yolo object detection.ipynb

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      "name": "Yolo for Object Detection.ipynb",
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  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "U2GwbpVM4XB9"
      },
      "source": [
        "!pip install wandb"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m4AFA88hKPfx"
      },
      "source": [
        "import wandb\n",
        "# import tensorflow as tf"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sqKAXHiuL1iB",
        "outputId": "8e7db56e-dcb2-4df0-d554-816bf4e212ad"
      },
      "source": [
        "cd /content/drive/MyDrive/Yolov5ObjectDetection"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/drive/MyDrive/Yolov5ObjectDetection\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!git clone https://github.com/ultralytics/yolov5.git"
      ],
      "metadata": {
        "id": "w8R5Ag3BmCl9"
      },
      "execution_count": null,
      "outputs": []
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          "height": 145
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        "id": "x_SmpyYxK5le",
        "outputId": "6a814987-1d10-46af-af64-7cdf0e9412e9"
      },
      "source": [
        "wandb.init(project='yolov5objectdetection', entity='nahid-ebrahimian')"
      ],
      "execution_count": null,
      "outputs": [
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            "\u001b[34m\u001b[1mwandb\u001b[0m: You can find your API key in your browser here: https://wandb.ai/authorize\n"
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            "\u001b[34m\u001b[1mwandb\u001b[0m: Appending key for api.wandb.ai to your netrc file: /root/.netrc\n"
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              "\n",
              "                    Syncing run <strong><a href=\"https://wandb.ai/nahid-ebrahimian/yolov5objectdetection/runs/10wckdur\" target=\"_blank\">eternal-pine-30</a></strong> to <a href=\"https://wandb.ai/nahid-ebrahimian/yolov5objectdetection\" target=\"_blank\">Weights & Biases</a> (<a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">docs</a>).<br/>\n",
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              "<button onClick=\"this.nextSibling.style.display='block';this.style.display='none';\">Display W&B run</button><iframe src=\"https://wandb.ai/nahid-ebrahimian/yolov5objectdetection/runs/10wckdur?jupyter=true\" style=\"border:none;width:100%;height:420px;display:none;\"></iframe>"
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    {
      "cell_type": "code",
      "metadata": {
        "id": "OAiw8chk3mV_"
      },
      "source": [
        "# !git clone https://github.com/ultralytics/yolov5  # clone repo\n",
        "!pip install -U -r yolov5/requirements.txt  # install dependencies"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eWlrIlZa10My",
        "outputId": "b76ffb03-d789-45a4-96f4-c994351413fe"
      },
      "source": [
        "# !cd /content/drive/MyDrive/YoloObjectDetection/yolov5/train.py\n",
        "!python yolov5/train.py --img 640 --batch 8 --epochs 30 --data coco128.yaml --weights yolov5s.pt"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading https://ultralytics.com/assets/Arial.ttf to /root/.config/Ultralytics/Arial.ttf...\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mnahid-ebrahimian\u001b[0m (use `wandb login --relogin` to force relogin)\n",
            "\u001b[34m\u001b[1mtrain: \u001b[0mweights=yolov5s.pt, cfg=, data=coco128.yaml, hyp=yolov5/data/hyps/hyp.scratch.yaml, epochs=30, batch_size=8, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, evolve=None, bucket=, cache=None, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=yolov5/runs/train, name=exp, exist_ok=False, quad=False, linear_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=-1, artifact_alias=latest\n",
            "\u001b[34m\u001b[1mgithub: \u001b[0mup to date with https://github.com/ultralytics/yolov5 ✅\n",
            "YOLOv5 🚀 2022-1-12 torch 1.10.1+cu102 CUDA:0 (Tesla K80, 11441MiB)\n",
            "\n",
            "\u001b[34m\u001b[1mhyperparameters: \u001b[0mlr0=0.01, lrf=0.1, 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 yolov5/runs/train', view at http://localhost:6006/\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Tracking run with wandb version 0.12.9\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Syncing run \u001b[33mfine-galaxy-1\u001b[0m\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: ⭐️ View project at \u001b[34m\u001b[4mhttps://wandb.ai/nahid-ebrahimian/train\u001b[0m\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: 🚀 View run at \u001b[34m\u001b[4mhttps://wandb.ai/nahid-ebrahimian/train/runs/2eshco7i\u001b[0m\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Run data is saved locally in /content/drive/My Drive/Yolov5ObjectDetection/wandb/run-20220112_044947-2eshco7i\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Run `wandb offline` to turn off syncing.\n",
            "\n",
            "Overriding model.yaml nc=80 with nc=5\n",
            "\n",
            "                 from  n    params  module                                  arguments                     \n",
            "  0                -1  1      3520  models.common.Focus                     [3, 32, 3]                    \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  3    156928  models.common.C3                        [128, 128, 3]                 \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    656896  models.common.SPP                       [512, 512, [5, 9, 13]]        \n",
            "  9                -1  1   1182720  models.common.C3                        [512, 512, 1, False]          \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     26970  models.yolo.Detect                      [5, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]\n",
            "Model Summary: 283 layers, 7074330 parameters, 7074330 gradients, 16.4 GFLOPs\n",
            "\n",
            "Transferred 355/361 items from yolov5s.pt\n",
            "Scaled weight_decay = 0.0005\n",
            "\u001b[34m\u001b[1moptimizer:\u001b[0m SGD with parameter groups 59 weight, 62 weight (no decay), 62 bias\n",
            "\u001b[34m\u001b[1malbumentations: \u001b[0mversion 1.0.3 required by YOLOv5, but version 0.1.12 is currently installed\n",
            "\u001b[34m\u001b[1mtrain: \u001b[0mScanning '/content/drive/MyDrive/Yolov5ObjectDetection/Dataset/Train/labels' images and labels...1600 found, 10 missing, 0 empty, 0 corrupted: 100% 1610/1610 [07:16<00:00,  3.69it/s]\n",
            "\u001b[34m\u001b[1mtrain: \u001b[0mNew cache created: /content/drive/MyDrive/Yolov5ObjectDetection/Dataset/Train/labels.cache\n",
            "\u001b[34m\u001b[1mval: \u001b[0mScanning '/content/drive/MyDrive/Yolov5ObjectDetection/Dataset/Val/labels' images and labels...500 found, 0 missing, 0 empty, 0 corrupted: 100% 500/500 [02:12<00:00,  3.78it/s]\n",
            "\u001b[34m\u001b[1mval: \u001b[0mNew cache created: /content/drive/MyDrive/Yolov5ObjectDetection/Dataset/Val/labels.cache\n",
            "Plotting labels to yolov5/runs/train/exp/labels.jpg... \n",
            "\n",
            "\u001b[34m\u001b[1mAutoAnchor: \u001b[0m2.26 anchors/target, 1.000 Best Possible Recall (BPR). Current anchors are a good fit to dataset ✅\n",
            "Image sizes 640 train, 640 val\n",
            "Using 2 dataloader workers\n",
            "Logging results to \u001b[1myolov5/runs/train/exp\u001b[0m\n",
            "Starting training for 30 epochs...\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "      0/29     1.78G   0.05449   0.02669   0.03741         3       640: 100% 202/202 [02:03<00:00,  1.64it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 32/32 [00:10<00:00,  3.13it/s]\n",
            "                 all        500        509      0.187      0.755      0.226       0.14\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "      1/29     3.21G   0.03369   0.01619   0.03447         6       640: 100% 202/202 [01:59<00:00,  1.70it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 32/32 [00:10<00:00,  3.09it/s]\n",
            "                 all        500        509      0.187      0.667      0.209      0.133\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "      2/29     3.21G   0.02922   0.01344    0.0331         4       640: 100% 202/202 [01:57<00:00,  1.72it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 32/32 [00:10<00:00,  2.92it/s]\n",
            "                 all        500        509      0.208       0.85      0.232      0.161\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "      3/29     3.21G   0.02836   0.01247    0.0347         4       640: 100% 202/202 [01:56<00:00,  1.73it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 32/32 [00:10<00:00,  3.13it/s]\n",
            "                 all        500        509      0.195      0.888      0.214      0.153\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "      4/29     3.21G   0.02616   0.01201   0.03331         6       640: 100% 202/202 [01:56<00:00,  1.73it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 32/32 [00:09<00:00,  3.22it/s]\n",
            "                 all        500        509      0.202      0.878      0.232      0.137\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "      5/29     3.21G   0.02344   0.01168   0.03395         8       640: 100% 202/202 [01:56<00:00,  1.73it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 32/32 [00:11<00:00,  2.70it/s]\n",
            "                 all        500        509      0.211      0.815      0.241      0.194\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "      6/29     3.21G   0.02209   0.01082   0.03409         3       640: 100% 202/202 [01:57<00:00,  1.72it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 32/32 [00:10<00:00,  3.17it/s]\n",
            "                 all        500        509      0.221      0.797      0.302      0.238\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "      7/29     3.21G    0.0201    0.0107    0.0329         8       640: 100% 202/202 [01:57<00:00,  1.71it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 32/32 [00:09<00:00,  3.22it/s]\n",
            "                 all        500        509      0.316       0.67      0.402       0.31\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "      8/29     3.21G   0.01972   0.01019   0.03086         7       640: 100% 202/202 [01:56<00:00,  1.73it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 32/32 [00:11<00:00,  2.80it/s]\n",
            "                 all        500        509      0.377      0.783      0.532      0.425\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "      9/29     3.21G   0.01845   0.01003   0.02954         8       640: 100% 202/202 [01:57<00:00,  1.71it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 32/32 [00:10<00:00,  3.10it/s]\n",
            "                 all        500        509      0.452      0.712      0.559      0.471\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     10/29     3.21G   0.01773  0.009836   0.02822         7       640: 100% 202/202 [01:58<00:00,  1.71it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 32/32 [00:10<00:00,  3.09it/s]\n",
            "                 all        500        509      0.516      0.771      0.608      0.496\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     11/29     3.21G   0.01748  0.009695   0.02693         7       640: 100% 202/202 [01:57<00:00,  1.72it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 32/32 [00:11<00:00,  2.75it/s]\n",
            "                 all        500        509      0.532      0.737      0.665      0.558\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     12/29     3.21G   0.01558  0.009676   0.02579         5       640: 100% 202/202 [01:58<00:00,  1.71it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 32/32 [00:10<00:00,  3.06it/s]\n",
            "                 all        500        509      0.648      0.794       0.74        0.6\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     13/29     3.21G   0.01756  0.009424   0.02538         5       640: 100% 202/202 [01:57<00:00,  1.72it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 32/32 [00:10<00:00,  3.12it/s]\n",
            "                 all        500        509      0.579      0.813       0.75       0.63\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     14/29     3.21G   0.01584  0.009449   0.02363         4       640: 100% 202/202 [01:57<00:00,  1.72it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 32/32 [00:11<00:00,  2.72it/s]\n",
            "                 all        500        509      0.676      0.768      0.807      0.675\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     15/29     3.21G   0.01561  0.009288    0.0223         6       640: 100% 202/202 [01:58<00:00,  1.71it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 32/32 [00:10<00:00,  3.10it/s]\n",
            "                 all        500        509      0.762      0.874      0.874       0.74\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     16/29     3.21G   0.01684  0.009363   0.02082         6       640: 100% 202/202 [01:57<00:00,  1.72it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 32/32 [00:10<00:00,  3.14it/s]\n",
            "                 all        500        509      0.853       0.85      0.924      0.749\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     17/29     3.21G   0.01547  0.009333   0.01855         6       640: 100% 202/202 [01:57<00:00,  1.72it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 32/32 [00:11<00:00,  2.79it/s]\n",
            "                 all        500        509      0.852      0.872      0.937      0.784\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     18/29     3.21G    0.0146   0.00911   0.01814         8       640: 100% 202/202 [01:57<00:00,  1.71it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 32/32 [00:10<00:00,  3.13it/s]\n",
            "                 all        500        509      0.898      0.875      0.955       0.83\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     19/29     3.21G   0.01369   0.00893   0.01617         5       640: 100% 202/202 [01:57<00:00,  1.72it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 32/32 [00:10<00:00,  3.19it/s]\n",
            "                 all        500        509      0.902      0.885      0.953      0.828\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     20/29     3.21G   0.01405    0.0086   0.01443         7       640: 100% 202/202 [01:56<00:00,  1.73it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 32/32 [00:11<00:00,  2.85it/s]\n",
            "                 all        500        509      0.939      0.894      0.966      0.831\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     21/29     3.21G    0.0138  0.008858    0.0145         6       640: 100% 202/202 [01:57<00:00,  1.72it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 32/32 [00:10<00:00,  3.14it/s]\n",
            "                 all        500        509      0.928      0.871      0.957      0.833\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     22/29     3.21G   0.01223  0.008406   0.01291         3       640: 100% 202/202 [01:56<00:00,  1.73it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 32/32 [00:09<00:00,  3.22it/s]\n",
            "                 all        500        509      0.974      0.891      0.974      0.866\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     23/29     3.21G   0.01304  0.008386   0.01258         3       640: 100% 202/202 [01:56<00:00,  1.74it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 32/32 [00:11<00:00,  2.89it/s]\n",
            "                 all        500        509      0.982      0.901      0.978      0.868\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     24/29     3.21G   0.01245  0.008354   0.01243         7       640: 100% 202/202 [01:57<00:00,  1.72it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 32/32 [00:10<00:00,  3.18it/s]\n",
            "                 all        500        509      0.937      0.937      0.976      0.861\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     25/29     3.21G   0.01127  0.007856   0.01222         5       640: 100% 202/202 [01:56<00:00,  1.73it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 32/32 [00:09<00:00,  3.22it/s]\n",
            "                 all        500        509      0.957       0.91      0.979      0.867\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     26/29     3.21G    0.0105  0.008019   0.01142         5       640: 100% 202/202 [01:56<00:00,  1.74it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 32/32 [00:11<00:00,  2.81it/s]\n",
            "                 all        500        509      0.944      0.939      0.981      0.875\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     27/29     3.21G   0.01121  0.007911   0.01111         8       640: 100% 202/202 [01:57<00:00,  1.72it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 32/32 [00:09<00:00,  3.20it/s]\n",
            "                 all        500        509      0.961      0.931      0.983      0.885\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     28/29     3.21G   0.01014  0.007556  0.009502         3       640: 100% 202/202 [01:56<00:00,  1.74it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 32/32 [00:09<00:00,  3.22it/s]\n",
            "                 all        500        509      0.979      0.928      0.982      0.885\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     29/29     3.21G    0.0108  0.007761  0.009865         2       640: 100% 202/202 [01:56<00:00,  1.74it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 32/32 [00:11<00:00,  2.85it/s]\n",
            "                 all        500        509      0.984      0.927      0.984      0.885\n",
            "\n",
            "30 epochs completed in 1.083 hours.\n",
            "Optimizer stripped from yolov5/runs/train/exp/weights/last.pt, 14.5MB\n",
            "Optimizer stripped from yolov5/runs/train/exp/weights/best.pt, 14.5MB\n",
            "\n",
            "Validating yolov5/runs/train/exp/weights/best.pt...\n",
            "Fusing layers... \n",
            "Model Summary: 224 layers, 7064698 parameters, 0 gradients, 16.4 GFLOPs\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 32/32 [00:16<00:00,  1.99it/s]\n",
            "                 all        500        509      0.984      0.927      0.984      0.885\n",
            "     iranKhodro_dena        500        101      0.979      0.921       0.99      0.903\n",
            "          kia_cerato        500        100      0.997       0.96      0.991      0.936\n",
            "             mazda_3        500        100      0.998       0.95      0.992      0.855\n",
            "         peugeot_206        500        103      0.978       0.88      0.975      0.864\n",
            "         saipa_saina        500        105      0.969      0.924      0.972      0.866\n",
            "\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Waiting for W&B process to finish, PID 470... (success).\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:                                                                                \n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Run history:\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:        metrics/mAP_0.5 ▁▁▁▁▁▁▂▃▄▄▅▅▆▆▆▇▇██████████████\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:   metrics/mAP_0.5:0.95 ▁▁▁▁▁▂▂▃▄▄▄▅▅▆▆▇▇▇▇▇▇██████████\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:      metrics/precision ▁▁▁▁▁▁▁▂▃▃▄▄▅▄▅▆▇▇▇▇███████████\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:         metrics/recall ▃▁▆▇▆▅▄▁▄▂▄▃▄▅▄▆▆▆▆▇▇▆▇▇█▇█████\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:         train/box_loss █▅▄▄▄▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▁▁▁▁▁▁▁▁\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:         train/cls_loss █▇▇▇▇▇▇▇▆▆▆▅▅▅▅▄▄▃▃▃▂▂▂▂▂▂▁▁▁▁\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:         train/obj_loss █▄▃▃▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▁▁▁▁▁▁▁▁▁▁\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:           val/box_loss ██▆▆▇▄▄▄▄▃▃▃▃▂▃▂▃▃▂▂▂▁▁▁▁▁▁▁▁▁▁\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:           val/cls_loss ███████▇▆▆▆▅▄▄▅▃▂▂▂▂▂▂▁▁▁▁▁▁▁▁▁\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:           val/obj_loss █▄▃▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▁▁▁▁▁▁▁▁▁▁▁▁\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:                  x/lr0 ▂▃▅▇████▇▇▇▆▆▆▅▅▅▄▄▃▃▃▂▂▂▂▁▁▁▁\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:                  x/lr1 ▂▃▅▇████▇▇▇▆▆▆▅▅▅▄▄▃▃▃▂▂▂▂▁▁▁▁\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:                  x/lr2 █▆▅▃▂▂▂▂▂▂▂▂▂▂▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Run summary:\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:             best/epoch 29\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:           best/mAP_0.5 0.9842\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:      best/mAP_0.5:0.95 0.88524\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:         best/precision 0.98415\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:            best/recall 0.92674\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:        metrics/mAP_0.5 0.98423\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:   metrics/mAP_0.5:0.95 0.88482\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:      metrics/precision 0.98443\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:         metrics/recall 0.92694\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:         train/box_loss 0.0108\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:         train/cls_loss 0.00987\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:         train/obj_loss 0.00776\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:           val/box_loss 0.00604\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:           val/cls_loss 0.00487\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:           val/obj_loss 0.00298\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:                  x/lr0 0.0011\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:                  x/lr1 0.0011\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:                  x/lr2 0.0011\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Synced 5 W&B file(s), 337 media file(s), 1 artifact file(s) and 0 other file(s)\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Synced \u001b[33mfine-galaxy-1\u001b[0m: \u001b[34mhttps://wandb.ai/nahid-ebrahimian/train/runs/2eshco7i\u001b[0m\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Find logs at: ./wandb/run-20220112_044947-2eshco7i/logs/debug.log\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \n",
            "Results saved to \u001b[1myolov5/runs/train/exp\u001b[0m\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-gp6t7a6vfTA",
        "outputId": "c0a5ffe7-9405-4a51-ce9d-eed6a32314fe"
      },
      "source": [
        "!python yolov5/train.py --img 640 --batch 8 --epochs 30 --data coco128.yaml --weights yolov5s.pt"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[34m\u001b[1mtrain: \u001b[0mweights=yolov5s.pt, cfg=, data=coco128.yaml, hyp=data/hyps/hyp.scratch.yaml, epochs=30, batch_size=8, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, evolve=None, bucket=, cache=None, image_weights=False, device=, multi_scale=False, single_cls=False, adam=False, sync_bn=False, workers=8, project=runs/train, entity=None, name=exp, exist_ok=False, quad=False, linear_lr=False, label_smoothing=0.0, upload_dataset=False, bbox_interval=-1, save_period=-1, artifact_alias=latest, local_rank=-1, freeze=0, patience=30\n",
            "fatal: could not read Username for 'https://github.com': No such device or address\n",
            "\u001b[34m\u001b[1mgithub: \u001b[0mCommand 'git fetch && git config --get remote.origin.url' returned non-zero exit status 128.\n",
            "\u001b[34m\u001b[1mhyperparameters: \u001b[0mlr0=0.01, lrf=0.2, 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[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mnahid-ebrahimian\u001b[0m (use `wandb login --relogin` to force relogin)\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Tracking run with wandb version 0.12.1\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Syncing run \u001b[33mradiant-feather-16\u001b[0m\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: ⭐️ View project at \u001b[34m\u001b[4mhttps://wandb.ai/nahid-ebrahimian/YOLOv5\u001b[0m\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: 🚀 View run at \u001b[34m\u001b[4mhttps://wandb.ai/nahid-ebrahimian/YOLOv5/runs/4fri6sgj\u001b[0m\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Run data is saved locally in /content/drive/My Drive/YoloObjectDetection/wandb/run-20210904_080740-4fri6sgj\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Run `wandb offline` to turn off syncing.\n",
            "\n",
            "Overriding model.yaml nc=80 with nc=5\n",
            "\n",
            "                 from  n    params  module                                  arguments                     \n",
            "  0                -1  1      3520  models.common.Focus                     [3, 32, 3]                    \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  3    156928  models.common.C3                        [128, 128, 3]                 \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    656896  models.common.SPP                       [512, 512, [5, 9, 13]]        \n",
            "  9                -1  1   1182720  models.common.C3                        [512, 512, 1, False]          \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     26970  models.yolo.Detect                      [5, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]\n",
            "Model Summary: 283 layers, 7074330 parameters, 7074330 gradients, 16.4 GFLOPs\n",
            "\n",
            "Transferred 356/362 items from yolov5s.pt\n",
            "Scaled weight_decay = 0.0005\n",
            "\u001b[34m\u001b[1moptimizer:\u001b[0m SGD with parameter groups 59 weight, 62 weight (no decay), 62 bias\n",
            "\u001b[34m\u001b[1malbumentations: \u001b[0mversion 1.0.3 required by YOLOv5, but version 0.1.12 is currently installed\n",
            "\u001b[34m\u001b[1mtrain: \u001b[0mScanning '/content/drive/MyDrive/Yolov5ObjectDetection/Dataset/Train/labels.cache' images and labels... 1600 found, 10 missing, 0 empty, 0 corrupted: 100% 1610/1610 [00:00<?, ?it/s]\n",
            "\u001b[34m\u001b[1mval: \u001b[0mScanning '/content/drive/MyDrive/Yolov5ObjectDetection/Dataset/Val/labels.cache' images and labels... 500 found, 0 missing, 0 empty, 0 corrupted: 100% 500/500 [00:00<?, ?it/s]\n",
            "[W pthreadpool-cpp.cc:90] Warning: Leaking Caffe2 thread-pool after fork. (function pthreadpool)\n",
            "[W pthreadpool-cpp.cc:90] Warning: Leaking Caffe2 thread-pool after fork. (function pthreadpool)\n",
            "Plotting labels... \n",
            "\n",
            "\u001b[34m\u001b[1mautoanchor: \u001b[0mAnalyzing anchors... anchors/target = 2.26, Best Possible Recall (BPR) = 1.0000\n",
            "Image sizes 640 train, 640 val\n",
            "Using 2 dataloader workers\n",
            "Logging results to runs/train/exp14\n",
            "Starting training for 30 epochs...\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "      0/29     1.78G   0.05266   0.02618   0.03623         4       640: 100% 202/202 [03:21<00:00,  1.00it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 32/32 [00:40<00:00,  1.25s/it]\n",
            "                 all        500        509      0.176      0.716      0.212      0.117\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "      1/29     3.21G   0.03309   0.01616   0.03523         5       640: 100% 202/202 [02:01<00:00,  1.66it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 32/32 [00:10<00:00,  3.18it/s]\n",
            "                 all        500        509      0.189      0.908      0.234      0.155\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "      2/29     3.21G   0.02845   0.01358   0.03422         8       640: 100% 202/202 [02:01<00:00,  1.66it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 32/32 [00:11<00:00,  2.81it/s]\n",
            "                 all        500        509      0.185      0.645      0.208      0.135\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "      3/29     3.21G   0.02785   0.01248   0.03497         3       640: 100% 202/202 [02:00<00:00,  1.67it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 32/32 [00:10<00:00,  3.19it/s]\n",
            "                 all        500        509      0.185      0.929      0.224      0.144\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "      4/29     3.21G   0.02619   0.01184   0.03396         4       640: 100% 202/202 [02:00<00:00,  1.68it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 32/32 [00:09<00:00,  3.20it/s]\n",
            "                 all        500        509      0.197      0.951      0.232      0.172\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "      5/29     3.21G   0.02392   0.01197   0.03382         5       640: 100% 202/202 [02:00<00:00,  1.68it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 32/32 [00:11<00:00,  2.85it/s]\n",
            "                 all        500        509      0.198      0.953      0.242      0.191\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "      6/29     3.21G    0.0229   0.01121   0.03369         7       640: 100% 202/202 [02:00<00:00,  1.67it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 32/32 [00:09<00:00,  3.21it/s]\n",
            "                 all        500        509      0.205      0.892      0.245      0.197\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "      7/29     3.21G   0.02151   0.01093   0.03473         8       640: 100% 202/202 [02:00<00:00,  1.68it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 32/32 [00:09<00:00,  3.23it/s]\n",
            "                 all        500        509      0.205      0.852      0.254      0.197\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "      8/29     3.21G   0.01831    0.0105   0.03362         7       640: 100% 202/202 [02:00<00:00,  1.68it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 32/32 [00:11<00:00,  2.83it/s]\n",
            "                 all        500        509      0.203      0.949      0.258      0.216\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "      9/29     3.21G   0.01887   0.01028   0.03377         3       640: 100% 202/202 [02:00<00:00,  1.68it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 32/32 [00:09<00:00,  3.24it/s]\n",
            "                 all        500        509      0.215       0.87      0.274      0.221\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     10/29     3.21G   0.01712  0.009939   0.03327         6       640: 100% 202/202 [01:59<00:00,  1.68it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 32/32 [00:10<00:00,  3.13it/s]\n",
            "                 all        500        509      0.213      0.898      0.274      0.235\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     11/29     3.21G   0.01628  0.009511   0.03339         5       640: 100% 202/202 [02:00<00:00,  1.67it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 32/32 [00:11<00:00,  2.83it/s]\n",
            "                 all        500        509      0.241      0.837      0.308       0.27\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     12/29     3.21G   0.01602  0.009462   0.03368         5       640: 100% 202/202 [02:00<00:00,  1.67it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 32/32 [00:10<00:00,  3.11it/s]\n",
            "                 all        500        509      0.221      0.847      0.336      0.279\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     13/29     3.21G    0.0156  0.009253   0.03333         5       640: 100% 202/202 [02:00<00:00,  1.68it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 32/32 [00:09<00:00,  3.22it/s]\n",
            "                 all        500        509      0.233      0.898      0.385      0.329\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     14/29     3.21G   0.01532  0.009167   0.03332         6       640: 100% 202/202 [02:00<00:00,  1.67it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 32/32 [00:11<00:00,  2.85it/s]\n",
            "                 all        500        509      0.424      0.621      0.493      0.422\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     15/29     3.21G    0.0138  0.009045   0.03149         7       640: 100% 202/202 [02:01<00:00,  1.66it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 32/32 [00:10<00:00,  3.20it/s]\n",
            "                 all        500        509      0.472      0.627      0.531      0.454\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     16/29     3.21G   0.01544  0.008985   0.03176         6       640: 100% 202/202 [02:00<00:00,  1.67it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 32/32 [00:10<00:00,  3.12it/s]\n",
            "                 all        500        509      0.419      0.736      0.592      0.506\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     17/29     3.21G   0.01353  0.008677   0.03035         4       640: 100% 202/202 [02:00<00:00,  1.68it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 32/32 [00:11<00:00,  2.85it/s]\n",
            "                 all        500        509      0.476       0.76      0.627      0.543\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     18/29     3.21G   0.01282  0.008387   0.02852         8       640: 100% 202/202 [02:00<00:00,  1.67it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 32/32 [00:10<00:00,  3.13it/s]\n",
            "                 all        500        509       0.45      0.664      0.602       0.52\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     19/29     3.21G   0.01276  0.008531   0.02777         3       640: 100% 202/202 [01:59<00:00,  1.69it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 32/32 [00:09<00:00,  3.23it/s]\n",
            "                 all        500        509      0.574      0.692      0.682      0.586\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     20/29     3.21G   0.01225  0.008439   0.02753         5       640: 100% 202/202 [01:59<00:00,  1.69it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 32/32 [00:11<00:00,  2.86it/s]\n",
            "                 all        500        509      0.521      0.761        0.7      0.603\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     21/29     3.21G    0.0121  0.008417   0.02663         7       640: 100% 202/202 [02:00<00:00,  1.68it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 32/32 [00:10<00:00,  3.20it/s]\n",
            "                 all        500        509      0.615      0.801      0.752      0.647\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     22/29     3.21G   0.01146  0.008242   0.02536         4       640: 100% 202/202 [01:59<00:00,  1.68it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 32/32 [00:10<00:00,  3.16it/s]\n",
            "                 all        500        509      0.691      0.754      0.799      0.702\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     23/29     3.21G   0.01238   0.00839   0.02555         4       640: 100% 202/202 [01:59<00:00,  1.69it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 32/32 [00:11<00:00,  2.80it/s]\n",
            "                 all        500        509      0.792      0.791      0.867       0.76\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     24/29     3.21G   0.01402  0.008335   0.02468         6       640: 100% 202/202 [02:00<00:00,  1.67it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 32/32 [00:09<00:00,  3.25it/s]\n",
            "                 all        500        509      0.767      0.869      0.903      0.798\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     25/29     3.21G   0.01253  0.008115    0.0228         6       640: 100% 202/202 [01:59<00:00,  1.69it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 32/32 [00:10<00:00,  3.19it/s]\n",
            "                 all        500        509      0.838      0.714      0.875      0.757\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     26/29     3.21G   0.01237  0.008139   0.02222         6       640: 100% 202/202 [02:00<00:00,  1.68it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 32/32 [00:11<00:00,  2.81it/s]\n",
            "                 all        500        509      0.869      0.874       0.93      0.822\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     27/29     3.21G   0.01195  0.008221   0.02122         7       640: 100% 202/202 [02:01<00:00,  1.66it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 32/32 [00:10<00:00,  3.17it/s]\n",
            "                 all        500        509      0.881      0.853      0.937      0.832\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     28/29     3.21G    0.0119   0.00816   0.02025         8       640: 100% 202/202 [02:00<00:00,  1.68it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 32/32 [00:10<00:00,  3.12it/s]\n",
            "                 all        500        509      0.896      0.848      0.951      0.822\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     29/29     3.21G   0.01081  0.008064   0.01917         6       640: 100% 202/202 [02:00<00:00,  1.68it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 32/32 [00:12<00:00,  2.47it/s]\n",
            "                 all        500        509       0.91      0.866      0.956       0.84\n",
            "     iranKhodro_dena        500        101      0.951      0.776      0.961      0.866\n",
            "          kia_cerato        500        100      0.942      0.817      0.954      0.877\n",
            "             mazda_3        500        100      0.872       0.94      0.964      0.826\n",
            "         peugeot_206        500        103      0.894      0.932      0.967      0.841\n",
            "         saipa_saina        500        105      0.892      0.866      0.931       0.79\n",
            "\n",
            "30 epochs completed in 1.140 hours.\n",
            "Optimizer stripped from runs/train/exp14/weights/last.pt, 14.4MB\n",
            "Optimizer stripped from runs/train/exp14/weights/best.pt, 14.4MB\n",
            "\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Waiting for W&B process to finish, PID 420\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Program ended successfully.\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:                                                                                \n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Find user logs for this run at: /content/drive/My Drive/YoloObjectDetection/wandb/run-20210904_080740-4fri6sgj/logs/debug.log\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Find internal logs for this run at: /content/drive/My Drive/YoloObjectDetection/wandb/run-20210904_080740-4fri6sgj/logs/debug-internal.log\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Run summary:\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:                 train/box_loss 0.01081\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:                 train/obj_loss 0.00806\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:                 train/cls_loss 0.01917\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:              metrics/precision 0.91039\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:                 metrics/recall 0.86634\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:                metrics/mAP_0.5 0.95557\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:           metrics/mAP_0.5:0.95 0.83992\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:                   val/box_loss 0.00597\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:                   val/obj_loss 0.00314\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:                   val/cls_loss 0.00692\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:                          x/lr0 0.00209\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:                          x/lr1 0.00209\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:                          x/lr2 0.00209\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:                       _runtime 4181\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:                     _timestamp 1630747041\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:                          _step 30\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Run history:\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:         train/box_loss █▅▄▄▄▃▃▃▂▂▂▂▂▂▂▂▂▁▁▁▁▁▁▁▂▁▁▁▁▁\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:         train/obj_loss █▄▃▃▂▃▂▂▂▂▂▂▂▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:         train/cls_loss ██▇▇▇▇▇▇▇▇▇▇▇▇▇▆▆▆▅▅▄▄▄▄▃▂▂▂▁▁\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:      metrics/precision ▁▁▁▁▁▁▁▁▁▁▁▂▁▂▃▄▃▄▄▅▄▅▆▇▇▇████\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:         metrics/recall ▃▇▂▇██▇▆█▆▇▆▆▇▁▁▃▄▂▂▄▅▄▅▆▃▆▆▆▆\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:        metrics/mAP_0.5 ▁▁▁▁▁▁▁▁▁▂▂▂▂▃▄▄▅▅▅▅▆▆▇▇█▇████\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:   metrics/mAP_0.5:0.95 ▁▁▁▁▂▂▂▂▂▂▂▂▃▃▄▄▅▅▅▆▆▆▇▇█▇████\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:           val/box_loss █▇▇▇▅▅▄▃▂▃▂▂▂▂▂▂▂▂▁▁▂▂▁▂▁▂▁▁▁▁\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:           val/obj_loss █▄▃▂▂▃▂▂▂▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:           val/cls_loss ██▇▇██▇▇▇▇▇▇▇▇▇▆▆▅▅▄▄▄▃▃▂▂▂▂▁▁\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:                  x/lr0 ▁▃▅▆████▇▇▇▆▆▆▅▅▅▄▄▄▃▃▃▂▂▂▁▁▁▁\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:                  x/lr1 ▁▃▅▆████▇▇▇▆▆▆▅▅▅▄▄▄▃▃▃▂▂▂▁▁▁▁\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:                  x/lr2 █▆▅▃▂▂▂▂▂▂▂▂▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:               _runtime ▁▁▁▂▂▂▂▃▃▃▃▄▄▄▄▅▅▅▅▆▆▆▆▇▇▇▇▇███\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:             _timestamp ▁▁▁▂▂▂▂▃▃▃▃▄▄▄▄▅▅▅▅▆▆▆▆▇▇▇▇▇███\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m:                  _step ▁▁▁▂▂▂▂▃▃▃▃▄▄▄▄▅▅▅▅▅▆▆▆▆▇▇▇▇███\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Synced 5 W&B file(s), 337 media file(s), 1 artifact file(s) and 0 other file(s)\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Synced \u001b[33mradiant-feather-16\u001b[0m: \u001b[34mhttps://wandb.ai/nahid-ebrahimian/YOLOv5/runs/4fri6sgj\u001b[0m\n",
            "Results saved to \u001b[1mruns/train/exp14\u001b[0m\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U0I66D7cI_BX"
      },
      "source": [
        "https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bwUf2WIUQmpB"
      },
      "source": [
        "### inference"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S1AW8XFq9tOj",
        "outputId": "ae692a3b-3b5d-4b0c-8062-b1f03a15e535"
      },
      "source": [
        "!python /content/drive/MyDrive/YoloObjectDetection/yolov5/detect.py --weights /content/drive/MyDrive/YoloObjectDetection/runs/train/exp13/weights/last.pt --img 224 --conf 0.4 --source /content/drive/MyDrive/YoloObjectDetection/data/saipa_saina88.jpg"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[34m\u001b[1mdetect: \u001b[0mweights=['/content/drive/MyDrive/YoloObjectDetection/runs/train/exp13/weights/last.pt'], source=/content/drive/MyDrive/YoloObjectDetection/data/saipa_saina88.jpg, imgsz=[224, 224], conf_thres=0.4, iou_thres=0.45, max_det=1000, device=, view_img=False, save_txt=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\n",
            "\u001b[31m\u001b[1mrequirements:\u001b[0m /content/drive/MyDrive/YoloObjectDetection/requirements.txt not found, check failed.\n",
            "YOLOv5 🚀 v5.0-387-g7b35971 torch 1.9.0+cu102 CUDA:0 (Tesla K80, 11441.1875MB)\n",
            "\n",
            "Fusing layers... \n",
            "Model Summary: 224 layers, 7064698 parameters, 0 gradients\n",
            "image 1/1 /content/drive/MyDrive/YoloObjectDetection/data/saipa_saina88.jpg: 192x224 1 saipa_saina, Done. (0.014s)\n",
            "Results saved to \u001b[1mruns/detect/exp11\u001b[0m\n",
            "Done. (0.254s)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ucjZiexMVKSk"
      },
      "source": [
        "### clone from my github\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cH1Q6urhVk2S"
      },
      "source": [
        "# !pip install -U -r yolov5/requirements.txt  # install dependencies"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HQcYFfG5IM4x"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}