infrared_detection.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/fuchstech/AKUS/blob/main/infrared_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/main/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>We hope that the resources in this notebook will help you get the most out of YOLOv5. Please browse the YOLOv5 <a href=\"https://docs.ultralytics.com/yolov5\">Docs</a> for details, raise an issue on <a href=\"https://github.com/ultralytics/yolov5\">GitHub</a> for support, and join our <a href=\"https://ultralytics.com/discord\">Discord</a> community for questions and discussions!\n",
        "\n",
        "</div>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "id": "5cTn87jK1gt0",
        "outputId": "7d00984e-8b8b-4569-f3d7-346b00174b86",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%cd /content/drive/MyDrive"
      ],
      "metadata": {
        "id": "mTUMQEqF2AVf",
        "outputId": "81ce5830-34fe-498c-e6d0-e52f5649131a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/drive/MyDrive\n"
          ]
        }
      ]
    },
    {
      "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": "b1f0a0e9-65b5-4f07-a1bc-f18aa25fc1ca"
      },
      "source": [
        "!git clone https://github.com/ultralytics/yolov5  # clone\n",
        "%cd yolov5\n",
        "%pip install -qr requirements.txt comet_ml  # 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 🚀 v7.0-224-g6262c7f Python-3.10.12 torch-2.0.1+cu118 CUDA:0 (Tesla T4, 15102MiB)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Setup complete ✅ (2 CPUs, 12.7 GB RAM, 26.3/78.2 GB disk)\n"
          ]
        }
      ]
    },
    {
      "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/LNwODJXcvt4'  # 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": "ed723da1-08a6-4301-fb96-28e67108f0a4"
      },
      "source": [
        "!python detect.py --weights runs/train/exp5/weights/best.pt --img 640 --conf 0.25 --source screen\n",
        "# display.Image(filename='runs/detect/exp/zidane.jpg', width=600)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[34m\u001b[1mdetect: \u001b[0mweights=['runs/train/exp5/weights/best.pt'], source=screen, data=data/coco128.yaml, imgsz=[640, 640], conf_thres=0.25, iou_thres=0.45, max_det=1000, device=, view_img=False, save_txt=False, save_csv=False, save_conf=False, save_crop=False, nosave=False, classes=None, agnostic_nms=False, augment=False, visualize=False, update=False, project=runs/detect, name=exp, exist_ok=False, line_thickness=3, hide_labels=False, hide_conf=False, half=False, dnn=False, vid_stride=1\n",
            "YOLOv5 🚀 v7.0-224-g6262c7f Python-3.10.12 torch-2.0.1+cu118 CUDA:0 (Tesla T4, 15102MiB)\n",
            "\n",
            "Fusing layers... \n",
            "Model summary: 157 layers, 7020913 parameters, 0 gradients, 15.8 GFLOPs\n",
            "\u001b[31m\u001b[1mrequirements:\u001b[0m Ultralytics requirement ['mss'] not found, attempting AutoUpdate...\n",
            "Collecting mss\n",
            "  Downloading mss-9.0.1-py3-none-any.whl (22 kB)\n",
            "Installing collected packages: mss\n",
            "Successfully installed mss-9.0.1\n",
            "\n",
            "\u001b[31m\u001b[1mrequirements:\u001b[0m AutoUpdate success ✅ 5.0s, installed 1 package: ['mss']\n",
            "\u001b[31m\u001b[1mrequirements:\u001b[0m ⚠️ \u001b[1mRestart runtime or rerun command for updates to take effect\u001b[0m\n",
            "\n",
            "Traceback (most recent call last):\n",
            "  File \"/content/drive/MyDrive/yolov5/detect.py\", line 285, in <module>\n",
            "    main(opt)\n",
            "  File \"/content/drive/MyDrive/yolov5/detect.py\", line 280, in main\n",
            "    run(**vars(opt))\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/torch/utils/_contextlib.py\", line 115, in decorate_context\n",
            "    return func(*args, **kwargs)\n",
            "  File \"/content/drive/MyDrive/yolov5/detect.py\", line 112, in run\n",
            "    dataset = LoadScreenshots(source, img_size=imgsz, stride=stride, auto=pt)\n",
            "  File \"/content/drive/MyDrive/yolov5/utils/dataloaders.py\", line 211, in __init__\n",
            "    self.sct = mss.mss()\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/mss/factory.py\", line 34, in mss\n",
            "    return linux.MSS(**kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/mss/linux.py\", line 288, in __init__\n",
            "    raise ScreenShotError(\"$DISPLAY not set.\") from None\n",
            "mss.exception.ScreenShotError: $DISPLAY not set.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!python detect.py --weights yolov5s.pt --img 640 --conf 0.25 --source data/images\n",
        "#ori"
      ],
      "metadata": {
        "id": "dAuFyey7MQWy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "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_"
      },
      "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": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X58w8JLpMnjH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b454eb21-1905-4e67-f085-cc525e5e6e6e"
      },
      "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/drive/MyDrive/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 🚀 v7.0-224-g6262c7f Python-3.10.12 torch-2.0.1+cu118 CUDA:0 (Tesla T4, 15102MiB)\n",
            "\n",
            "Fusing layers... \n",
            "YOLOv5s summary: 213 layers, 7225885 parameters, 0 gradients\n",
            "\u001b[34m\u001b[1mval: \u001b[0mScanning /content/drive/MyDrive/datasets/coco/val2017... 4952 images, 48 backgrounds, 0 corrupt: 100% 5000/5000 [00:22<00:00, 226.51it/s]\n",
            "\u001b[34m\u001b[1mval: \u001b[0mNew cache created: /content/drive/MyDrive/datasets/coco/val2017.cache\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 157/157 [01:30<00:00,  1.74it/s]\n",
            "                   all       5000      36335      0.671      0.519      0.566      0.371\n",
            "Speed: 0.2ms pre-process, 3.4ms inference, 2.8ms 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=0.44s)\n",
            "creating index...\n",
            "index created!\n",
            "Loading and preparing results...\n",
            "DONE (t=5.41s)\n",
            "creating index...\n",
            "index created!\n",
            "Running per image evaluation...\n",
            "Evaluate annotation type *bbox*\n",
            "DONE (t=73.82s).\n",
            "Accumulating evaluation results...\n",
            "DONE (t=19.30s).\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 ] = 0.722\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://bit.ly/ultralytics_hub\"><img width=\"1000\" src=\"https://github.com/ultralytics/assets/raw/main/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>\n",
        "\n",
        "A **Mosaic Dataloader** is used for training which combines 4 images into 1 mosaic.\n",
        "\n",
        "## Label a dataset on Roboflow (optional)\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."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Select YOLOv5 🚀 logger {run: 'auto'}\n",
        "logger = 'Comet' #@param ['Comet', 'ClearML', 'TensorBoard']\n",
        "\n",
        "if 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()\n",
        "elif logger == 'TensorBoard':\n",
        "  %load_ext tensorboard\n",
        "  %tensorboard --logdir runs/train"
      ],
      "metadata": {
        "id": "i3oKtE4g-aNn",
        "outputId": "28536361-0233-43e8-9dc4-ec948ab5de32",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Please paste your Comet API key from https://www.comet.com/api/my/settings/\n",
            "(api key may not show as you type)\n",
            "Comet API key: ··········\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Valid Comet API Key saved in /content/drive/MyDrive/.comet.config (set COMET_CONFIG to change where it is saved).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1NcFxRcFdJ_O",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c3a55ffc-83f4-4ff6-8ca0-aea387bfb492"
      },
      "source": [
        "# Train YOLOv5s on COCO128 for 3 epochs\n",
        "!python train.py --img 640 --batch 16 --epochs 3 --data thermal_imaging_dataset.yaml --weights yolov5s.pt --cache"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[34m\u001b[1mtrain: \u001b[0mweights=yolov5s.pt, cfg=, data=thermal_imaging_dataset.yaml, hyp=data/hyps/hyp.scratch-low.yaml, epochs=3, 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 🚀 v7.0-224-g6262c7f Python-3.10.12 torch-2.0.1+cu118 CUDA:0 (Tesla T4, 15102MiB)\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[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/train', view at http://localhost:6006/\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Experiment is live on comet.com \u001b[38;5;39mhttps://www.comet.com/fuchstech/yolov5/d303850eb96e4ed9ad9c1a45b06e8f82\u001b[0m\n",
            "\n",
            "Overriding model.yaml nc=80 with nc=4\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     24273  models.yolo.Detect                      [4, [[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, 7030417 parameters, 7030417 gradients, 16.0 GFLOPs\n",
            "\n",
            "Transferred 343/349 items from yolov5s.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/drive/MyDrive/yolov5/data/labels/train... 1000 images, 164 backgrounds, 0 corrupt: 100%|██████████| 1000/1000 [05:42<00:00,  2.92it/s]\n",
            "\u001b[34m\u001b[1mtrain: \u001b[0mNew cache created: /content/drive/MyDrive/yolov5/data/labels/train.cache\n",
            "\u001b[34m\u001b[1mtrain: \u001b[0mCaching images (0.9GB ram): 100%|██████████| 1000/1000 [00:06<00:00, 158.59it/s]\n",
            "\u001b[34m\u001b[1mval: \u001b[0mScanning /content/drive/MyDrive/yolov5/data/labels/val... 772 images, 28 backgrounds, 0 corrupt: 100%|██████████| 772/772 [05:09<00:00,  2.49it/s]\n",
            "\u001b[34m\u001b[1mval: \u001b[0mWARNING ⚠️ /content/drive/MyDrive/yolov5/data/images/val/05639.jpg: 1 duplicate labels removed\n",
            "\u001b[34m\u001b[1mval: \u001b[0mNew cache created: /content/drive/MyDrive/yolov5/data/labels/val.cache\n",
            "\u001b[34m\u001b[1mval: \u001b[0mCaching images (0.7GB ram): 100%|██████████| 772/772 [00:04<00:00, 175.92it/s]\n",
            "\n",
            "\u001b[34m\u001b[1mAutoAnchor: \u001b[0m4.89 anchors/target, 1.000 Best Possible Recall (BPR). Current anchors are a good fit to dataset ✅\n",
            "Plotting labels to runs/train/exp5/labels.jpg... \n",
            "Image sizes 640 train, 640 val\n",
            "Using 2 dataloader workers\n",
            "Logging results to \u001b[1mruns/train/exp5\u001b[0m\n",
            "Starting training for 3 epochs...\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "        0/2      3.48G     0.1047    0.05311    0.03314         75        640: 100%|██████████| 63/63 [00:24<00:00,  2.59it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 25/25 [00:25<00:00,  1.00s/it]\n",
            "                   all        772       7160      0.543     0.0625     0.0382    0.00997\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "        1/2      3.95G    0.08009    0.04344    0.01701         64        640: 100%|██████████| 63/63 [00:17<00:00,  3.60it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 25/25 [00:09<00:00,  2.68it/s]\n",
            "                   all        772       7160      0.696      0.204      0.176     0.0533\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "        2/2      3.95G     0.0694    0.04178    0.01302         81        640: 100%|██████████| 63/63 [00:17<00:00,  3.55it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 25/25 [00:08<00:00,  2.86it/s]\n",
            "                   all        772       7160      0.593      0.245      0.208     0.0725\n",
            "\n",
            "3 epochs completed in 0.030 hours.\n",
            "Optimizer stripped from runs/train/exp5/weights/last.pt, 14.4MB\n",
            "Optimizer stripped from runs/train/exp5/weights/best.pt, 14.4MB\n",
            "\n",
            "Validating runs/train/exp5/weights/best.pt...\n",
            "Fusing layers... \n",
            "Model summary: 157 layers, 7020913 parameters, 0 gradients, 15.8 GFLOPs\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 25/25 [00:24<00:00,  1.03it/s]\n",
            "                   all        772       7160      0.594      0.246      0.208     0.0726\n",
            "               bicycle        772        280      0.606    0.00585     0.0605     0.0148\n",
            "                   car        772       4225      0.346      0.552      0.403      0.148\n",
            "                   dog        772         31          1          0   0.000219   7.61e-05\n",
            "                person        772       2624      0.424      0.427      0.367      0.128\n",
            "Results saved to \u001b[1mruns/train/exp5\u001b[0m\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m ---------------------------------------------------------------------------------------\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Comet.ml Experiment Summary\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m ---------------------------------------------------------------------------------------\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Data:\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     display_summary_level : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     url                   : \u001b[38;5;39mhttps://www.comet.com/fuchstech/yolov5/d303850eb96e4ed9ad9c1a45b06e8f82\u001b[0m\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Metrics [count] (min, max):\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     loss [19]                : (1.8946831226348877, 3.7739689350128174)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/mAP_0.5 [6]      : (0.03819794727698726, 0.20755134862624275)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/mAP_0.5:0.95 [6] : (0.009965802242965706, 0.07254933014177609)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/precision [6]    : (0.5426481315528505, 0.6964223780349028)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/recall [6]       : (0.06254437869822485, 0.24520211962184624)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/box_loss [6]       : (0.0694030299782753, 0.10471654683351517)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/cls_loss [6]       : (0.01301949005573988, 0.03313856199383736)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/obj_loss [6]       : (0.04177547246217728, 0.05310792103409767)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/box_loss [6]         : (0.06619200110435486, 0.09657897800207138)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/cls_loss [6]         : (0.009850403293967247, 0.020301513373851776)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/obj_loss [6]         : (0.04567916691303253, 0.05668624863028526)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     x/lr0 [6]                : (0.003911111111111101, 0.07047619047619047)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     x/lr1 [6]                : (0.0032804232804232807, 0.004431216931216932)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     x/lr2 [6]                : (0.0032804232804232807, 0.004431216931216932)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Others:\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Name                        : exp\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Run Path                    : fuchstech/yolov5/d303850eb96e4ed9ad9c1a45b06e8f82\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_log_batch_metrics     : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_log_confusion_matrix  : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_log_per_class_metrics : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_max_image_uploads     : 100\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_mode                  : online\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_model_name            : yolov5\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hasNestedParams             : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Parameters:\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     anchor_t            : 4.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     artifact_alias      : latest\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     batch_size          : 16\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bbox_interval       : -1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     box                 : 0.05\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bucket              : \n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cfg                 : \n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cls                 : 0.025000000000000005\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cls_pw              : 1.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     copy_paste          : 0.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cos_lr              : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     degrees             : 0.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     device              : \n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     entity              : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     evolve              : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     exist_ok            : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     fl_gamma            : 0.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     fliplr              : 0.5\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     flipud              : 0.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     freeze              : [0]\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hsv_h               : 0.015\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hsv_s               : 0.7\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hsv_v               : 0.4\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|anchor_t        : 4.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|box             : 0.05\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|cls             : 0.5\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|cls_pw          : 1.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|copy_paste      : 0.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|degrees         : 0.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|fl_gamma        : 0.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|fliplr          : 0.5\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|flipud          : 0.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|hsv_h           : 0.015\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|hsv_s           : 0.7\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|hsv_v           : 0.4\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|iou_t           : 0.2\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|lr0             : 0.01\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|lrf             : 0.01\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|mixup           : 0.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|momentum        : 0.937\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|mosaic          : 1.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|obj             : 1.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|obj_pw          : 1.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|perspective     : 0.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|scale           : 0.5\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|shear           : 0.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|translate       : 0.1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|warmup_bias_lr  : 0.1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|warmup_epochs   : 3.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|warmup_momentum : 0.8\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|weight_decay    : 0.0005\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     image_weights       : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     imgsz               : 640\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     iou_t               : 0.2\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     label_smoothing     : 0.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     local_rank          : -1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     lr0                 : 0.01\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     lrf                 : 0.01\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     mixup               : 0.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     momentum            : 0.937\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     mosaic              : 1.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     multi_scale         : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     name                : exp\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     noautoanchor        : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     noplots             : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     nosave              : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     noval               : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     obj                 : 1.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     obj_pw              : 1.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     optimizer           : SGD\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     patience            : 100\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     perspective         : 0.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     project             : runs/train\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     quad                : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     rect                : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     resume              : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     save_dir            : runs/train/exp5\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     save_period         : -1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     scale               : 0.5\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     seed                : 0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     shear               : 0.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     single_cls          : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sync_bn             : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     translate           : 0.1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     upload_dataset      : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val_conf_threshold  : 0.001\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val_iou_threshold   : 0.6\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     warmup_bias_lr      : 0.1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     warmup_epochs       : 3.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     warmup_momentum     : 0.8\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     weight_decay        : 0.0005\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     workers             : 8\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Uploads:\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     asset               : 13 (1.34 MB)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     confusion-matrix    : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     environment details : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     git metadata        : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     images              : 106\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     installed packages  : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     model graph         : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     os packages         : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m \n",
            "\u001b[36m────────────────────────────── \u001b[0m\u001b[1;36mNew Comet feature!\u001b[0m\u001b[36m ──────────────────────────────\u001b[0m\n",
            "Log your models to better track, deploy, share, and reproduce your work using: 'comet_ml.integration.pytorch.log_model'.\n",
            "Learn more at: https://comet.com/docs/v2/pytorch_log_model\n",
            "\n",
            "Hide this message by setting environment variable \"COMET_DISABLE_ANNOUNCEMENT=1\" \n",
            "\u001b[36m────────────────────────────────────────────────────────────────────────────────\u001b[0m\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Please wait for metadata to finish uploading (timeout is 3600 seconds)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "15glLzbQx5u0"
      },
      "source": [
        "# 4. Visualize"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Comet Logging and Visualization 🌟 NEW\n",
        "\n",
        "[Comet](https://www.comet.com/site/lp/yolov5-with-comet/?utm_source=yolov5&utm_medium=partner&utm_campaign=partner_yolov5_2022&utm_content=yolov5_colab) 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://www.comet.com/docs/v2/guides/comet-dashboard/code-panels/about-panels/?utm_source=yolov5&utm_medium=partner&utm_campaign=partner_yolov5_2022&utm_content=yolov5_colab)! 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=hZU5jrptGleypgM0ylrL1kO4m # 2. paste API key\n",
        "python train.py --img 640 --epochs 3 --data coco128.yaml --weights yolov5s.pt  # 3. train\n",
        "```\n",
        "To learn more about all of the supported Comet features for this integration, check out the [Comet Tutorial](https://docs.ultralytics.com/yolov5/tutorials/comet_logging_integration). If you'd like to learn more about Comet, head over to our [documentation](https://www.comet.com/docs/v2/?utm_source=yolov5&utm_medium=partner&utm_campaign=partner_yolov5_2022&utm_content=yolov5_colab). 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",
        "<a href=\"https://bit.ly/yolov5-readme-comet2\">\n",
        "<img alt=\"Comet Dashboard\" src=\"https://user-images.githubusercontent.com/26833433/202851203-164e94e1-2238-46dd-91f8-de020e9d6b41.png\" width=\"1280\"/></a>"
      ],
      "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://docs.ultralytics.com/yolov5/tutorials/clearml_logging_integration) 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://docs.ultralytics.com/yolov5/environments/google_cloud_quickstart_tutorial/)\n",
        "- **Amazon** Deep Learning AMI. See [AWS Quickstart Guide](https://docs.ultralytics.com/yolov5/environments/aws_quickstart_tutorial/)\n",
        "- **Docker Image**. See [Docker Quickstart Guide](https://docs.ultralytics.com/yolov5/environments/docker_image_quickstart_tutorial/) <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"
      },
      "source": [
        "# YOLOv5 PyTorch HUB Inference (DetectionModels only)\n",
        "import torch\n",
        "\n",
        "model = torch.hub.load('ultralytics/yolov5', 'yolov5s', force_reload=True, trust_repo=True)  # or 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": []
    }
  ]
}