YOLOv5.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU",
    "widgets": {
      "application/vnd.jupyter.widget-state+json": {
        "1f7df330663048998adcf8a45bc8f69b": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_e896e6096dd244c59d7955e2035cd729",
              "IPY_MODEL_a6ff238c29984b24bf6d0bd175c19430",
              "IPY_MODEL_3c085ba3f3fd4c3c8a6bb41b41ce1479"
            ],
            "layout": "IPY_MODEL_16b0c8aa6e0f427e8a54d3791abb7504"
          }
        },
        "e896e6096dd244c59d7955e2035cd729": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_c7b2dd0f78384cad8e400b282996cdf5",
            "placeholder": "​",
            "style": "IPY_MODEL_6a27e43b0e434edd82ee63f0a91036ca",
            "value": "100%"
          }
        },
        "a6ff238c29984b24bf6d0bd175c19430": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "success",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_cce0e6c0c4ec442cb47e65c674e02e92",
            "max": 818322941,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_c5b9f38e2f0d4f9aa97fe87265263743",
            "value": 818322941
          }
        },
        "3c085ba3f3fd4c3c8a6bb41b41ce1479": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_df554fb955c7454696beac5a82889386",
            "placeholder": "​",
            "style": "IPY_MODEL_74e9112a87a242f4831b7d68c7da6333",
            "value": " 780M/780M [00:05&lt;00:00, 126MB/s]"
          }
        },
        "16b0c8aa6e0f427e8a54d3791abb7504": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "c7b2dd0f78384cad8e400b282996cdf5": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "6a27e43b0e434edd82ee63f0a91036ca": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "cce0e6c0c4ec442cb47e65c674e02e92": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "c5b9f38e2f0d4f9aa97fe87265263743": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ProgressStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "bar_color": null,
            "description_width": ""
          }
        },
        "df554fb955c7454696beac5a82889386": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "74e9112a87a242f4831b7d68c7da6333": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        }
      }
    },
    "gpuClass": "standard"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/nnilayy/Hairline-Medium-Sized-and-Major-Sized-Crack-Detection-using-YOLOV5/blob/main/YOLOv5_For_Three_Class_Crack_Detection.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t6MPjfT5NrKQ"
      },
      "source": [
        "<div align=\"center\">\n",
        "\n",
        "  <a href=\"https://ultralytics.com/yolov5\" target=\"_blank\">\n",
        "    <img width=\"1024\", src=\"https://raw.githubusercontent.com/ultralytics/assets/master/yolov5/v70/splash.png\"></a>\n",
        "\n",
        "\n",
        "<br>\n",
        "  <a href=\"https://bit.ly/yolov5-paperspace-notebook\"><img src=\"https://assets.paperspace.io/img/gradient-badge.svg\" alt=\"Run on Gradient\"></a>\n",
        "  <a href=\"https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
        "  <a href=\"https://www.kaggle.com/ultralytics/yolov5\"><img src=\"https://kaggle.com/static/images/open-in-kaggle.svg\" alt=\"Open In Kaggle\"></a>\n",
        "<br>\n",
        "\n",
        "This <a href=\"https://github.com/ultralytics/yolov5\">YOLOv5</a> 🚀 notebook by <a href=\"https://ultralytics.com\">Ultralytics</a> presents simple train, validate and predict examples to help start your AI adventure.<br>See <a href=\"https://github.com/ultralytics/yolov5/issues/new/choose\">GitHub</a> for community support or <a href=\"https://ultralytics.com/contact\">contact us</a> for professional support.\n",
        "\n",
        "</div>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7mGmQbAO5pQb"
      },
      "source": [
        "# Setup\n",
        "\n",
        "Clone GitHub [repository](https://github.com/ultralytics/yolov5), install [dependencies](https://github.com/ultralytics/yolov5/blob/master/requirements.txt) and check PyTorch and GPU."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wbvMlHd_QwMG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "56e236c3-a456-41ae-fc3f-3a1988972f46"
      },
      "source": [
        "!git clone https://github.com/ultralytics/yolov5  # clone\n",
        "%cd yolov5\n",
        "%pip install -qr requirements.txt  # install\n",
        "\n",
        "import torch\n",
        "import utils\n",
        "display = utils.notebook_init()  # checks"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "YOLOv5 🚀 v7.0-21-ga1b6e79 Python-3.8.15 torch-1.12.1+cu113 CUDA:0 (Tesla T4, 15110MiB)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Setup complete ✅ (2 CPUs, 12.7 GB RAM, 22.7/78.2 GB disk)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!unzip -q ../main_images_1.zip -d ../"
      ],
      "metadata": {
        "id": "r9GfiFGOEqGb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4JnkELT0cIJg"
      },
      "source": [
        "# 1. Detect\n",
        "\n",
        "`detect.py` runs YOLOv5 inference on a variety of sources, downloading models automatically from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases), and saving results to `runs/detect`. Example inference sources are:\n",
        "\n",
        "```shell\n",
        "python detect.py --source 0  # webcam\n",
        "                          img.jpg  # image \n",
        "                          vid.mp4  # video\n",
        "                          screen  # screenshot\n",
        "                          path/  # directory\n",
        "                         'path/*.jpg'  # glob\n",
        "                         'https://youtu.be/Zgi9g1ksQHc'  # YouTube\n",
        "                         'rtsp://example.com/media.mp4'  # RTSP, RTMP, HTTP stream\n",
        "```"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zR9ZbuQCH7FX",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6a791320-bca7-4f67-f31c-cc55f5eb20c7"
      },
      "source": [
        "!python detect.py --weights /content/yolov5/runs/train/exp11/weights/best.pt --img 640 --conf 0.35 --source /content/8.jpeg\n",
        "# display.Image(filename='runs/detect/exp/zidane.jpg', width=600)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "python3: can't open file 'detect.py': [Errno 2] No such file or directory\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hkAzDWJ7cWTr"
      },
      "source": [
        "&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;\n",
        "<img align=\"left\" src=\"https://user-images.githubusercontent.com/26833433/127574988-6a558aa1-d268-44b9-bf6b-62d4c605cc72.jpg\" width=\"600\">"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0eq1SMWl6Sfn"
      },
      "source": [
        "# 2. Validate\n",
        "Validate a model's accuracy on the [COCO](https://cocodataset.org/#home) dataset's `val` or `test` splits. Models are downloaded automatically from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases). To show results by class use the `--verbose` flag."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WQPtK1QYVaD_",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "1f7df330663048998adcf8a45bc8f69b",
            "e896e6096dd244c59d7955e2035cd729",
            "a6ff238c29984b24bf6d0bd175c19430",
            "3c085ba3f3fd4c3c8a6bb41b41ce1479",
            "16b0c8aa6e0f427e8a54d3791abb7504",
            "c7b2dd0f78384cad8e400b282996cdf5",
            "6a27e43b0e434edd82ee63f0a91036ca",
            "cce0e6c0c4ec442cb47e65c674e02e92",
            "c5b9f38e2f0d4f9aa97fe87265263743",
            "df554fb955c7454696beac5a82889386",
            "74e9112a87a242f4831b7d68c7da6333"
          ]
        },
        "outputId": "c7d0a0d2-abfb-44c3-d60d-f99d0e7aabad"
      },
      "source": [
        "# Download COCO val\n",
        "torch.hub.download_url_to_file('https://ultralytics.com/assets/coco2017val.zip', 'tmp.zip')  # download (780M - 5000 images)\n",
        "!unzip -q tmp.zip -d ../datasets && rm tmp.zip  # unzip"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0.00/780M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1f7df330663048998adcf8a45bc8f69b"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X58w8JLpMnjH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5fc61358-7bc5-4310-a310-9059f66c6322"
      },
      "source": [
        "# Validate YOLOv5s on COCO val\n",
        "!python val.py --weights yolov5s.pt --data coco.yaml --img 640 --half"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[34m\u001b[1mval: \u001b[0mdata=/content/yolov5/data/coco.yaml, weights=['yolov5s.pt'], batch_size=32, imgsz=640, conf_thres=0.001, iou_thres=0.6, max_det=300, task=val, device=, workers=8, single_cls=False, augment=False, verbose=False, save_txt=False, save_hybrid=False, save_conf=False, save_json=True, project=runs/val, name=exp, exist_ok=False, half=True, dnn=False\n",
            "YOLOv5 🚀 v7.0-1-gb32f67f Python-3.7.15 torch-1.12.1+cu113 CUDA:0 (Tesla T4, 15110MiB)\n",
            "\n",
            "Fusing layers... \n",
            "YOLOv5s summary: 213 layers, 7225885 parameters, 0 gradients\n",
            "\u001b[34m\u001b[1mval: \u001b[0mScanning /content/datasets/coco/val2017... 4952 images, 48 backgrounds, 0 corrupt: 100% 5000/5000 [00:02<00:00, 1977.30it/s]\n",
            "\u001b[34m\u001b[1mval: \u001b[0mNew cache created: /content/datasets/coco/val2017.cache\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 157/157 [01:12<00:00,  2.17it/s]\n",
            "                   all       5000      36335       0.67      0.521      0.566      0.371\n",
            "Speed: 0.1ms pre-process, 2.9ms inference, 2.0ms NMS per image at shape (32, 3, 640, 640)\n",
            "\n",
            "Evaluating pycocotools mAP... saving runs/val/exp/yolov5s_predictions.json...\n",
            "loading annotations into memory...\n",
            "Done (t=0.43s)\n",
            "creating index...\n",
            "index created!\n",
            "Loading and preparing results...\n",
            "DONE (t=5.85s)\n",
            "creating index...\n",
            "index created!\n",
            "Running per image evaluation...\n",
            "Evaluate annotation type *bbox*\n",
            "DONE (t=82.22s).\n",
            "Accumulating evaluation results...\n",
            "DONE (t=14.92s).\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.374\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.572\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.402\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.211\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.423\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.489\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.311\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.516\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.566\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.378\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.625\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.723\n",
            "Results saved to \u001b[1mruns/val/exp\u001b[0m\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZY2VXXXu74w5"
      },
      "source": [
        "# 3. Train\n",
        "\n",
        "<p align=\"\"><a href=\"https://roboflow.com/?ref=ultralytics\"><img width=\"1000\" src=\"https://github.com/ultralytics/assets/raw/master/im/integrations-loop.png\"/></a></p>\n",
        "Close the active learning loop by sampling images from your inference conditions with the `roboflow` pip package\n",
        "<br><br>\n",
        "\n",
        "Train a YOLOv5s model on the [COCO128](https://www.kaggle.com/ultralytics/coco128) dataset with `--data coco128.yaml`, starting from pretrained `--weights yolov5s.pt`, or from randomly initialized `--weights '' --cfg yolov5s.yaml`.\n",
        "\n",
        "- **Pretrained [Models](https://github.com/ultralytics/yolov5/tree/master/models)** are downloaded\n",
        "automatically from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases)\n",
        "- **[Datasets](https://github.com/ultralytics/yolov5/tree/master/data)** available for autodownload include: [COCO](https://github.com/ultralytics/yolov5/blob/master/data/coco.yaml), [COCO128](https://github.com/ultralytics/yolov5/blob/master/data/coco128.yaml), [VOC](https://github.com/ultralytics/yolov5/blob/master/data/VOC.yaml), [Argoverse](https://github.com/ultralytics/yolov5/blob/master/data/Argoverse.yaml), [VisDrone](https://github.com/ultralytics/yolov5/blob/master/data/VisDrone.yaml), [GlobalWheat](https://github.com/ultralytics/yolov5/blob/master/data/GlobalWheat2020.yaml), [xView](https://github.com/ultralytics/yolov5/blob/master/data/xView.yaml), [Objects365](https://github.com/ultralytics/yolov5/blob/master/data/Objects365.yaml), [SKU-110K](https://github.com/ultralytics/yolov5/blob/master/data/SKU-110K.yaml).\n",
        "- **Training Results** are saved to `runs/train/` with incrementing run directories, i.e. `runs/train/exp2`, `runs/train/exp3` etc.\n",
        "<br><br>\n",
        "\n",
        "A **Mosaic Dataloader** is used for training which combines 4 images into 1 mosaic.\n",
        "\n",
        "## Train on Custom Data with Roboflow 🌟 NEW\n",
        "\n",
        "[Roboflow](https://roboflow.com/?ref=ultralytics) enables you to easily **organize, label, and prepare** a high quality dataset with your own custom data. Roboflow also makes it easy to establish an active learning pipeline, collaborate with your team on dataset improvement, and integrate directly into your model building workflow with the `roboflow` pip package.\n",
        "\n",
        "- Custom Training Example: [https://blog.roboflow.com/how-to-train-yolov5-on-a-custom-dataset/](https://blog.roboflow.com/how-to-train-yolov5-on-a-custom-dataset/?ref=ultralytics)\n",
        "- Custom Training Notebook: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/roboflow-ai/yolov5-custom-training-tutorial/blob/main/yolov5-custom-training.ipynb)\n",
        "<br>\n",
        "\n",
        "<p align=\"\"><a href=\"https://roboflow.com/?ref=ultralytics\"><img width=\"480\" src=\"https://uploads-ssl.webflow.com/5f6bc60e665f54545a1e52a5/6152a275ad4b4ac20cd2e21a_roboflow-annotate.gif\"/></a></p>Label images lightning fast (including with model-assisted labeling)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Select YOLOv5 🚀 logger {run: 'auto'}\n",
        "logger = 'TensorBoard' #@param ['TensorBoard', 'Comet', 'ClearML']\n",
        "\n",
        "if logger == 'TensorBoard':\n",
        "  %load_ext tensorboard\n",
        "  %tensorboard --logdir runs/train\n",
        "elif logger == 'Comet':\n",
        "  %pip install -q comet_ml\n",
        "  import comet_ml; comet_ml.init()\n",
        "elif logger == 'ClearML':\n",
        "  %pip install -q clearml\n",
        "  import clearml; clearml.browser_login()"
      ],
      "metadata": {
        "id": "i3oKtE4g-aNn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1NcFxRcFdJ_O",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f30336ed-41a8-4a5e-c498-69a234b6efb3"
      },
      "source": [
        "# Train YOLOv5s on COCO128 for 3 epochs\n",
        "!python train.py --img 640 --batch 16 --epochs 50 --data custom.yaml --weights /content/yolov5/runs/train/exp10/weights/best.pt --cache"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[34m\u001b[1mtrain: \u001b[0mweights=/content/yolov5/runs/train/exp10/weights/best.pt, cfg=, data=custom.yaml, hyp=data/hyps/hyp.scratch-low.yaml, epochs=50, batch_size=16, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, bucket=, cache=ram, 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[0mup to date with https://github.com/ultralytics/yolov5 ✅\n",
            "YOLOv5 🚀 v7.0-21-ga1b6e79 Python-3.8.15 torch-1.12.1+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[1mClearML: \u001b[0mrun 'pip install clearml' to automatically track, visualize and remotely train YOLOv5 🚀 in ClearML\n",
            "\u001b[34m\u001b[1mComet: \u001b[0mrun 'pip install comet_ml' to automatically track and visualize YOLOv5 🚀 runs in Comet\n",
            "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/train', view at http://localhost:6006/\n",
            "\n",
            "                 from  n    params  module                                  arguments                     \n",
            "  0                -1  1      3520  models.common.Conv                      [3, 32, 6, 2, 2]              \n",
            "  1                -1  1     18560  models.common.Conv                      [32, 64, 3, 2]                \n",
            "  2                -1  1     18816  models.common.C3                        [64, 64, 1]                   \n",
            "  3                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               \n",
            "  4                -1  2    115712  models.common.C3                        [128, 128, 2]                 \n",
            "  5                -1  1    295424  models.common.Conv                      [128, 256, 3, 2]              \n",
            "  6                -1  3    625152  models.common.C3                        [256, 256, 3]                 \n",
            "  7                -1  1   1180672  models.common.Conv                      [256, 512, 3, 2]              \n",
            "  8                -1  1   1182720  models.common.C3                        [512, 512, 1]                 \n",
            "  9                -1  1    656896  models.common.SPPF                      [512, 512, 5]                 \n",
            " 10                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
            " 11                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
            " 12           [-1, 6]  1         0  models.common.Concat                    [1]                           \n",
            " 13                -1  1    361984  models.common.C3                        [512, 256, 1, False]          \n",
            " 14                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n",
            " 15                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
            " 16           [-1, 4]  1         0  models.common.Concat                    [1]                           \n",
            " 17                -1  1     90880  models.common.C3                        [256, 128, 1, False]          \n",
            " 18                -1  1    147712  models.common.Conv                      [128, 128, 3, 2]              \n",
            " 19          [-1, 14]  1         0  models.common.Concat                    [1]                           \n",
            " 20                -1  1    296448  models.common.C3                        [256, 256, 1, False]          \n",
            " 21                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]              \n",
            " 22          [-1, 10]  1         0  models.common.Concat                    [1]                           \n",
            " 23                -1  1   1182720  models.common.C3                        [512, 512, 1, False]          \n",
            " 24      [17, 20, 23]  1     21576  models.yolo.Detect                      [3, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]\n",
            "Model summary: 214 layers, 7027720 parameters, 7027720 gradients, 16.0 GFLOPs\n",
            "\n",
            "Transferred 349/349 items from /content/yolov5/runs/train/exp10/weights/best.pt\n",
            "\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n",
            "\u001b[34m\u001b[1moptimizer:\u001b[0m SGD(lr=0.01) with parameter groups 57 weight(decay=0.0), 60 weight(decay=0.0005), 60 bias\n",
            "\u001b[34m\u001b[1malbumentations: \u001b[0mBlur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01), CLAHE(p=0.01, clip_limit=(1, 4.0), tile_grid_size=(8, 8))\n",
            "\u001b[34m\u001b[1mtrain: \u001b[0mScanning /content/main_images_1/labels/train.cache... 365 images, 0 backgrounds, 0 corrupt: 100% 365/365 [00:00<?, ?it/s]\n",
            "\u001b[34m\u001b[1mtrain: \u001b[0mCaching images (0.4GB ram): 100% 365/365 [00:00<00:00, 438.49it/s]\n",
            "\u001b[34m\u001b[1mval: \u001b[0mScanning /content/main_images_1/labels/val.cache... 95 images, 0 backgrounds, 0 corrupt: 100% 95/95 [00:00<?, ?it/s]\n",
            "\u001b[34m\u001b[1mval: \u001b[0mCaching images (0.1GB ram): 100% 95/95 [00:00<00:00, 139.48it/s]\n",
            "\n",
            "\u001b[34m\u001b[1mAutoAnchor: \u001b[0m2.03 anchors/target, 0.995 Best Possible Recall (BPR). Current anchors are a good fit to dataset ✅\n",
            "Plotting labels to runs/train/exp11/labels.jpg... \n",
            "Image sizes 640 train, 640 val\n",
            "Using 2 dataloader workers\n",
            "Logging results to \u001b[1mruns/train/exp11\u001b[0m\n",
            "Starting training for 50 epochs...\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "       0/49      3.74G    0.02013    0.01404   0.005371         35        640: 100% 23/23 [00:08<00:00,  2.77it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 3/3 [00:00<00:00,  3.65it/s]\n",
            "                   all         95        102      0.853      0.775      0.822      0.437\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "       1/49      4.63G    0.02528    0.01447   0.005265         32        640: 100% 23/23 [00:05<00:00,  4.19it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 3/3 [00:00<00:00,  3.66it/s]\n",
            "                   all         95        102      0.842       0.78      0.822       0.44\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "       2/49      4.63G    0.02422    0.01509   0.005037         37        640: 100% 23/23 [00:05<00:00,  4.16it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 3/3 [00:00<00:00,  3.75it/s]\n",
            "                   all         95        102      0.797      0.663      0.715      0.326\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "       3/49      4.63G    0.02984    0.01578   0.005684         41        640: 100% 23/23 [00:05<00:00,  4.17it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 3/3 [00:00<00:00,  3.67it/s]\n",
            "                   all         95        102       0.73      0.688      0.723      0.365\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "       4/49      4.63G    0.03287     0.0146   0.006961         37        640: 100% 23/23 [00:05<00:00,  4.24it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 3/3 [00:00<00:00,  3.81it/s]\n",
            "                   all         95        102      0.792      0.661       0.71      0.366\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "       5/49      4.63G    0.03161    0.01597   0.007742         35        640: 100% 23/23 [00:05<00:00,  4.28it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 3/3 [00:00<00:00,  3.77it/s]\n",
            "                   all         95        102      0.754      0.682      0.728      0.356\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "       6/49      4.63G     0.0301    0.01469   0.007368         24        640: 100% 23/23 [00:05<00:00,  4.32it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 3/3 [00:00<00:00,  3.37it/s]\n",
            "                   all         95        102      0.811        0.7      0.776        0.4\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "       7/49      4.63G    0.03545    0.01576   0.008121         30        640: 100% 23/23 [00:05<00:00,  4.18it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 3/3 [00:00<00:00,  3.75it/s]\n",
            "                   all         95        102      0.641       0.56      0.628      0.292\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "       8/49      4.63G    0.03448    0.01559    0.01033         34        640: 100% 23/23 [00:05<00:00,  4.30it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 3/3 [00:00<00:00,  3.64it/s]\n",
            "                   all         95        102      0.703      0.733      0.712      0.365\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "       9/49      4.63G    0.03647    0.01571    0.01048         36        640: 100% 23/23 [00:05<00:00,  3.87it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 3/3 [00:01<00:00,  2.13it/s]\n",
            "                   all         95        102      0.769       0.67      0.722      0.366\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      10/49      4.63G    0.03082    0.01605   0.009524         36        640: 100% 23/23 [00:06<00:00,  3.59it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 3/3 [00:00<00:00,  3.67it/s]\n",
            "                   all         95        102      0.646      0.621      0.602      0.289\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      11/49      4.63G     0.0315    0.01583    0.01333         32        640: 100% 23/23 [00:05<00:00,  4.15it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 3/3 [00:00<00:00,  3.71it/s]\n",
            "                   all         95        102       0.65       0.71      0.633      0.303\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      12/49      4.63G    0.03305    0.01511   0.008947         33        640: 100% 23/23 [00:05<00:00,  4.27it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 3/3 [00:00<00:00,  3.80it/s]\n",
            "                   all         95        102       0.63      0.633      0.644      0.294\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      13/49      4.63G    0.02916    0.01586    0.01098         33        640: 100% 23/23 [00:05<00:00,  4.33it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 3/3 [00:00<00:00,  3.74it/s]\n",
            "                   all         95        102      0.708      0.704      0.735      0.316\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      14/49      4.63G    0.02988    0.01617   0.008282         33        640: 100% 23/23 [00:05<00:00,  4.42it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 3/3 [00:00<00:00,  3.64it/s]\n",
            "                   all         95        102      0.723      0.677      0.716      0.334\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      15/49      4.63G    0.03293    0.01613   0.009677         39        640: 100% 23/23 [00:05<00:00,  4.33it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 3/3 [00:00<00:00,  3.83it/s]\n",
            "                   all         95        102      0.808      0.694      0.712      0.331\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      16/49      4.63G    0.02918    0.01514   0.009385         28        640: 100% 23/23 [00:05<00:00,  4.30it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 3/3 [00:00<00:00,  3.72it/s]\n",
            "                   all         95        102      0.774      0.722      0.703      0.343\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      17/49      4.63G    0.03227    0.01598   0.008303         31        640: 100% 23/23 [00:05<00:00,  4.28it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 3/3 [00:00<00:00,  3.74it/s]\n",
            "                   all         95        102       0.74      0.729      0.728       0.34\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      18/49      4.63G    0.03144    0.01604    0.01073         36        640: 100% 23/23 [00:05<00:00,  4.32it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 3/3 [00:00<00:00,  3.61it/s]\n",
            "                   all         95        102      0.731      0.746      0.752      0.335\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      19/49      4.63G    0.03289    0.01614   0.008279         35        640: 100% 23/23 [00:05<00:00,  4.34it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 3/3 [00:00<00:00,  3.64it/s]\n",
            "                   all         95        102      0.755      0.658      0.741      0.342\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      20/49      4.63G    0.03136    0.01558   0.009213         33        640: 100% 23/23 [00:05<00:00,  4.31it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 3/3 [00:00<00:00,  3.58it/s]\n",
            "                   all         95        102      0.681      0.628      0.626      0.307\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      21/49      4.63G    0.02881    0.01548    0.00826         36        640: 100% 23/23 [00:05<00:00,  4.09it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 3/3 [00:00<00:00,  3.83it/s]\n",
            "                   all         95        102      0.722      0.727      0.671      0.351\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      22/49      4.63G    0.03282    0.01618   0.008854         23        640: 100% 23/23 [00:05<00:00,  4.25it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 3/3 [00:00<00:00,  3.75it/s]\n",
            "                   all         95        102      0.703      0.742      0.712      0.331\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      23/49      4.63G    0.03308    0.01528   0.007794         27        640: 100% 23/23 [00:05<00:00,  4.20it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 3/3 [00:00<00:00,  3.78it/s]\n",
            "                   all         95        102      0.725      0.744       0.73      0.346\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      24/49      4.63G    0.02973    0.01571   0.007857         31        640: 100% 23/23 [00:05<00:00,  4.29it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 3/3 [00:00<00:00,  3.64it/s]\n",
            "                   all         95        102      0.687      0.686      0.691      0.368\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      25/49      4.63G    0.03386    0.01555   0.009899         28        640: 100% 23/23 [00:05<00:00,  4.30it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 3/3 [00:00<00:00,  3.56it/s]\n",
            "                   all         95        102      0.626      0.702       0.65       0.33\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      26/49      4.63G    0.03169    0.01589   0.009163         32        640: 100% 23/23 [00:05<00:00,  4.29it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 3/3 [00:00<00:00,  3.70it/s]\n",
            "                   all         95        102      0.671      0.679      0.667      0.341\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      27/49      4.63G    0.02972     0.0154   0.006251         35        640: 100% 23/23 [00:05<00:00,  4.23it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 3/3 [00:00<00:00,  3.85it/s]\n",
            "                   all         95        102      0.788      0.737      0.752      0.394\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      28/49      4.63G     0.0282    0.01538    0.00682         32        640: 100% 23/23 [00:05<00:00,  4.29it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 3/3 [00:00<00:00,  3.68it/s]\n",
            "                   all         95        102      0.831      0.708      0.772      0.422\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      29/49      4.63G    0.02775    0.01529   0.009329         23        640: 100% 23/23 [00:05<00:00,  4.34it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 3/3 [00:00<00:00,  3.51it/s]\n",
            "                   all         95        102      0.778      0.698      0.741      0.407\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      30/49      4.63G    0.02924    0.01535    0.01051         35        640: 100% 23/23 [00:05<00:00,  4.28it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 3/3 [00:00<00:00,  3.79it/s]\n",
            "                   all         95        102      0.777      0.708      0.727      0.403\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      31/49      4.63G    0.02762    0.01473   0.006956         29        640: 100% 23/23 [00:05<00:00,  4.15it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 3/3 [00:00<00:00,  3.80it/s]\n",
            "                   all         95        102      0.716      0.736      0.747      0.391\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      32/49      4.63G    0.02604    0.01427   0.008064         32        640: 100% 23/23 [00:05<00:00,  4.24it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 3/3 [00:00<00:00,  3.43it/s]\n",
            "                   all         95        102      0.768      0.715      0.747      0.375\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      33/49      4.63G    0.03078    0.01484   0.008403         29        640: 100% 23/23 [00:05<00:00,  4.17it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 3/3 [00:00<00:00,  3.68it/s]\n",
            "                   all         95        102      0.759       0.71       0.74      0.373\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      34/49      4.63G     0.0281    0.01485    0.00659         36        640: 100% 23/23 [00:05<00:00,  4.20it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 3/3 [00:00<00:00,  3.51it/s]\n",
            "                   all         95        102       0.83      0.716      0.786       0.42\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      35/49      4.63G    0.02716    0.01508   0.006972         31        640: 100% 23/23 [00:05<00:00,  4.14it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 3/3 [00:00<00:00,  3.77it/s]\n",
            "                   all         95        102      0.786      0.742      0.774      0.434\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      36/49      4.63G    0.02653    0.01527   0.006354         31        640: 100% 23/23 [00:05<00:00,  4.18it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 3/3 [00:00<00:00,  3.69it/s]\n",
            "                   all         95        102      0.763      0.706      0.749      0.416\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      37/49      4.63G    0.02706    0.01531   0.008072         37        640: 100% 23/23 [00:05<00:00,  4.20it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 3/3 [00:00<00:00,  3.64it/s]\n",
            "                   all         95        102      0.795      0.651       0.71      0.404\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      38/49      4.63G    0.02715    0.01504   0.006282         37        640: 100% 23/23 [00:05<00:00,  4.09it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 3/3 [00:00<00:00,  3.76it/s]\n",
            "                   all         95        102      0.867      0.704      0.765      0.424\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      39/49      4.63G    0.02724    0.01545   0.007327         36        640: 100% 23/23 [00:05<00:00,  4.10it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 3/3 [00:00<00:00,  3.81it/s]\n",
            "                   all         95        102      0.857      0.754      0.798       0.43\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      40/49      4.63G    0.02428    0.01541   0.007347         34        640: 100% 23/23 [00:05<00:00,  4.18it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 3/3 [00:00<00:00,  3.65it/s]\n",
            "                   all         95        102      0.838      0.748      0.795       0.43\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      41/49      4.63G    0.02825    0.01506   0.006158         32        640: 100% 23/23 [00:05<00:00,  4.28it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 3/3 [00:00<00:00,  3.52it/s]\n",
            "                   all         95        102      0.864      0.783      0.811      0.436\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      42/49      4.63G    0.02873    0.01569   0.005852         40        640: 100% 23/23 [00:05<00:00,  4.20it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 3/3 [00:00<00:00,  3.74it/s]\n",
            "                   all         95        102       0.91       0.69      0.793      0.439\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      43/49      4.63G    0.02807    0.01478   0.006313         31        640: 100% 23/23 [00:05<00:00,  4.23it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 3/3 [00:00<00:00,  3.65it/s]\n",
            "                   all         95        102       0.89      0.701      0.806      0.448\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      44/49      4.63G     0.0245    0.01439   0.005326         32        640: 100% 23/23 [00:05<00:00,  4.24it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 3/3 [00:00<00:00,  3.57it/s]\n",
            "                   all         95        102      0.864      0.699      0.786      0.433\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      45/49      4.63G    0.02684    0.01408   0.007163         35        640: 100% 23/23 [00:05<00:00,  4.34it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 3/3 [00:00<00:00,  3.40it/s]\n",
            "                   all         95        102      0.847      0.699      0.763      0.432\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      46/49      4.63G    0.02602    0.01498   0.004783         38        640: 100% 23/23 [00:05<00:00,  4.32it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 3/3 [00:00<00:00,  3.40it/s]\n",
            "                   all         95        102      0.848      0.697      0.773      0.429\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      47/49      4.63G    0.02452    0.01368   0.003924         34        640: 100% 23/23 [00:07<00:00,  2.90it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 3/3 [00:00<00:00,  3.03it/s]\n",
            "                   all         95        102      0.851      0.696      0.778       0.43\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      48/49      4.63G    0.02424     0.0146    0.00468         34        640: 100% 23/23 [00:05<00:00,  4.31it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 3/3 [00:00<00:00,  3.36it/s]\n",
            "                   all         95        102      0.845      0.714      0.777      0.428\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      49/49      4.63G    0.02531    0.01463   0.004601         28        640: 100% 23/23 [00:05<00:00,  4.26it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 3/3 [00:00<00:00,  3.72it/s]\n",
            "                   all         95        102      0.872      0.713      0.784      0.434\n",
            "\n",
            "50 epochs completed in 0.094 hours.\n",
            "Optimizer stripped from runs/train/exp11/weights/last.pt, 14.5MB\n",
            "Optimizer stripped from runs/train/exp11/weights/best.pt, 14.5MB\n",
            "\n",
            "Validating runs/train/exp11/weights/best.pt...\n",
            "Fusing layers... \n",
            "Model summary: 157 layers, 7018216 parameters, 0 gradients, 15.8 GFLOPs\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 3/3 [00:01<00:00,  1.54it/s]\n",
            "                   all         95        102      0.892      0.701      0.806      0.447\n",
            "           Minor Crack         95         33      0.822      0.667       0.68      0.362\n",
            "          Medium Crack         95         39      0.881      0.769      0.831      0.425\n",
            "           Major Crack         95         30      0.973      0.667      0.907      0.554\n",
            "Results saved to \u001b[1mruns/train/exp11\u001b[0m\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "15glLzbQx5u0"
      },
      "source": [
        "# 4. Visualize"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Comet Logging and Visualization 🌟 NEW\n",
        "[Comet](https://bit.ly/yolov5-readme-comet) is now fully integrated with YOLOv5. Track and visualize model metrics in real time, save your hyperparameters, datasets, and model checkpoints, and visualize your model predictions with [Comet Custom Panels](https://bit.ly/yolov5-colab-comet-panels)! Comet makes sure you never lose track of your work and makes it easy to share results and collaborate across teams of all sizes! \n",
        "\n",
        "Getting started is easy:\n",
        "```shell\n",
        "pip install comet_ml  # 1. install\n",
        "export COMET_API_KEY=<Your API Key>  # 2. paste API key\n",
        "python train.py --img 640 --epochs 3 --data coco128.yaml --weights yolov5s.pt  # 3. train\n",
        "```\n",
        "\n",
        "To learn more about all of the supported Comet features for this integration, check out the [Comet Tutorial](https://github.com/ultralytics/yolov5/tree/master/utils/loggers/comet). If you'd like to learn more about Comet, head over to our [documentation](https://bit.ly/yolov5-colab-comet-docs). Get started by trying out the Comet Colab Notebook:\n",
        "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1RG0WOQyxlDlo5Km8GogJpIEJlg_5lyYO?usp=sharing)\n",
        "\n",
        "<img width=\"1920\" alt=\"yolo-ui\" src=\"https://user-images.githubusercontent.com/26833433/202851203-164e94e1-2238-46dd-91f8-de020e9d6b41.png\">"
      ],
      "metadata": {
        "id": "nWOsI5wJR1o3"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## ClearML Logging and Automation 🌟 NEW\n",
        "\n",
        "[ClearML](https://cutt.ly/yolov5-notebook-clearml) is completely integrated into YOLOv5 to track your experimentation, manage dataset versions and even remotely execute training runs. To enable ClearML (check cells above):\n",
        "\n",
        "- `pip install clearml`\n",
        "- run `clearml-init` to connect to a ClearML server (**deploy your own [open-source server](https://github.com/allegroai/clearml-server)**, or use our [free hosted server](https://cutt.ly/yolov5-notebook-clearml))\n",
        "\n",
        "You'll get all the great expected features from an experiment manager: live updates, model upload, experiment comparison etc. but ClearML also tracks uncommitted changes and installed packages for example. Thanks to that ClearML Tasks (which is what we call experiments) are also reproducible on different machines! With only 1 extra line, we can schedule a YOLOv5 training task on a queue to be executed by any number of ClearML Agents (workers).\n",
        "\n",
        "You can use ClearML Data to version your dataset and then pass it to YOLOv5 simply using its unique ID. This will help you keep track of your data without adding extra hassle. Explore the [ClearML Tutorial](https://github.com/ultralytics/yolov5/tree/master/utils/loggers/clearml) for details!\n",
        "\n",
        "<a href=\"https://cutt.ly/yolov5-notebook-clearml\">\n",
        "<img alt=\"ClearML Experiment Management UI\" src=\"https://github.com/thepycoder/clearml_screenshots/raw/main/scalars.jpg\" width=\"1280\"/></a>"
      ],
      "metadata": {
        "id": "Lay2WsTjNJzP"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-WPvRbS5Swl6"
      },
      "source": [
        "## Local Logging\n",
        "\n",
        "Training results are automatically logged with [Tensorboard](https://www.tensorflow.org/tensorboard) and [CSV](https://github.com/ultralytics/yolov5/pull/4148) loggers to `runs/train`, with a new experiment directory created for each new training as `runs/train/exp2`, `runs/train/exp3`, etc.\n",
        "\n",
        "This directory contains train and val statistics, mosaics, labels, predictions and augmentated mosaics, as well as metrics and charts including precision-recall (PR) curves and confusion matrices. \n",
        "\n",
        "<img alt=\"Local logging results\" src=\"https://user-images.githubusercontent.com/26833433/183222430-e1abd1b7-782c-4cde-b04d-ad52926bf818.jpg\" width=\"1280\"/>\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Zelyeqbyt3GD"
      },
      "source": [
        "# Environments\n",
        "\n",
        "YOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including [CUDA](https://developer.nvidia.com/cuda)/[CUDNN](https://developer.nvidia.com/cudnn), [Python](https://www.python.org/) and [PyTorch](https://pytorch.org/) preinstalled):\n",
        "\n",
        "- **Notebooks** with free GPU: <a href=\"https://bit.ly/yolov5-paperspace-notebook\"><img src=\"https://assets.paperspace.io/img/gradient-badge.svg\" alt=\"Run on Gradient\"></a> <a href=\"https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a> <a href=\"https://www.kaggle.com/ultralytics/yolov5\"><img src=\"https://kaggle.com/static/images/open-in-kaggle.svg\" alt=\"Open In Kaggle\"></a>\n",
        "- **Google Cloud** Deep Learning VM. See [GCP Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/GCP-Quickstart)\n",
        "- **Amazon** Deep Learning AMI. See [AWS Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/AWS-Quickstart)\n",
        "- **Docker Image**. See [Docker Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/Docker-Quickstart) <a href=\"https://hub.docker.com/r/ultralytics/yolov5\"><img src=\"https://img.shields.io/docker/pulls/ultralytics/yolov5?logo=docker\" alt=\"Docker Pulls\"></a>\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6Qu7Iesl0p54"
      },
      "source": [
        "# Status\n",
        "\n",
        "![YOLOv5 CI](https://github.com/ultralytics/yolov5/actions/workflows/ci-testing.yml/badge.svg)\n",
        "\n",
        "If this badge is green, all [YOLOv5 GitHub Actions](https://github.com/ultralytics/yolov5/actions) Continuous Integration (CI) tests are currently passing. CI tests verify correct operation of YOLOv5 training ([train.py](https://github.com/ultralytics/yolov5/blob/master/train.py)), testing ([val.py](https://github.com/ultralytics/yolov5/blob/master/val.py)), inference ([detect.py](https://github.com/ultralytics/yolov5/blob/master/detect.py)) and export ([export.py](https://github.com/ultralytics/yolov5/blob/master/export.py)) on macOS, Windows, and Ubuntu every 24 hours and on every commit.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IEijrePND_2I"
      },
      "source": [
        "# Appendix\n",
        "\n",
        "Additional content below."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GMusP4OAxFu6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 399
        },
        "outputId": "2e0e0e99-3b64-4885-a9ce-fdc5907a8b03"
      },
      "source": [
        "# YOLOv5 PyTorch HUB Inference (DetectionModels only)\n",
        "import torch\n",
        "\n",
        "model = torch.hub.load('ultralytics/yolov5', 'yolov5s', 'custom', path='/content/best.pt')  # yolov5n - yolov5x6 or custom\n",
        "im = 'https://ultralytics.com/images/zidane.jpg'  # file, Path, PIL.Image, OpenCV, nparray, list\n",
        "results = model(im)  # inference\n",
        "results.print()  # or .show(), .save(), .crop(), .pandas(), etc."
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Downloading: \"https://github.com/ultralytics/yolov5/zipball/master\" to /root/.cache/torch/hub/master.zip\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-6-aab9e798733e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhub\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'ultralytics/yolov5'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'yolov5s'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'custom'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpath\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'/content/best.pt'\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# yolov5n - yolov5x6 or custom\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0mim\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'https://ultralytics.com/images/zidane.jpg'\u001b[0m  \u001b[0;31m# file, Path, PIL.Image, OpenCV, nparray, list\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mim\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# inference\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/torch/hub.py\u001b[0m in \u001b[0;36mload\u001b[0;34m(repo_or_dir, model, source, trust_repo, force_reload, verbose, skip_validation, *args, **kwargs)\u001b[0m\n\u001b[1;32m    538\u001b[0m                                            verbose=verbose, skip_validation=skip_validation)\n\u001b[1;32m    539\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 540\u001b[0;31m     \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_load_local\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrepo_or_dir\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    541\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    542\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/torch/hub.py\u001b[0m in \u001b[0;36m_load_local\u001b[0;34m(hubconf_dir, model, *args, **kwargs)\u001b[0m\n\u001b[1;32m    567\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    568\u001b[0m     \u001b[0mentry\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_load_entry_from_hubconf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhub_module\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 569\u001b[0;31m     \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mentry\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    570\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    571\u001b[0m     \u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhubconf_dir\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mTypeError\u001b[0m: yolov5s() got an unexpected keyword argument 'path'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "id": "-28da7qdMl-n"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}