae')\"\n",
       "              title=\"Convert this dataframe to an interactive table.\"\n",
       "              style=\"display:none;\">\n",
       "        \n",
       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
       "       width=\"24px\">\n",
       "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
       "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
       "  </svg>\n",
       "      </button>\n",
       "      \n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      flex-wrap:wrap;\n",
       "      gap: 12px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert {\n",
       "      background-color: #E8F0FE;\n",
       "      border: none;\n",
       "      border-radius: 50%;\n",
       "      cursor: pointer;\n",
       "      display: none;\n",
       "      fill: #1967D2;\n",
       "      height: 32px;\n",
       "      padding: 0 0 0 0;\n",
       "      width: 32px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert:hover {\n",
       "      background-color: #E2EBFA;\n",
       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "      fill: #174EA6;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
       "      fill: #D2E3FC;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "      fill: #FFFFFF;\n",
       "    }\n",
       "  </style>\n",
       "\n",
       "      <script>\n",
       "        const buttonEl =\n",
       "          document.querySelector('#df-2f33dd5a-0aae-4509-bdcc-20a2eb73e0ae button.colab-df-convert');\n",
       "        buttonEl.style.display =\n",
       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "        async function convertToInteractive(key) {\n",
       "          const element = document.querySelector('#df-2f33dd5a-0aae-4509-bdcc-20a2eb73e0ae');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
       "          await google.colab.output.renderOutput(dataTable, element);\n",
       "          const docLink = document.createElement('div');\n",
       "          docLink.innerHTML = docLinkHtml;\n",
       "          element.appendChild(docLink);\n",
       "        }\n",
       "      </script>\n",
       "    </div>\n",
       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "                  Img Path                Text Path\n",
       "0  drive-data/images/0.jpg  drive-data/labels/0.txt\n",
       "1  drive-data/images/1.jpg  drive-data/labels/1.txt\n",
       "2  drive-data/images/2.jpg  drive-data/labels/2.txt\n",
       "3  drive-data/images/3.jpg  drive-data/labels/3.txt\n",
       "4  drive-data/images/4.jpg  drive-data/labels/4.txt\n",
       "5  drive-data/images/5.jpg  drive-data/labels/5.txt\n",
       "6  drive-data/images/6.jpg  drive-data/labels/6.txt\n",
       "7  drive-data/images/7.jpg  drive-data/labels/7.txt\n",
       "8  drive-data/images/8.jpg  drive-data/labels/8.txt\n",
       "9  drive-data/images/9.jpg  drive-data/labels/9.txt"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(image_text_mapping.items(), columns=['Img Path', 'Text Path'])\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7698b6da",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "7698b6da",
    "outputId": "b9d26a6b-f3e2-43e0-d983-4cd6f9ae3776"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(146, 2)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "678c19d7",
   "metadata": {
    "id": "678c19d7"
   },
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_iris\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "train_df, test_df = train_test_split(df, train_size=0.8)\n",
    "train_df, valid_df = train_test_split(train_df, train_size=0.9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0f2efc6b",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "0f2efc6b",
    "outputId": "af0db9a4-231d-4f8d-b650-617329d1554a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(104, 2)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "279e535b",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "279e535b",
    "outputId": "b8726b66-5338-409a-f410-4d53a329c2af"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(30, 2)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fbbeeb6a",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "fbbeeb6a",
    "outputId": "1654113a-09fc-42b0-d8e4-12101ad0c034"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12, 2)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "valid_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b7f4837c",
   "metadata": {
    "id": "b7f4837c"
   },
   "outputs": [],
   "source": [
    "import shutil\n",
    "\n",
    "def df_to_dir(df, set_name):\n",
    "    for index, row in df.iterrows():\n",
    "      if os.path.exists(row['Img Path']):\n",
    "           img_name = os.path.split(row['Img Path'])[1]\n",
    "           shutil.copyfile(row['Img Path'], os.path.join(IMAGES_PATH, set_name, img_name))\n",
    "           os.remove(row['Img Path'])\n",
    "      if os.path.exists(row['Text Path']):\n",
    "          txt_name = os.path.split(row['Text Path'])[1]\n",
    "          shutil.copyfile(row['Text Path'], os.path.join(LABELS_PATH, set_name, txt_name))\n",
    "          os.remove(row['Text Path'])\n",
    "          "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8250b6bd",
   "metadata": {
    "id": "8250b6bd"
   },
   "outputs": [],
   "source": [
    "df_to_dir(train_df, 'train')\n",
    "df_to_dir(test_df, 'test')\n",
    "df_to_dir(valid_df, 'valid')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61db298f",
   "metadata": {
    "id": "61db298f"
   },
   "source": [
    "# Train Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "mv-nE8Rjgo97",
   "metadata": {
    "id": "mv-nE8Rjgo97"
   },
   "outputs": [],
   "source": [
    "path = os.getcwd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "GiPkAz8U0MUv",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "GiPkAz8U0MUv",
    "outputId": "c8f6cb4f-0e6b-4bea-ecec-4b8c0e134a83"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/content/yolov5\n"
     ]
    }
   ],
   "source": [
    "%cd yolov5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "BfdONYaK4SAl",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "id": "BfdONYaK4SAl",
    "outputId": "ee4e7b37-bfa1-44e2-d584-faa3947196af"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "string"
      },
      "text/plain": [
       "'/content'"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "14be0aaa",
   "metadata": {
    "id": "14be0aaa"
   },
   "outputs": [],
   "source": [
    "import yaml\n",
    "config = {'path': os.path.join(path, 'drive-data'), # root dir\n",
    "         'train': 'images/train', # relative to path\n",
    "         'test': 'images/test',\n",
    "         'val': 'images/valid',\n",
    "         'nc': len(labels),\n",
    "         'names': labels}\n",
    " \n",
    "with open(\"data.yaml\", \"w\") as file:\n",
    "    yaml.dump(config, file, default_flow_style=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "04ab29e3",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "04ab29e3",
    "outputId": "e7aae747-f38b-4ccc-a078-2bf3dc1efcf7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mtrain: \u001b[0mweights=runs/train/exp2/weights/best.pt, cfg=, data=data.yaml, hyp=data/hyps/hyp.scratch-low.yaml, epochs=100, batch_size=-1, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, bucket=, cache=None, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=runs/train, name=exp, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=-1, artifact_alias=latest\n",
      "\u001b[34m\u001b[1mgithub: \u001b[0mskipping check (Docker image), for updates see https://github.com/ultralytics/yolov5\n",
      "YOLOv5 🚀 v6.1-344-g0e165c5 Python-3.7.13 torch-1.12.0+cu113 CUDA:0 (Tesla T4, 15110MiB)\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=1 with nc=5\n",
      "\n",
      "                 from  n    params  module                                  arguments                     \n",
      "  0                -1  1      5280  models.common.Conv                      [3, 48, 6, 2, 2]              \n",
      "  1                -1  1     41664  models.common.Conv                      [48, 96, 3, 2]                \n",
      "  2                -1  2     65280  models.common.C3                        [96, 96, 2]                   \n",
      "  3                -1  1    166272  models.common.Conv                      [96, 192, 3, 2]               \n",
      "  4                -1  4    444672  models.common.C3                        [192, 192, 4]                 \n",
      "  5                -1  1    664320  models.common.Conv                      [192, 384, 3, 2]              \n",
      "  6                -1  6   2512896  models.common.C3                        [384, 384, 6]                 \n",
      "  7                -1  1   2655744  models.common.Conv                      [384, 768, 3, 2]              \n",
      "  8                -1  2   4134912  models.common.C3                        [768, 768, 2]                 \n",
      "  9                -1  1   1476864  models.common.SPPF                      [768, 768, 5]                 \n",
      " 10                -1  1    295680  models.common.Conv                      [768, 384, 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  2   1182720  models.common.C3                        [768, 384, 2, False]          \n",
      " 14                -1  1     74112  models.common.Conv                      [384, 192, 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  2    296448  models.common.C3                        [384, 192, 2, False]          \n",
      " 18                -1  1    332160  models.common.Conv                      [192, 192, 3, 2]              \n",
      " 19          [-1, 14]  1         0  models.common.Concat                    [1]                           \n",
      " 20                -1  2   1035264  models.common.C3                        [384, 384, 2, False]          \n",
      " 21                -1  1   1327872  models.common.Conv                      [384, 384, 3, 2]              \n",
      " 22          [-1, 10]  1         0  models.common.Concat                    [1]                           \n",
      " 23                -1  2   4134912  models.common.C3                        [768, 768, 2, False]          \n",
      " 24      [17, 20, 23]  1     40410  models.yolo.Detect                      [5, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [192, 384, 768]]\n",
      "Model summary: 369 layers, 20887482 parameters, 20887482 gradients, 48.3 GFLOPs\n",
      "\n",
      "Transferred 475/481 items from runs/train/exp2/weights/best.pt\n",
      "\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n",
      "\u001b[34m\u001b[1mAutoBatch: \u001b[0mComputing optimal batch size for --imgsz 640\n",
      "\u001b[34m\u001b[1mAutoBatch: \u001b[0mCUDA:0 (Tesla T4) 14.76G total, 0.18G reserved, 0.16G allocated, 14.42G free\n",
      "      Params      GFLOPs  GPU_mem (GB)  forward (ms) backward (ms)                   input                  output\n",
      "    20887482       48.27         0.545          55.4         25.73        (1, 3, 640, 640)                    list\n",
      "    20887482       96.54         0.835         30.66          31.1        (2, 3, 640, 640)                    list\n",
      "    20887482       193.1         1.523         31.46         34.72        (4, 3, 640, 640)                    list\n",
      "    20887482       386.2         2.953         62.94         66.36        (8, 3, 640, 640)                    list\n",
      "    20887482       772.3         5.633         116.4         133.9       (16, 3, 640, 640)                    list\n",
      "\u001b[34m\u001b[1mAutoBatch: \u001b[0mUsing batch-size 37 for CUDA:0 12.82G/14.76G (87%) ✅\n",
      "Scaled weight_decay = 0.000578125\n",
      "\u001b[34m\u001b[1moptimizer:\u001b[0m SGD with parameter groups 79 weight (no decay), 82 weight, 82 bias\n",
      "\u001b[34m\u001b[1malbumentations: \u001b[0mversion 1.0.3 required by YOLOv5, but version 0.1.12 is currently installed\n",
      "\u001b[34m\u001b[1mtrain: \u001b[0mScanning '/content/drive-data/labels/train.cache' images and labels... 65 found, 39 missing, 0 empty, 0 corrupt: 100% 104/104 [00:00<?, ?it/s]\n",
      "\u001b[34m\u001b[1mval: \u001b[0mScanning '/content/drive-data/labels/valid.cache' images and labels... 6 found, 6 missing, 0 empty, 0 corrupt: 100% 12/12 [00:00<?, ?it/s]\n",
      "Plotting labels to runs/train/exp4/labels.jpg... \n",
      "\n",
      "\u001b[34m\u001b[1mAutoAnchor: \u001b[0m4.82 anchors/target, 1.000 Best Possible Recall (BPR). Current anchors are a good fit to dataset ✅\n",
      "Image sizes 640 train, 640 val\n",
      "Using 2 dataloader workers\n",
      "Logging results to \u001b[1mruns/train/exp4\u001b[0m\n",
      "Starting training for 100 epochs...\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "      0/99     12.9G    0.1132   0.02767   0.04933        54       640: 100% 3/3 [00:08<00:00,  2.86s/it]\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00,  1.96it/s]\n",
      "                 all         12          7          0          0          0          0\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "      1/99     12.9G    0.1129   0.02665   0.04907        59       640: 100% 3/3 [00:02<00:00,  1.14it/s]\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00,  2.89it/s]\n",
      "                 all         12          7          0          0          0          0\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "      2/99     12.9G    0.1075   0.02452   0.04895        42       640: 100% 3/3 [00:02<00:00,  1.05it/s]\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00,  3.72it/s]\n",
      "                 all         12          7   0.000402        0.2    0.00101   0.000201\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "      3/99     12.9G    0.1057   0.02325   0.04866        58       640: 100% 3/3 [00:06<00:00,  2.07s/it]\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00,  4.30it/s]\n",
      "                 all         12          7   0.000518        0.2    0.00234   0.000702\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "      4/99     12.9G    0.1013   0.02221   0.04736        71       640: 100% 3/3 [00:03<00:00,  1.23s/it]\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00,  4.90it/s]\n",
      "                 all         12          7   0.000722        0.2    0.00796    0.00239\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "      5/99     12.9G   0.09778   0.02017   0.04658        42       640: 100% 3/3 [00:05<00:00,  1.82s/it]\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00,  5.31it/s]\n",
      "                 all         12          7     0.0011        0.2     0.0199    0.00597\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "      6/99     12.9G   0.09173   0.02069   0.04633        63       640: 100% 3/3 [00:03<00:00,  1.16s/it]\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00,  6.08it/s]\n",
      "                 all         12          7     0.0015        0.2     0.0398     0.0159\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "      7/99     12.9G   0.08651   0.02129   0.04539        45       640: 100% 3/3 [00:05<00:00,  1.73s/it]\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00,  4.57it/s]\n",
      "                 all         12          7    0.00225        0.2     0.0663     0.0265\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "      8/99     12.9G   0.07888   0.02229    0.0451        61       640: 100% 3/3 [00:03<00:00,  1.12s/it]\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00,  5.33it/s]\n",
      "                 all         12          7    0.00416      0.267        0.1     0.0499\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "      9/99     12.9G   0.07229   0.02229   0.04423        54       640: 100% 3/3 [00:04<00:00,  1.61s/it]\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00,  5.74it/s]\n",
      "                 all         12          7    0.00797      0.333      0.103      0.052\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "     10/99     12.9G   0.06445   0.02504   0.04399        41       640: 100% 3/3 [00:03<00:00,  1.19s/it]\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00,  5.73it/s]\n",
      "                 all         12          7     0.0175      0.533      0.122     0.0629\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "     11/99     12.9G   0.05978   0.02107   0.04362        51       640: 100% 3/3 [00:05<00:00,  1.87s/it]\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00,  6.58it/s]\n",
      "                 all         12          7     0.0251      0.733      0.126     0.0621\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "     12/99     12.9G   0.05616   0.02413   0.04356        66       640: 100% 3/3 [00:03<00:00,  1.30s/it]\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00,  5.28it/s]\n",
      "                 all         12          7     0.0426      0.933      0.173      0.097\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "     13/99     12.9G   0.05235   0.02343   0.04087        52       640: 100% 3/3 [00:05<00:00,  1.83s/it]\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00,  4.72it/s]\n",
      "                 all         12          7     0.0452      0.933      0.218      0.127\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "     14/99     12.9G   0.05056   0.02536   0.04129        63       640: 100% 3/3 [00:03<00:00,  1.29s/it]\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00,  4.98it/s]\n",
      "                 all         12          7     0.0386      0.933      0.274      0.148\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "     15/99     12.9G   0.04718   0.02312   0.04088        53       640: 100% 3/3 [00:05<00:00,  1.84s/it]\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00,  5.45it/s]\n",
      "                 all         12          7      0.036      0.933      0.274       0.14\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "     16/99     12.9G    0.0479   0.02416   0.03881        60       640: 100% 3/3 [00:04<00:00,  1.38s/it]\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00,  4.84it/s]\n",
      "                 all         12          7     0.0284      0.933      0.215      0.139\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "     17/99     12.9G   0.04353   0.02364   0.03969        50       640: 100% 3/3 [00:05<00:00,  1.81s/it]\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00,  4.81it/s]\n",
      "                 all         12          7     0.0245          1      0.223      0.103\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "     18/99     12.9G    0.0409   0.02548   0.03947        47       640: 100% 3/3 [00:04<00:00,  1.47s/it]\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00,  5.38it/s]\n",
      "                 all         12          7      0.017          1      0.214      0.137\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "     19/99     12.9G   0.03754   0.02497   0.03883        43       640: 100% 3/3 [00:05<00:00,  1.95s/it]\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00,  5.29it/s]\n",
      "                 all         12          7     0.0135          1      0.222      0.135\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "     20/99     12.9G   0.03618   0.02318     0.038        48       640: 100% 3/3 [00:04<00:00,  1.53s/it]\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00,  4.40it/s]\n",
      "                 all         12          7     0.0113          1      0.238       0.15\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "     21/99     12.9G   0.03701   0.02195   0.03641        55       640: 100% 3/3 [00:05<00:00,  1.90s/it]\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00,  8.82it/s]\n",
      "                 all         12          7    0.00899          1      0.273      0.135\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "     22/99     12.9G   0.03873   0.02252    0.0353        63       640: 100% 3/3 [00:04<00:00,  1.36s/it]\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00,  3.80it/s]\n",
      "                 all         12          7    0.00804          1      0.322      0.159\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "     23/99     12.9G   0.04261    0.0236   0.03498        50       640: 100% 3/3 [00:04<00:00,  1.64s/it]\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00,  8.42it/s]\n",
      "                 all         12          7    0.00659          1        0.3      0.146\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "     24/99     12.9G   0.05041   0.01701   0.03486        52       640: 100% 3/3 [00:04<00:00,  1.39s/it]\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00,  4.75it/s]\n",
      "                 all         12          7    0.00666        0.8      0.306      0.132\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "     25/99     12.9G   0.06374   0.01974   0.03346        62       640: 100% 3/3 [00:07<00:00,  2.38s/it]\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00,  4.67it/s]\n",
      "                 all         12          7    0.00592          1      0.301      0.135\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "     26/99     12.9G   0.05368    0.0193   0.03469        46       640: 100% 3/3 [00:04<00:00,  1.39s/it]\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00,  5.35it/s]\n",
      "                 all         12          7    0.00604          1      0.303      0.144\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "     27/99     12.9G   0.04287   0.02036   0.03456        53       640: 100% 3/3 [00:05<00:00,  1.99s/it]\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00,  4.45it/s]\n",
      "                 all         12          7    0.00575          1      0.302     0.0989\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "     28/99     12.9G   0.04132    0.0221   0.03274        73       640: 100% 3/3 [00:03<00:00,  1.23s/it]\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00,  4.52it/s]\n",
      "                 all         12          7    0.00499          1      0.323      0.108\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "     29/99     12.9G   0.04466   0.01929   0.03243        50       640: 100% 3/3 [00:05<00:00,  1.95s/it]\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00,  5.07it/s]\n",
      "                 all         12          7    0.00488          1      0.316      0.165\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "     30/99     12.9G   0.04455   0.01855   0.03147        57       640: 100% 3/3 [00:03<00:00,  1.26s/it]\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00,  5.09it/s]\n",
      "                 all         12          7    0.00515          1      0.343      0.126\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "     31/99     12.9G   0.04332    0.0181   0.03053        52       640: 100% 3/3 [00:05<00:00,  1.97s/it]\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00,  6.32it/s]\n",
      "                 all         12          7     0.0044          1      0.336      0.172\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "     32/99     12.9G   0.04697   0.01784    0.0312        61       640: 100% 3/3 [00:03<00:00,  1.24s/it]\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00,  4.95it/s]\n",
      "                 all         12          7      0.192        0.8      0.295      0.151\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "     33/99     12.9G   0.04521   0.01703   0.03199        46       640: 100% 3/3 [00:05<00:00,  1.95s/it]\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00,  4.44it/s]\n",
      "                 all         12          7      0.464        0.4       0.43      0.252\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "     34/99     12.9G   0.04306   0.01618   0.03111        56       640: 100% 3/3 [00:03<00:00,  1.25s/it]\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00,  5.20it/s]\n",
      "                 all         12          7      0.422      0.207      0.479      0.218\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "     35/99     12.9G   0.04868   0.01547   0.03093        62       640: 100% 3/3 [00:05<00:00,  1.92s/it]\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00,  4.75it/s]\n",
      "                 all         12          7       0.54      0.333       0.33      0.178\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "     36/99     12.9G   0.04986   0.01524   0.02803        62       640: 100% 3/3 [00:05<00:00,  1.86s/it]\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00,  4.81it/s]\n",
      "                 all         12          7      0.577        0.4      0.428        0.2\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "     37/99     12.9G   0.04303   0.01522   0.02648        51       640: 100% 3/3 [00:06<00:00,  2.02s/it]\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00,  5.72it/s]\n",
      "                 all         12          7      0.596        0.4      0.446      0.198\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "     38/99     12.9G    0.0384   0.01538   0.02755        50       640: 100% 3/3 [00:04<00:00,  1.35s/it]\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00,  5.61it/s]\n",
      "                 all         12          7      0.829        0.4      0.442      0.244\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "     39/99     12.9G   0.04401   0.01402   0.02626        59       640: 100% 3/3 [00:05<00:00,  1.94s/it]\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00,  4.85it/s]\n",
      "                 all         12          7      0.839        0.4      0.441      0.175\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "     40/99     12.9G   0.04503   0.01274   0.02698        67       640: 100% 3/3 [00:04<00:00,  1.41s/it]\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00,  4.25it/s]\n",
      "                 all         12          7      0.765      0.333       0.36      0.187\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "     41/99     12.9G   0.04412   0.01356   0.02446        48       640: 100% 3/3 [00:05<00:00,  1.96s/it]\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00,  5.11it/s]\n",
      "                 all         12          7      0.798      0.354      0.409      0.201\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "     42/99     12.9G    0.0391   0.01273   0.02501        52       640: 100% 3/3 [00:04<00:00,  1.36s/it]\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00,  5.97it/s]\n",
      "                 all         12          7      0.804      0.333      0.382      0.234\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "     43/99     12.9G   0.03778   0.01399   0.02331        63       640: 100% 3/3 [00:05<00:00,  1.96s/it]\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00,  4.45it/s]\n",
      "                 all         12          7      0.875        0.4       0.38       0.19\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "     44/99     12.9G   0.03688   0.01415    0.0251        73       640: 100% 3/3 [00:04<00:00,  1.38s/it]\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00,  5.52it/s]\n",
      "                 all         12          7      0.853      0.395      0.514      0.248\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "     45/99     12.9G   0.04194   0.01081   0.02188        53       640: 100% 3/3 [00:05<00:00,  1.77s/it]\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00,  4.79it/s]\n",
      "                 all         12          7      0.868      0.397       0.52      0.284\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "     46/99     12.9G   0.04356   0.01202   0.02448        63       640: 100% 3/3 [00:03<00:00,  1.30s/it]\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00,  4.44it/s]\n",
      "                 all         12          7      0.873      0.387      0.562      0.227\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "     47/99     12.9G   0.04263   0.01099   0.02221        57       640: 100% 3/3 [00:07<00:00,  2.47s/it]\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00,  6.06it/s]\n",
      "                 all         12          7      0.872        0.4      0.423      0.246\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "     48/99     12.9G   0.04075   0.01119   0.02116        65       640: 100% 3/3 [00:04<00:00,  1.40s/it]\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00,  6.02it/s]\n",
      "                 all         12          7        0.9        0.4      0.547       0.25\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "     49/99     12.9G   0.03918   0.01035   0.01971        51       640: 100% 3/3 [00:05<00:00,  1.79s/it]\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00,  5.32it/s]\n",
      "                 all         12          7      0.889        0.4       0.68      0.448\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "     50/99     12.9G   0.03578   0.01174   0.02004        72       640: 100% 3/3 [00:03<00:00,  1.23s/it]\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00,  4.68it/s]\n",
      "                 all         12          7      0.949        0.4      0.669      0.416\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "     51/99     12.9G    0.0369    0.0087   0.01736        56       640: 100% 3/3 [00:05<00:00,  1.93s/it]\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00,  5.61it/s]\n",
      "                 all         12          7      0.936        0.4      0.672      0.421\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "     52/99     12.9G   0.03853   0.01128   0.01961        64       640: 100% 3/3 [00:04<00:00,  1.43s/it]\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00,  4.50it/s]\n",
      "                 all         12          7      0.955      0.374      0.692        0.4\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "     53/99     12.9G   0.03946   0.01038   0.01694        68       640: 100% 3/3 [00:06<00:00,  2.05s/it]\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00,  4.98it/s]\n",
      "                 all         12          7      0.973        0.4      0.713      0.405\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "     54/99     12.9G   0.03594  0.008129   0.01775        54       640: 100% 3/3 [00:03<00:00,  1.27s/it]\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00,  6.30it/s]\n",
      "                 all         12          7      0.697        0.6      0.713      0.427\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "     55/99     12.9G   0.04051  0.008483   0.01659        48       640: 100% 3/3 [00:05<00:00,  1.92s/it]\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00,  4.14it/s]\n",
      "                 all         12          7      0.545      0.746      0.746      0.499\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "     56/99     12.9G   0.03363   0.01032   0.01718        54       640: 100% 3/3 [00:03<00:00,  1.10s/it]\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00,  6.97it/s]\n",
      "                 all         12          7       0.56      0.773      0.746      0.461\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "     57/99     12.9G   0.03898  0.007739   0.01479        45       640: 100% 3/3 [00:05<00:00,  1.82s/it]\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00,  5.28it/s]\n",
      "                 all         12          7      0.566      0.794      0.713      0.438\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "     58/99     12.9G   0.04212  0.009266   0.01312        65       640: 100% 3/3 [00:04<00:00,  1.56s/it]\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00,  2.89it/s]\n",
      "                 all         12          7      0.614      0.756      0.746      0.459\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "     59/99     12.9G   0.03833  0.008543    0.0143        51       640: 100% 3/3 [00:06<00:00,  2.07s/it]\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00,  5.32it/s]\n",
      "                 all         12          7       0.63      0.793      0.746      0.484\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "     60/99     12.9G   0.03493  0.009251    0.0136        49       640: 100% 3/3 [00:04<00:00,  1.34s/it]\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00,  7.37it/s]\n",
      "                 all         12          7      0.619        0.8      0.746        0.5\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "     61/99     12.9G   0.03266  0.008717   0.01481        65       640: 100% 3/3 [00:05<00:00,  1.79s/it]\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00,  4.49it/s]\n",
      "                 all         12          7      0.624        0.8      0.746      0.425\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "     62/99     12.9G   0.03297  0.007783   0.01263        43       640: 100% 3/3 [00:04<00:00,  1.34s/it]\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00,  4.80it/s]\n",
      "                 all         12          7      0.861        0.6      0.736      0.475\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "     63/99     12.9G   0.03797  0.007162   0.01286        52       640: 100% 3/3 [00:05<00:00,  1.98s/it]\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00,  4.58it/s]\n",
      "                 all         12          7      0.855        0.6      0.736       0.54\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "     64/99     12.9G    0.0341  0.008258   0.01445        64       640: 100% 3/3 [00:03<00:00,  1.26s/it]\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00,  4.35it/s]\n",
      "                 all         12          7      0.614        0.8      0.836      0.596\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "     65/99     12.9G   0.03451  0.006847   0.01106        45       640: 100% 3/3 [00:05<00:00,  1.72s/it]\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00,  4.78it/s]\n",
      "                 all         12          7      0.898        0.6      0.829       0.53\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "     66/99     12.9G   0.03076  0.006941   0.01046        42       640: 100% 3/3 [00:04<00:00,  1.37s/it]\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00,  4.51it/s]\n",
      "                 all         12          7      0.683        0.8      0.829      0.588\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "     67/99     12.9G   0.03638   0.00731   0.01034        62       640: 100% 3/3 [00:06<00:00,  2.09s/it]\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00,  4.54it/s]\n",
      "                 all         12          7      0.719        0.8      0.829      0.559\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "     68/99     12.9G   0.03127  0.008091     0.011        67       640: 100% 3/3 [00:04<00:00,  1.40s/it]\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00,  4.51it/s]\n",
      "                 all         12          7      0.737        0.8      0.824      0.518\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "     69/99     12.9G   0.03405  0.007332   0.01181        61       640: 100% 3/3 [00:07<00:00,  2.37s/it]\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00,  8.16it/s]\n",
      "                 all         12          7      0.718        0.8      0.821      0.607\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "     70/99     12.9G   0.03067  0.006935   0.00993        49       640: 100% 3/3 [00:03<00:00,  1.17s/it]\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00,  4.39it/s]\n",
      "                 all         12          7      0.752        0.8      0.818      0.564\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "     71/99     12.9G     0.032  0.007809  0.008935        37       640: 100% 3/3 [00:05<00:00,  1.86s/it]\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00,  5.01it/s]\n",
      "                 all         12          7       0.77        0.8      0.818      0.553\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "     72/99     12.9G   0.03085  0.006952   0.01158        62       640: 100% 3/3 [00:04<00:00,  1.40s/it]\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00,  6.92it/s]\n",
      "                 all         12          7      0.763        0.8      0.821      0.566\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "     73/99     12.9G   0.03321  0.007092   0.01022        59       640: 100% 3/3 [00:06<00:00,  2.01s/it]\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00,  4.77it/s]\n",
      "                 all         12          7      0.754        0.8      0.824      0.591\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "     74/99     12.9G   0.02973  0.007954   0.01036        80       640: 100% 3/3 [00:04<00:00,  1.49s/it]\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00,  5.03it/s]\n",
      "                 all         12          7      0.765        0.8      0.829      0.564\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "     75/99     12.9G   0.02773  0.007614  0.009546        85       640: 100% 3/3 [00:05<00:00,  1.91s/it]\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00,  4.23it/s]\n",
      "                 all         12          7      0.726        0.8      0.829      0.526\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "     76/99     12.9G   0.02963  0.007355   0.01072        54       640: 100% 3/3 [00:04<00:00,  1.41s/it]\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00,  4.65it/s]\n",
      "                 all         12          7      0.769        0.8      0.846      0.594\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "     77/99     12.9G   0.02844  0.007035  0.007072        51       640: 100% 3/3 [00:05<00:00,  1.87s/it]\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00,  7.25it/s]\n",
      "                 all         12          7      0.801        0.8      0.846      0.478\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "     78/99     12.9G   0.03267  0.006626  0.007847        62       640: 100% 3/3 [00:04<00:00,  1.38s/it]\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00,  4.07it/s]\n",
      "                 all         12          7       0.79        0.8      0.862      0.614\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "     79/99     12.9G   0.02879  0.007951  0.007789        75       640: 100% 3/3 [00:05<00:00,  1.73s/it]\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00,  5.30it/s]\n",
      "                 all         12          7      0.822        0.8      0.862      0.498\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "     80/99     12.9G   0.02683  0.007646  0.007556        55       640: 100% 3/3 [00:04<00:00,  1.59s/it]\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00,  3.39it/s]\n",
      "                 all         12          7      0.832        0.8      0.862      0.567\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "     81/99     12.9G   0.02777  0.007045  0.007115        60       640: 100% 3/3 [00:06<00:00,  2.09s/it]\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00,  4.73it/s]\n",
      "                 all         12          7      0.839        0.8      0.862      0.518\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "     82/99     12.9G   0.02716  0.006108  0.006778        54       640: 100% 3/3 [00:04<00:00,  1.35s/it]\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00,  5.39it/s]\n",
      "                 all         12          7      0.843        0.8      0.862      0.567\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "     83/99     12.9G   0.02725  0.006634   0.00799        50       640: 100% 3/3 [00:05<00:00,  1.95s/it]\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00,  4.96it/s]\n",
      "                 all         12          7      0.849        0.8      0.862      0.541\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "     84/99     12.9G   0.02556  0.006443  0.005789        49       640: 100% 3/3 [00:04<00:00,  1.41s/it]\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00,  5.33it/s]\n",
      "                 all         12          7      0.851        0.8      0.895      0.604\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "     85/99     12.9G   0.02793  0.007321  0.007166        55       640: 100% 3/3 [00:05<00:00,  1.99s/it]\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00,  4.69it/s]\n",
      "                 all         12          7      0.856        0.8      0.895      0.614\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "     86/99     12.9G   0.02498  0.006807  0.007589        65       640: 100% 3/3 [00:03<00:00,  1.17s/it]\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00,  5.98it/s]\n",
      "                 all         12          7      0.867        0.8      0.895      0.567\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "     87/99     12.9G   0.02499  0.006097  0.006265        59       640: 100% 3/3 [00:06<00:00,  2.10s/it]\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00,  4.86it/s]\n",
      "                 all         12          7      0.877        0.8      0.895      0.607\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "     88/99     12.9G   0.02498   0.00725  0.006347        53       640: 100% 3/3 [00:04<00:00,  1.34s/it]\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00,  6.68it/s]\n",
      "                 all         12          7       0.89        0.8      0.862      0.558\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "     89/99     12.9G   0.02381  0.005985  0.006813        48       640: 100% 3/3 [00:05<00:00,  1.96s/it]\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00,  5.74it/s]\n",
      "                 all         12          7      0.894        0.8      0.862      0.591\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "     90/99     12.9G   0.02367  0.006431  0.006009        48       640: 100% 3/3 [00:04<00:00,  1.42s/it]\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00,  5.46it/s]\n",
      "                 all         12          7      0.903        0.8      0.862      0.577\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "     91/99     12.9G   0.02263  0.006058  0.006434        72       640: 100% 3/3 [00:07<00:00,  2.43s/it]\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00,  4.84it/s]\n",
      "                 all         12          7      0.908        0.8      0.862      0.531\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "     92/99     12.9G   0.02344  0.006338  0.007258        66       640: 100% 3/3 [00:04<00:00,  1.36s/it]\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00,  5.65it/s]\n",
      "                 all         12          7      0.915        0.8      0.862      0.558\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "     93/99     12.9G   0.02307  0.007729  0.006904        54       640: 100% 3/3 [00:06<00:00,  2.00s/it]\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00,  4.95it/s]\n",
      "                 all         12          7      0.919        0.8      0.862      0.558\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "     94/99     12.9G   0.02172  0.006704  0.006861        73       640: 100% 3/3 [00:04<00:00,  1.35s/it]\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00,  3.81it/s]\n",
      "                 all         12          7      0.917        0.8      0.862      0.541\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "     95/99     12.9G   0.02191  0.006877  0.007587        62       640: 100% 3/3 [00:05<00:00,  1.97s/it]\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00,  5.25it/s]\n",
      "                 all         12          7      0.925        0.8      0.862      0.577\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "     96/99     12.9G   0.02129  0.005912  0.006113        45       640: 100% 3/3 [00:04<00:00,  1.48s/it]\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00,  3.76it/s]\n",
      "                 all         12          7       0.93        0.8      0.862      0.561\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "     97/99     12.9G   0.02196  0.006957  0.005056        63       640: 100% 3/3 [00:06<00:00,  2.09s/it]\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00,  5.06it/s]\n",
      "                 all         12          7      0.932        0.8      0.862      0.541\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "     98/99     12.9G   0.02052  0.006029  0.003815        49       640: 100% 3/3 [00:03<00:00,  1.31s/it]\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00,  6.20it/s]\n",
      "                 all         12          7      0.937        0.8      0.862      0.558\n",
      "\n",
      "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
      "     99/99     12.9G   0.02045  0.006035    0.0062        55       640: 100% 3/3 [00:05<00:00,  1.89s/it]\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00,  4.77it/s]\n",
      "                 all         12          7      0.937        0.8      0.862      0.567\n",
      "\n",
      "100 epochs completed in 0.193 hours.\n",
      "Optimizer stripped from runs/train/exp4/weights/last.pt, 42.2MB\n",
      "Optimizer stripped from runs/train/exp4/weights/best.pt, 42.2MB\n",
      "\n",
      "Validating runs/train/exp4/weights/best.pt...\n",
      "Fusing layers... \n",
      "Model summary: 290 layers, 20869098 parameters, 0 gradients, 47.9 GFLOPs\n",
      "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 1/1 [00:00<00:00,  4.56it/s]\n",
      "                 all         12          7      0.856        0.8      0.895      0.614\n",
      "            crossing         12          1      0.669          1      0.995      0.796\n",
      "             parking         12          1          1          0      0.497      0.249\n",
      "           wrong way         12          1      0.943          1      0.995      0.497\n",
      "         no vehicles         12          1      0.761          1      0.995      0.697\n",
      "       traffic light         12          3      0.906          1      0.995       0.83\n",
      "Results saved to \u001b[1mruns/train/exp4\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "! python3 train.py --batch -1 --epochs 100 --data data.yaml --weights runs/train/exp2/weights/best.pt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8bc22282",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "8bc22282",
    "outputId": "871bfc64-3bb8-42da-b5b7-6c7bbc70fb0d"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using cache found in /root/.cache/torch/hub/ultralytics_yolov5_master\n",
      "\u001b[31m\u001b[1mrequirements:\u001b[0m PyYAML>=5.3.1 not found and is required by YOLOv5, attempting auto-update...\n",
      "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
      "Requirement already satisfied: PyYAML>=5.3.1 in /usr/local/lib/python3.7/dist-packages (6.0)\n",
      "\n",
      "\u001b[31m\u001b[1mrequirements:\u001b[0m 1 package updated per /root/.cache/torch/hub/ultralytics_yolov5_master/requirements.txt\n",
      "\u001b[31m\u001b[1mrequirements:\u001b[0m ⚠️ \u001b[1mRestart runtime or rerun command for updates to take effect\u001b[0m\n",
      "\n",
      "YOLOv5 🚀 2022-7-31 Python-3.7.13 torch-1.12.0+cu113 CUDA:0 (Tesla T4, 15110MiB)\n",
      "\n",
      "Fusing layers... \n",
      "Model summary: 290 layers, 20869098 parameters, 0 gradients, 47.9 GFLOPs\n",
      "Adding AutoShape... \n"
     ]
    }
   ],
   "source": [
    "model = torch.hub.load('ultralytics/yolov5', 'custom', path='runs/train/exp4/weights/best.pt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "YsMKi1T_2My1",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "YsMKi1T_2My1",
    "outputId": "1c1110b6-3218-4552-868f-f4c499f21d5e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/content\n"
     ]
    }
   ],
   "source": [
    "% cd .."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7251e501",
   "metadata": {
    "id": "7251e501"
   },
   "outputs": [],
   "source": [
    "path = os.path.join(IMAGES_PATH, 'test', '100.jpg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "173980de",
   "metadata": {
    "id": "173980de"
   },
   "outputs": [],
   "source": [
    "results = model(path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "50e0f81b",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "50e0f81b",
    "outputId": "9f64155a-72aa-4da4-ed8f-8040e4153812"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "image 1/1: 720x1280 2 crossings\n",
      "Speed: 26.3ms pre-process, 22.6ms inference, 1.6ms NMS per image at shape (1, 3, 384, 640)\n"
     ]
    }
   ],
   "source": [
    "results.print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9564618c",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 396
    },
    "id": "9564618c",
    "outputId": "f4f63eb8-6928-4630-d35a-8eee90935216"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f45274c8cd0>"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "fig, ax = plt.subplots(figsize=(16, 6))\n",
    "ax.imshow(results.render()[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f72e1dc2",
   "metadata": {
    "id": "f72e1dc2"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "collapsed_sections": [],
   "name": "dl4cv9.ipynb",
   "provenance": []
  },
  "gpuClass": "standard",
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}