tutorial_done.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "YOLOv5 Tutorial",
      "provenance": [],
      "gpuType": "T4"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "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": "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": "00bb7851-0b04-4818-ac62-07ae92e9f205"
      },
      "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": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "YOLOv5 🚀 v7.0-231-gc2f131a Python-3.10.12 torch-2.1.0+cu118 CUDA:0 (Tesla T4, 15102MiB)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Setup complete ✅ (2 CPUs, 12.7 GB RAM, 27.1/78.2 GB disk)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!unzip -q ../train_data.zip -d ../"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JkcYGkgCX8Oy",
        "outputId": "ba66bce7-3a2e-47db-dba9-d8b202e2319d"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "replace ../train_data/images/train/1.jpg? [y]es, [n]o, [A]ll, [N]one, [r]ename: A\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": "d0439075-f102-4637-92ec-59d014ca711b"
      },
      "source": [
        "!python detect.py --weights runs/train/exp2/weights/last.pt --img 640 --conf 0.25 --source 3\n"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[34m\u001b[1mdetect: \u001b[0mweights=['runs/train/exp2/weights/last.pt'], source=3, 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 🚀 v7.0-231-gc2f131a Python-3.10.12 torch-2.1.0+cu118 CUDA:0 (Tesla T4, 15102MiB)\n",
            "\n",
            "Fusing layers... \n",
            "Model summary: 157 layers, 7015519 parameters, 0 gradients, 15.8 GFLOPs\n",
            "WARNING ⚠️ Environment does not support cv2.imshow() or PIL Image.show()\n",
            "\n",
            "[ WARN:0@4.115] global cap_v4l.cpp:982 open VIDEOIO(V4L2:/dev/video3): can't open camera by index\n",
            "[ERROR:0@4.115] global obsensor_uvc_stream_channel.cpp:156 getStreamChannelGroup Camera index out of range\n",
            "Traceback (most recent call last):\n",
            "  File \"/content/yolov5/detect.py\", line 285, in <module>\n",
            "    main(opt)\n",
            "  File \"/content/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/yolov5/detect.py\", line 109, in run\n",
            "    dataset = LoadStreams(source, img_size=imgsz, stride=stride, auto=pt, vid_stride=vid_stride)\n",
            "  File \"/content/yolov5/utils/dataloaders.py\", line 367, in __init__\n",
            "    assert cap.isOpened(), f'{st}Failed to open {s}'\n",
            "AssertionError: 1/1: 3... Failed to open 3\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/"
        },
        "outputId": "cf7d52f0-281c-4c96-a488-79f5908f8426"
      },
      "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": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 780M/780M [00:12<00:00, 66.6MB/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X58w8JLpMnjH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3e234e05-ee8b-4ad1-b1a4-f6a55d5e4f3d"
      },
      "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 🚀 v7.0-136-g71244ae Python-3.9.16 torch-2.0.0+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/datasets/coco/val2017... 4952 images, 48 backgrounds, 0 corrupt: 100% 5000/5000 [00:02<00:00, 2024.59it/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 [01:25<00:00,  1.84it/s]\n",
            "                   all       5000      36335      0.671      0.519      0.566      0.371\n",
            "Speed: 0.1ms pre-process, 3.1ms inference, 2.3ms 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=5.32s)\n",
            "creating index...\n",
            "index created!\n",
            "Running per image evaluation...\n",
            "Evaluate annotation type *bbox*\n",
            "DONE (t=78.89s).\n",
            "Accumulating evaluation results...\n",
            "DONE (t=14.51s).\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"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1NcFxRcFdJ_O",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "10145464-d053-4bff-805a-6c686760a0c8"
      },
      "source": [
        "# Train YOLOv5s on COCO128 for 3 epochs\n",
        "!python train.py --img 640 --batch 16 --epochs 60 --data cstm_data.yaml --weights yolov5s.pt --cache"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2023-10-30 07:04:50.209492: E tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:9342] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
            "2023-10-30 07:04:50.209540: E tensorflow/compiler/xla/stream_executor/cuda/cuda_fft.cc:609] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
            "2023-10-30 07:04:50.209575: E tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:1518] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
            "\u001b[34m\u001b[1mtrain: \u001b[0mweights=yolov5s.pt, cfg=, data=cstm_data.yaml, hyp=data/hyps/hyp.scratch-low.yaml, epochs=60, 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 🚀 v7.0-231-gc2f131a Python-3.10.12 torch-2.1.0+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",
            "COMET WARNING: Comet credentials have not been set. Comet will default to offline logging. Please set your credentials to enable online logging.\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Using '/content/yolov5/.cometml-runs' path as offline directory. Pass 'offline_directory' parameter into constructor or set the 'COMET_OFFLINE_DIRECTORY' environment variable to manually choose where to store offline experiment archives.\n",
            "Overriding model.yaml nc=80 with nc=2\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     18879  models.yolo.Detect                      [2, [[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, 7025023 parameters, 7025023 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/train_data/labels/train.cache... 30 images, 0 backgrounds, 0 corrupt: 100%|██████████| 30/30 [00:00<?, ?it/s]\n",
            "\u001b[34m\u001b[1mtrain: \u001b[0mWARNING ⚠️ /content/train_data/images/train/1.jpg: corrupt JPEG restored and saved\n",
            "\u001b[34m\u001b[1mtrain: \u001b[0mWARNING ⚠️ /content/train_data/images/train/10.jpg: corrupt JPEG restored and saved\n",
            "\u001b[34m\u001b[1mtrain: \u001b[0mWARNING ⚠️ /content/train_data/images/train/11.jpg: corrupt JPEG restored and saved\n",
            "\u001b[34m\u001b[1mtrain: \u001b[0mWARNING ⚠️ /content/train_data/images/train/12.jpg: corrupt JPEG restored and saved\n",
            "\u001b[34m\u001b[1mtrain: \u001b[0mWARNING ⚠️ /content/train_data/images/train/13.jpg: corrupt JPEG restored and saved\n",
            "\u001b[34m\u001b[1mtrain: \u001b[0mWARNING ⚠️ /content/train_data/images/train/14.jpg: corrupt JPEG restored and saved\n",
            "\u001b[34m\u001b[1mtrain: \u001b[0mWARNING ⚠️ /content/train_data/images/train/15.jpg: corrupt JPEG restored and saved\n",
            "\u001b[34m\u001b[1mtrain: \u001b[0mWARNING ⚠️ /content/train_data/images/train/16.jpg: corrupt JPEG restored and saved\n",
            "\u001b[34m\u001b[1mtrain: \u001b[0mWARNING ⚠️ /content/train_data/images/train/17.jpg: corrupt JPEG restored and saved\n",
            "\u001b[34m\u001b[1mtrain: \u001b[0mWARNING ⚠️ /content/train_data/images/train/18.jpg: corrupt JPEG restored and saved\n",
            "\u001b[34m\u001b[1mtrain: \u001b[0mWARNING ⚠️ /content/train_data/images/train/19.jpg: corrupt JPEG restored and saved\n",
            "\u001b[34m\u001b[1mtrain: \u001b[0mWARNING ⚠️ /content/train_data/images/train/2.jpg: corrupt JPEG restored and saved\n",
            "\u001b[34m\u001b[1mtrain: \u001b[0mWARNING ⚠️ /content/train_data/images/train/20.jpg: corrupt JPEG restored and saved\n",
            "\u001b[34m\u001b[1mtrain: \u001b[0mWARNING ⚠️ /content/train_data/images/train/21.jpg: corrupt JPEG restored and saved\n",
            "\u001b[34m\u001b[1mtrain: \u001b[0mWARNING ⚠️ /content/train_data/images/train/22.jpg: corrupt JPEG restored and saved\n",
            "\u001b[34m\u001b[1mtrain: \u001b[0mWARNING ⚠️ /content/train_data/images/train/23.jpg: corrupt JPEG restored and saved\n",
            "\u001b[34m\u001b[1mtrain: \u001b[0mWARNING ⚠️ /content/train_data/images/train/24.jpg: corrupt JPEG restored and saved\n",
            "\u001b[34m\u001b[1mtrain: \u001b[0mWARNING ⚠️ /content/train_data/images/train/25.jpg: corrupt JPEG restored and saved\n",
            "\u001b[34m\u001b[1mtrain: \u001b[0mWARNING ⚠️ /content/train_data/images/train/26.jpg: corrupt JPEG restored and saved\n",
            "\u001b[34m\u001b[1mtrain: \u001b[0mWARNING ⚠️ /content/train_data/images/train/27.jpg: corrupt JPEG restored and saved\n",
            "\u001b[34m\u001b[1mtrain: \u001b[0mWARNING ⚠️ /content/train_data/images/train/28.jpg: corrupt JPEG restored and saved\n",
            "\u001b[34m\u001b[1mtrain: \u001b[0mWARNING ⚠️ /content/train_data/images/train/29.jpg: corrupt JPEG restored and saved\n",
            "\u001b[34m\u001b[1mtrain: \u001b[0mWARNING ⚠️ /content/train_data/images/train/3.jpg: corrupt JPEG restored and saved\n",
            "\u001b[34m\u001b[1mtrain: \u001b[0mWARNING ⚠️ /content/train_data/images/train/30.jpg: corrupt JPEG restored and saved\n",
            "\u001b[34m\u001b[1mtrain: \u001b[0mWARNING ⚠️ /content/train_data/images/train/4.jpg: corrupt JPEG restored and saved\n",
            "\u001b[34m\u001b[1mtrain: \u001b[0mWARNING ⚠️ /content/train_data/images/train/5.jpg: corrupt JPEG restored and saved\n",
            "\u001b[34m\u001b[1mtrain: \u001b[0mWARNING ⚠️ /content/train_data/images/train/6.jpg: corrupt JPEG restored and saved\n",
            "\u001b[34m\u001b[1mtrain: \u001b[0mWARNING ⚠️ /content/train_data/images/train/7.jpg: corrupt JPEG restored and saved\n",
            "\u001b[34m\u001b[1mtrain: \u001b[0mWARNING ⚠️ /content/train_data/images/train/8.jpg: corrupt JPEG restored and saved\n",
            "\u001b[34m\u001b[1mtrain: \u001b[0mWARNING ⚠️ /content/train_data/images/train/9.jpg: corrupt JPEG restored and saved\n",
            "\u001b[34m\u001b[1mtrain: \u001b[0mCaching images (0.0GB ram): 100%|██████████| 30/30 [00:05<00:00,  5.13it/s]\n",
            "\u001b[34m\u001b[1mval: \u001b[0mScanning /content/train_data/labels/val.cache... 7 images, 0 backgrounds, 0 corrupt: 100%|██████████| 7/7 [00:00<?, ?it/s]\n",
            "\u001b[34m\u001b[1mval: \u001b[0mWARNING ⚠️ /content/train_data/images/val/1.jpg: corrupt JPEG restored and saved\n",
            "\u001b[34m\u001b[1mval: \u001b[0mWARNING ⚠️ /content/train_data/images/val/2.jpg: corrupt JPEG restored and saved\n",
            "\u001b[34m\u001b[1mval: \u001b[0mWARNING ⚠️ /content/train_data/images/val/3.jpg: corrupt JPEG restored and saved\n",
            "\u001b[34m\u001b[1mval: \u001b[0mWARNING ⚠️ /content/train_data/images/val/4.jpg: corrupt JPEG restored and saved\n",
            "\u001b[34m\u001b[1mval: \u001b[0mWARNING ⚠️ /content/train_data/images/val/5.jpg: corrupt JPEG restored and saved\n",
            "\u001b[34m\u001b[1mval: \u001b[0mWARNING ⚠️ /content/train_data/images/val/6.jpg: corrupt JPEG restored and saved\n",
            "\u001b[34m\u001b[1mval: \u001b[0mWARNING ⚠️ /content/train_data/images/val/7.jpg: corrupt JPEG restored and saved\n",
            "\u001b[34m\u001b[1mval: \u001b[0mCaching images (0.0GB ram): 100%|██████████| 7/7 [00:01<00:00,  5.14it/s]\n",
            "\n",
            "\u001b[34m\u001b[1mAutoAnchor: \u001b[0m4.45 anchors/target, 1.000 Best Possible Recall (BPR). Current anchors are a good fit to dataset ✅\n",
            "Plotting labels to runs/train/exp2/labels.jpg... \n",
            "Image sizes 640 train, 640 val\n",
            "Using 2 dataloader workers\n",
            "Logging results to \u001b[1mruns/train/exp2\u001b[0m\n",
            "Starting training for 60 epochs...\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "       0/59      3.46G     0.1204    0.03494    0.02906         44        640: 100%|██████████| 2/2 [00:04<00:00,  2.35s/it]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<00:00,  1.16it/s]\n",
            "                   all          7         10    0.00291        0.6    0.00377   0.000847\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "       1/59      3.46G     0.1179    0.03536    0.03097         51        640: 100%|██████████| 2/2 [00:00<00:00,  3.65it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<00:00,  1.48it/s]\n",
            "                   all          7         10    0.00291        0.6    0.00385    0.00091\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "       2/59      3.46G     0.1182    0.03499    0.02963         47        640: 100%|██████████| 2/2 [00:00<00:00,  4.28it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<00:00,  2.36it/s]\n",
            "                   all          7         10    0.00232        0.4    0.00378    0.00086\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "       3/59      3.46G     0.1145    0.03455    0.02898         51        640: 100%|██████████| 2/2 [00:00<00:00,  4.55it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<00:00,  2.76it/s]\n",
            "                   all          7         10    0.00249        0.5    0.00251   0.000723\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "       4/59      3.46G     0.1066    0.03407    0.02926         41        640: 100%|██████████| 2/2 [00:00<00:00,  4.60it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<00:00,  2.81it/s]\n",
            "                   all          7         10    0.00249        0.5     0.0027    0.00062\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "       5/59      3.46G     0.1014    0.03417    0.02701         50        640: 100%|██████████| 2/2 [00:00<00:00,  4.21it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<00:00,  3.14it/s]\n",
            "                   all          7         10    0.00292        0.6    0.00329    0.00122\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "       6/59      3.46G    0.09829    0.04164    0.02685         61        640: 100%|██████████| 2/2 [00:00<00:00,  4.61it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<00:00,  3.46it/s]\n",
            "                   all          7         10    0.00278        0.6    0.00525    0.00134\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "       7/59      3.46G    0.09163    0.03894    0.02546         49        640: 100%|██████████| 2/2 [00:00<00:00,  3.83it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<00:00,  2.27it/s]\n",
            "                   all          7         10    0.00236        0.5    0.00564    0.00206\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "       8/59      3.46G    0.08896    0.03464    0.02412         43        640: 100%|██████████| 2/2 [00:00<00:00,  3.32it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<00:00,  3.48it/s]\n",
            "                   all          7         10    0.00278        0.6    0.00853    0.00312\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "       9/59      3.46G    0.08773     0.0394    0.02276         50        640: 100%|██████████| 2/2 [00:00<00:00,  5.02it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<00:00,  4.86it/s]\n",
            "                   all          7         10    0.00318        0.7     0.0133      0.004\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      10/59      3.46G    0.08108    0.03388    0.02228         39        640: 100%|██████████| 2/2 [00:00<00:00,  3.94it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<00:00,  5.30it/s]\n",
            "                   all          7         10    0.00318        0.7     0.0153    0.00519\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      11/59      3.46G    0.08527    0.03591    0.02213         42        640: 100%|██████████| 2/2 [00:00<00:00,  4.94it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<00:00,  5.34it/s]\n",
            "                   all          7         10    0.00355        0.7     0.0161    0.00682\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      12/59      3.46G    0.08466    0.03361    0.02163         44        640: 100%|██████████| 2/2 [00:00<00:00,  4.68it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<00:00,  5.67it/s]\n",
            "                   all          7         10    0.00522          1     0.0254    0.00844\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      13/59      3.46G    0.08261    0.03779    0.02009         42        640: 100%|██████████| 2/2 [00:00<00:00,  5.06it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<00:00,  5.64it/s]\n",
            "                   all          7         10    0.00428        0.8     0.0171    0.00707\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      14/59      3.46G    0.07813    0.03725    0.01916         39        640: 100%|██████████| 2/2 [00:00<00:00,  4.90it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<00:00,  5.49it/s]\n",
            "                   all          7         10    0.00359        0.7     0.0153    0.00747\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      15/59      3.46G    0.07401     0.0318    0.01832         44        640: 100%|██████████| 2/2 [00:00<00:00,  4.76it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<00:00,  5.70it/s]\n",
            "                   all          7         10     0.0227        0.4     0.0254    0.00922\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      16/59      3.46G    0.07739    0.04396    0.01888         56        640: 100%|██████████| 2/2 [00:00<00:00,  4.95it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<00:00,  5.69it/s]\n",
            "                   all          7         10    0.00397        0.8     0.0246     0.0114\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      17/59      3.46G    0.07548    0.03644    0.01761         47        640: 100%|██████████| 2/2 [00:00<00:00,  5.08it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<00:00,  5.46it/s]\n",
            "                   all          7         10     0.0038        0.8     0.0426     0.0204\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      18/59      3.46G    0.07214    0.04064    0.01627         55        640: 100%|██████████| 2/2 [00:00<00:00,  5.03it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<00:00,  4.16it/s]\n",
            "                   all          7         10    0.00529        0.8     0.0454     0.0247\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      19/59      3.46G     0.0686    0.04006    0.01557         63        640: 100%|██████████| 2/2 [00:00<00:00,  3.55it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<00:00,  3.31it/s]\n",
            "                   all          7         10     0.0249        0.8     0.0652     0.0327\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      20/59      3.46G    0.06948    0.03563    0.01546         45        640: 100%|██████████| 2/2 [00:00<00:00,  3.63it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<00:00,  4.30it/s]\n",
            "                   all          7         10      0.052        0.7     0.0847     0.0467\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      21/59      3.46G      0.068    0.03183    0.01566         41        640: 100%|██████████| 2/2 [00:00<00:00,  4.85it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<00:00,  6.03it/s]\n",
            "                   all          7         10     0.0809        0.4      0.133     0.0733\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      22/59      3.46G    0.06248    0.03012    0.01435         42        640: 100%|██████████| 2/2 [00:00<00:00,  4.98it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<00:00,  6.58it/s]\n",
            "                   all          7         10      0.102        0.4      0.213      0.112\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      23/59      3.46G     0.0637     0.0324    0.01235         48        640: 100%|██████████| 2/2 [00:00<00:00,  5.11it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<00:00,  6.69it/s]\n",
            "                   all          7         10      0.669        0.2       0.26      0.145\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      24/59      3.46G    0.06513    0.03086    0.01255         40        640: 100%|██████████| 2/2 [00:00<00:00,  4.81it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<00:00,  7.23it/s]\n",
            "                   all          7         10      0.874        0.3      0.294       0.16\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      25/59      3.46G    0.06247    0.02952    0.01238         44        640: 100%|██████████| 2/2 [00:00<00:00,  4.77it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<00:00,  7.84it/s]\n",
            "                   all          7         10      0.231        0.5      0.419      0.203\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      26/59      3.46G    0.06307    0.03192     0.0118         54        640: 100%|██████████| 2/2 [00:00<00:00,  4.35it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<00:00,  7.82it/s]\n",
            "                   all          7         10      0.264        0.5       0.43      0.231\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      27/59      3.46G    0.06443    0.03057    0.01214         45        640: 100%|██████████| 2/2 [00:00<00:00,  5.33it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<00:00,  5.73it/s]\n",
            "                   all          7         10      0.264        0.5       0.43      0.231\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      28/59      3.46G    0.06248    0.03227    0.01122         49        640: 100%|██████████| 2/2 [00:00<00:00,  3.58it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<00:00,  2.97it/s]\n",
            "                   all          7         10      0.636      0.584      0.549      0.305\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      29/59      3.46G    0.06115     0.0337    0.01133         47        640: 100%|██████████| 2/2 [00:00<00:00,  3.63it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<00:00,  3.24it/s]\n",
            "                   all          7         10      0.629      0.652      0.588      0.282\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      30/59      3.46G    0.06206    0.03285   0.009711         42        640: 100%|██████████| 2/2 [00:00<00:00,  4.61it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<00:00,  6.11it/s]\n",
            "                   all          7         10      0.629      0.652      0.588      0.282\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      31/59      3.46G    0.06099    0.02603   0.009719         35        640: 100%|██████████| 2/2 [00:00<00:00,  4.88it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<00:00,  7.27it/s]\n",
            "                   all          7         10      0.513        0.6      0.532      0.276\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      32/59      3.46G    0.05763    0.02779   0.009869         42        640: 100%|██████████| 2/2 [00:00<00:00,  4.78it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<00:00,  8.44it/s]\n",
            "                   all          7         10      0.639        0.7       0.61      0.289\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      33/59      3.46G    0.05805    0.03029   0.008845         47        640: 100%|██████████| 2/2 [00:00<00:00,  4.81it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<00:00,  8.09it/s]\n",
            "                   all          7         10      0.639        0.7       0.61      0.289\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      34/59      3.46G    0.06069    0.02804   0.009352         44        640: 100%|██████████| 2/2 [00:00<00:00,  5.12it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<00:00,  6.35it/s]\n",
            "                   all          7         10      0.308        0.5      0.359      0.132\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      35/59      3.46G    0.06038    0.03167   0.008879         55        640: 100%|██████████| 2/2 [00:00<00:00,  4.79it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<00:00,  8.06it/s]\n",
            "                   all          7         10      0.465        0.6      0.566      0.261\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      36/59      3.46G    0.06228    0.02484    0.00905         42        640: 100%|██████████| 2/2 [00:00<00:00,  4.84it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<00:00,  6.89it/s]\n",
            "                   all          7         10      0.465        0.6      0.566      0.261\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      37/59      3.46G     0.0523    0.02722   0.007077         43        640: 100%|██████████| 2/2 [00:00<00:00,  4.79it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<00:00,  8.31it/s]\n",
            "                   all          7         10       0.58      0.675      0.568      0.339\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      38/59      3.46G    0.05935    0.02821   0.007697         42        640: 100%|██████████| 2/2 [00:00<00:00,  4.84it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<00:00,  7.91it/s]\n",
            "                   all          7         10      0.623      0.695      0.648      0.304\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      39/59      3.46G    0.05839    0.02667   0.007468         48        640: 100%|██████████| 2/2 [00:00<00:00,  4.90it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<00:00,  8.69it/s]\n",
            "                   all          7         10      0.623      0.695      0.648      0.304\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      40/59      3.46G    0.05793    0.02479   0.007841         41        640: 100%|██████████| 2/2 [00:00<00:00,  4.84it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<00:00,  5.74it/s]\n",
            "                   all          7         10      0.579        0.7      0.662      0.275\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      41/59      3.46G    0.06305    0.02564   0.007656         47        640: 100%|██████████| 2/2 [00:00<00:00,  3.60it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<00:00,  4.08it/s]\n",
            "                   all          7         10      0.685      0.644      0.772      0.365\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      42/59      3.46G    0.05494    0.02521   0.006673         39        640: 100%|██████████| 2/2 [00:00<00:00,  3.67it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<00:00,  5.71it/s]\n",
            "                   all          7         10      0.685      0.644      0.772      0.365\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      43/59      3.46G    0.05544    0.02464   0.007388         39        640: 100%|██████████| 2/2 [00:00<00:00,  4.65it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<00:00,  9.08it/s]\n",
            "                   all          7         10      0.623        0.7      0.619      0.374\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      44/59      3.46G    0.06212    0.02591   0.008276         46        640: 100%|██████████| 2/2 [00:00<00:00,  5.35it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<00:00,  7.93it/s]\n",
            "                   all          7         10      0.623        0.7      0.619      0.374\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      45/59      3.46G    0.05841    0.02986   0.006958         58        640: 100%|██████████| 2/2 [00:00<00:00,  4.64it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<00:00, 10.44it/s]\n",
            "                   all          7         10      0.758      0.676      0.721      0.332\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      46/59      3.46G    0.05272    0.02677   0.005696         50        640: 100%|██████████| 2/2 [00:00<00:00,  4.89it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<00:00,  8.42it/s]\n",
            "                   all          7         10      0.758      0.676      0.721      0.332\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      47/59      3.46G    0.05013    0.02264    0.00579         39        640: 100%|██████████| 2/2 [00:00<00:00,  4.68it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<00:00,  6.80it/s]\n",
            "                   all          7         10      0.667      0.695      0.652      0.397\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      48/59      3.46G    0.05247    0.02296   0.005655         44        640: 100%|██████████| 2/2 [00:00<00:00,  4.85it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<00:00,  8.47it/s]\n",
            "                   all          7         10      0.667      0.695      0.652      0.397\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      49/59      3.46G    0.05195    0.02706   0.005943         46        640: 100%|██████████| 2/2 [00:00<00:00,  4.46it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<00:00,  9.14it/s]\n",
            "                   all          7         10      0.842      0.666      0.812       0.43\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      50/59      3.46G    0.05448    0.02705   0.006366         55        640: 100%|██████████| 2/2 [00:00<00:00,  5.46it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<00:00,  8.47it/s]\n",
            "                   all          7         10      0.842      0.666      0.812       0.43\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      51/59      3.46G    0.05052    0.02423   0.004871         47        640: 100%|██████████| 2/2 [00:00<00:00,  3.79it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<00:00,  8.45it/s]\n",
            "                   all          7         10      0.643      0.725      0.848      0.513\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      52/59      3.46G    0.04891    0.02454   0.004623         44        640: 100%|██████████| 2/2 [00:00<00:00,  4.25it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<00:00,  3.25it/s]\n",
            "                   all          7         10      0.643      0.725      0.848      0.513\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      53/59      3.46G    0.04998    0.02411   0.005421         43        640: 100%|██████████| 2/2 [00:00<00:00,  3.58it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<00:00,  4.62it/s]\n",
            "                   all          7         10      0.616      0.723      0.822      0.387\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      54/59      3.46G    0.04716    0.02527   0.005483         49        640: 100%|██████████| 2/2 [00:00<00:00,  5.17it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<00:00,  7.73it/s]\n",
            "                   all          7         10      0.616      0.723      0.822      0.387\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      55/59      3.46G    0.04685    0.02331   0.006259         53        640: 100%|██████████| 2/2 [00:00<00:00,  4.60it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<00:00, 10.69it/s]\n",
            "                   all          7         10      0.865      0.743      0.913      0.503\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      56/59      3.46G    0.04245    0.02151   0.004528         34        640: 100%|██████████| 2/2 [00:00<00:00,  5.23it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<00:00,  8.59it/s]\n",
            "                   all          7         10      0.865      0.743      0.913      0.503\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      57/59      3.46G    0.04868    0.02696   0.006284         47        640: 100%|██████████| 2/2 [00:00<00:00,  4.69it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<00:00,  9.08it/s]\n",
            "                   all          7         10      0.947      0.679        0.9      0.495\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      58/59      3.46G    0.04779    0.02544   0.005181         46        640: 100%|██████████| 2/2 [00:00<00:00,  5.29it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<00:00,  7.39it/s]\n",
            "                   all          7         10      0.947      0.679        0.9      0.495\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      59/59      3.46G    0.04113    0.02206   0.003579         46        640: 100%|██████████| 2/2 [00:00<00:00,  4.46it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<00:00, 10.47it/s]\n",
            "                   all          7         10      0.846       0.69      0.849      0.461\n",
            "\n",
            "60 epochs completed in 0.022 hours.\n",
            "Optimizer stripped from runs/train/exp2/weights/last.pt, 14.4MB\n",
            "Optimizer stripped from runs/train/exp2/weights/best.pt, 14.4MB\n",
            "\n",
            "Validating runs/train/exp2/weights/best.pt...\n",
            "Fusing layers... \n",
            "Model summary: 157 layers, 7015519 parameters, 0 gradients, 15.8 GFLOPs\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 1/1 [00:00<00:00, 10.28it/s]\n",
            "                   all          7         10       0.61      0.727      0.841       0.51\n",
            "                 robot          7          5      0.763      0.653      0.928      0.596\n",
            "                  desk          7          5      0.457        0.8      0.754      0.425\n",
            "Results saved to \u001b[1mruns/train/exp2\u001b[0m\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m ---------------------------------------------------------------------------------------\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Comet.ml OfflineExperiment 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                   : [OfflineExperiment will get URL after upload]\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Metrics [count] (min, max):\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     loss [6]                   : (1.386382818222046, 2.9320294857025146)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/mAP_0.5 [120]      : (0.0025114237481933914, 0.9134615384615383)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/mAP_0.5:0.95 [120] : (0.0006198781099277307, 0.5132050076591532)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/precision [120]    : (0.002319347920406312, 0.9466265330751312)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/recall [120]       : (0.2, 1.0)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/box_loss [120]       : (0.04112865403294563, 0.12041831016540527)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/cls_loss [120]       : (0.0035786775406450033, 0.030973680317401886)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/obj_loss [120]       : (0.02150759845972061, 0.04396040365099907)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/box_loss [120]         : (0.020189329981803894, 0.08165568113327026)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/cls_loss [120]         : (0.0024116516578942537, 0.02752075158059597)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/obj_loss [120]         : (0.01072835922241211, 0.02721405029296875)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     x/lr0 [120]                : (0.00043, 0.09910000000000001)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     x/lr1 [120]                : (0.0001, 0.0030805)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     x/lr2 [120]                : (0.0001, 0.0030805)\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     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     offline_experiment          : 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                 : 0.012500000000000002\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/exp2\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 (1.18 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              : 6\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[1;38;5;39mCOMET INFO:\u001b[0m Still saving offline stats to messages file before program termination (may take up to 120 seconds)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Starting saving the offline archive\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m To upload this offline experiment, run:\n",
            "    comet upload /content/yolov5/.cometml-runs/a98a7d77f50e4bfe9d21b657c5f4cd40.zip\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=<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",
        "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": []
    }
  ]
}