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      "metadata": {
        "id": "view-in-github",
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      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ultralytics/yolov3/blob/master/tutorial.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t6MPjfT5NrKQ"
      },
      "source": [
        "<a align=\"left\" href=\"https://ultralytics.com/yolov3\" target=\"_blank\">\n",
        "<img width=\"1024\", src=\"https://user-images.githubusercontent.com/26833433/99805971-90f66b80-2b3d-11eb-80eb-8b45a15cb68e.jpg\"></a>\n",
        "\n",
        "This is the **official YOLOv3 🚀 notebook** by **Ultralytics**, and is freely available for redistribution under the [GPL-3.0 license](https://choosealicense.com/licenses/gpl-3.0/). \n",
        "For more information please visit https://github.com/ultralytics/yolov3 and https://ultralytics.com. Thank you!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7mGmQbAO5pQb"
      },
      "source": [
        "# Setup\n",
        "\n",
        "Clone repo, install dependencies and check PyTorch and GPU."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wbvMlHd_QwMG",
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        },
        "outputId": "7efd38e6-c41f-4fe3-9864-ce4fa43fbb5b"
      },
      "source": [
        "!git clone https://github.com/ultralytics/yolov3  # clone\n",
        "%cd yolov3\n",
        "%pip install -qr requirements.txt  # install\n",
        "\n",
        "import torch\n",
        "from yolov3 import utils\n",
        "display = utils.notebook_init()  # checks"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "YOLOv3 🚀 v9.6.0-1-g93a2bcc torch 1.10.0+cu111 CUDA:0 (A100-SXM4-40GB, 40536MiB)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Setup complete ✅\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4JnkELT0cIJg"
      },
      "source": [
        "# 1. Inference\n",
        "\n",
        "`detect.py` runs YOLOv3 inference on a variety of sources, downloading models automatically from the [latest YOLOv3 release](https://github.com/ultralytics/yolov3/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",
        "                          path/  # directory\n",
        "                          path/*.jpg  # glob\n",
        "                          'https://youtu.be/Zgi9g1ksQHc'  # YouTube\n",
        "                          'rtsp://example.com/media.mp4'  # RTSP, RTMP, HTTP stream\n",
        "```"
      ]
    },
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      "metadata": {
        "id": "zR9ZbuQCH7FX",
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          "base_uri": "https://localhost:8080/"
        },
        "outputId": "486202a4-bae2-454f-da62-2c74676a3058"
      },
      "source": [
        "!python detect.py --weights yolov3.pt --img 640 --conf 0.25 --source data/images\n",
        "display.Image(filename='runs/detect/exp/zidane.jpg', width=600)"
      ],
      "execution_count": 22,
      "outputs": [
        {
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          "name": "stdout",
          "text": [
            "\u001b[34m\u001b[1mdetect: \u001b[0mweights=['yolov3.pt'], source=data/images, imgsz=[640, 640], conf_thres=0.25, 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, dnn=False\n",
            "YOLOv3 🚀 v9.6.0-1-g93a2bcc torch 1.10.0+cu111 CUDA:0 (A100-SXM4-40GB, 40536MiB)\n",
            "\n",
            "Fusing layers... \n",
            "Model Summary: 261 layers, 61922845 parameters, 0 gradients, 156.1 GFLOPs\n",
            "image 1/2 /content/yolov3/data/images/bus.jpg: 640x480 4 persons, 1 bus, 1 tie, 1 sports ball, Done. (0.020s)\n",
            "image 2/2 /content/yolov3/data/images/zidane.jpg: 384x640 2 persons, 3 ties, Done. (0.020s)\n",
            "Speed: 0.5ms pre-process, 20.0ms inference, 1.3ms NMS per image at shape (1, 3, 640, 640)\n",
            "Results saved to \u001b[1mruns/detect/exp\u001b[0m\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hkAzDWJ7cWTr"
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      "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 [COCO](https://cocodataset.org/#home) val or test-dev datasets. Models are downloaded automatically from the [latest YOLOv3 release](https://github.com/ultralytics/yolov3/releases). To show results by class use the `--verbose` flag. Note that `pycocotools` metrics may be ~1% better than the equivalent repo metrics, as is visible below, due to slight differences in mAP computation."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eyTZYGgRjnMc"
      },
      "source": [
        "## COCO val\n",
        "Download [COCO val 2017](https://github.com/ultralytics/yolov3/blob/master/data/coco.yaml) dataset (1GB - 5000 images), and test model accuracy."
      ]
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      "source": [
        "# Download COCO val\n",
        "torch.hub.download_url_to_file('https://ultralytics.com/assets/coco2017val.zip', 'tmp.zip')\n",
        "!unzip -q tmp.zip -d ../datasets && rm tmp.zip"
      ],
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        "outputId": "15c92efb-05ec-48e0-b9ef-ff34871354c8"
      },
      "source": [
        "# Run YOLOv3 on COCO val\n",
        "!python val.py --weights yolov3.pt --data coco.yaml --img 640 --iou 0.65 --half"
      ],
      "execution_count": 23,
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            "\u001b[34m\u001b[1mval: \u001b[0mdata=/content/yolov3/data/coco.yaml, weights=['yolov3.pt'], batch_size=32, imgsz=640, conf_thres=0.001, iou_thres=0.65, task=val, device=, 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",
            "YOLOv3 🚀 v9.6.0-1-g93a2bcc torch 1.10.0+cu111 CUDA:0 (A100-SXM4-40GB, 40536MiB)\n",
            "\n",
            "Fusing layers... \n",
            "Model Summary: 261 layers, 61922845 parameters, 0 gradients, 156.1 GFLOPs\n",
            "\u001b[34m\u001b[1mval: \u001b[0mScanning '../datasets/coco/val2017.cache' images and labels... 4952 found, 48 missing, 0 empty, 0 corrupted: 100% 5000/5000 [00:00<?, ?it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 157/157 [00:45<00:00,  3.45it/s]\n",
            "                 all       5000      36335       0.71      0.602      0.649      0.453\n",
            "Speed: 0.1ms pre-process, 2.1ms inference, 1.5ms NMS per image at shape (32, 3, 640, 640)\n",
            "\n",
            "Evaluating pycocotools mAP... saving runs/val/exp/yolov3_predictions.json...\n",
            "loading annotations into memory...\n",
            "Done (t=0.44s)\n",
            "creating index...\n",
            "index created!\n",
            "Loading and preparing results...\n",
            "DONE (t=4.99s)\n",
            "creating index...\n",
            "index created!\n",
            "Running per image evaluation...\n",
            "Evaluate annotation type *bbox*\n",
            "DONE (t=91.45s).\n",
            "Accumulating evaluation results...\n",
            "DONE (t=15.18s).\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.464\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.655\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.507\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.298\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.515\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.585\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.359\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.594\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.649\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.476\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.698\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.779\n",
            "Results saved to \u001b[1mruns/val/exp\u001b[0m\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rc_KbFk0juX2"
      },
      "source": [
        "## COCO test\n",
        "Download [COCO test2017](https://github.com/ultralytics/yolov3/blob/master/data/coco.yaml) dataset (7GB - 40,000 images), to test model accuracy on test-dev set (**20,000 images, no labels**). Results are saved to a `*.json` file which should be **zipped** and submitted to the evaluation server at https://competitions.codalab.org/competitions/20794."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V0AJnSeCIHyJ"
      },
      "source": [
        "# Download COCO test-dev2017\n",
        "torch.hub.download_url_to_file('https://ultralytics.com/assets/coco2017labels.zip', 'tmp.zip')\n",
        "!unzip -q tmp.zip -d ../datasets && rm tmp.zip\n",
        "!f=\"test2017.zip\" && curl http://images.cocodataset.org/zips/$f -o $f && unzip -q $f -d ../datasets/coco/images"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "29GJXAP_lPrt"
      },
      "source": [
        "# Run YOLOv3 on COCO test\n",
        "!python val.py --weights yolov3.pt --data coco.yaml --img 640 --iou 0.65 --half --task test"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZY2VXXXu74w5"
      },
      "source": [
        "# 3. Train\n",
        "\n",
        "<p align=\"\"><a href=\"https://roboflow.com/?ref=ultralytics\"><img width=\"1000\" src=\"https://uploads-ssl.webflow.com/5f6bc60e665f54545a1e52a5/615627e5824c9c6195abfda9_computer-vision-cycle.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 YOLOv3 model on the [COCO128](https://www.kaggle.com/ultralytics/coco128) dataset with `--data coco128.yaml`, starting from pretrained `--weights yolov3.pt`, or from randomly initialized `--weights '' --cfg yolov3yaml`.\n",
        "\n",
        "- **Pretrained [Models](https://github.com/ultralytics/yolov3/tree/master/models)** are downloaded\n",
        "automatically from the [latest YOLOv3 release](https://github.com/ultralytics/yolov3/releases)\n",
        "- **[Datasets](https://github.com/ultralytics/yolov3/tree/master/data)** available for autodownload include: [COCO](https://github.com/ultralytics/yolov3/blob/master/data/coco.yaml), [COCO128](https://github.com/ultralytics/yolov3/blob/master/data/coco128.yaml), [VOC](https://github.com/ultralytics/yolov3/blob/master/data/VOC.yaml), [Argoverse](https://github.com/ultralytics/yolov3/blob/master/data/Argoverse.yaml), [VisDrone](https://github.com/ultralytics/yolov3/blob/master/data/VisDrone.yaml), [GlobalWheat](https://github.com/ultralytics/yolov3/blob/master/data/GlobalWheat2020.yaml), [xView](https://github.com/ultralytics/yolov3/blob/master/data/xView.yaml), [Objects365](https://github.com/ultralytics/yolov3/blob/master/data/Objects365.yaml), [SKU-110K](https://github.com/ultralytics/yolov3/blob/master/data/SKU-110K.yaml).\n",
        "- **Training Results** are saved to `runs/train/` with incrementing run directories, i.e. `runs/train/exp2`, `runs/train/exp3` etc.\n",
        "<br><br>\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bOy5KI2ncnWd"
      },
      "source": [
        "# Tensorboard  (optional)\n",
        "%load_ext tensorboard\n",
        "%tensorboard --logdir runs/train"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2fLAV42oNb7M"
      },
      "source": [
        "# Weights & Biases  (optional)\n",
        "%pip install -q wandb\n",
        "import wandb\n",
        "wandb.login()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1NcFxRcFdJ_O",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a601aa72-687c-4dda-a16c-c0b2d9073910"
      },
      "source": [
        "# Train YOLOv3 on COCO128 for 3 epochs\n",
        "!python train.py --img 640 --batch 16 --epochs 3 --data coco128.yaml --weights yolov3.pt --cache"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[34m\u001b[1mtrain: \u001b[0mweights=yolov3.pt, cfg=, data=coco128.yaml, hyp=data/hyps/hyp.scratch.yaml, epochs=3, batch_size=16, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, evolve=None, bucket=, cache=ram, image_weights=False, device=, multi_scale=False, single_cls=False, adam=False, sync_bn=False, workers=8, project=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/yolov3 ✅\n",
            "YOLOv3 🚀 v9.6.0-1-g93a2bcc torch 1.10.0+cu111 CUDA:0 (A100-SXM4-40GB, 40536MiB)\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[1mWeights & Biases: \u001b[0mrun 'pip install wandb' to automatically track and visualize YOLOv3 🚀 runs (RECOMMENDED)\n",
            "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/train', view at http://localhost:6006/\n",
            "\n",
            "                 from  n    params  module                                  arguments                     \n",
            "  0                -1  1       928  models.common.Conv                      [3, 32, 3, 1]                 \n",
            "  1                -1  1     18560  models.common.Conv                      [32, 64, 3, 2]                \n",
            "  2                -1  1     20672  models.common.Bottleneck                [64, 64]                      \n",
            "  3                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               \n",
            "  4                -1  2    164608  models.common.Bottleneck                [128, 128]                    \n",
            "  5                -1  1    295424  models.common.Conv                      [128, 256, 3, 2]              \n",
            "  6                -1  8   2627584  models.common.Bottleneck                [256, 256]                    \n",
            "  7                -1  1   1180672  models.common.Conv                      [256, 512, 3, 2]              \n",
            "  8                -1  8  10498048  models.common.Bottleneck                [512, 512]                    \n",
            "  9                -1  1   4720640  models.common.Conv                      [512, 1024, 3, 2]             \n",
            " 10                -1  4  20983808  models.common.Bottleneck                [1024, 1024]                  \n",
            " 11                -1  1   5245952  models.common.Bottleneck                [1024, 1024, False]           \n",
            " 12                -1  1    525312  models.common.Conv                      [1024, 512, [1, 1]]           \n",
            " 13                -1  1   4720640  models.common.Conv                      [512, 1024, 3, 1]             \n",
            " 14                -1  1    525312  models.common.Conv                      [1024, 512, 1, 1]             \n",
            " 15                -1  1   4720640  models.common.Conv                      [512, 1024, 3, 1]             \n",
            " 16                -2  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
            " 17                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
            " 18           [-1, 8]  1         0  models.common.Concat                    [1]                           \n",
            " 19                -1  1   1377792  models.common.Bottleneck                [768, 512, False]             \n",
            " 20                -1  1   1312256  models.common.Bottleneck                [512, 512, False]             \n",
            " 21                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
            " 22                -1  1   1180672  models.common.Conv                      [256, 512, 3, 1]              \n",
            " 23                -2  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n",
            " 24                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
            " 25           [-1, 6]  1         0  models.common.Concat                    [1]                           \n",
            " 26                -1  1    344832  models.common.Bottleneck                [384, 256, False]             \n",
            " 27                -1  2    656896  models.common.Bottleneck                [256, 256, False]             \n",
            " 28      [27, 22, 15]  1    457725  models.yolo.Detect                      [80, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [256, 512, 1024]]\n",
            "Model Summary: 333 layers, 61949149 parameters, 61949149 gradients, 156.3 GFLOPs\n",
            "\n",
            "Transferred 439/439 items from yolov3.pt\n",
            "Scaled weight_decay = 0.0005\n",
            "\u001b[34m\u001b[1moptimizer:\u001b[0m SGD with parameter groups 72 weight, 75 weight (no decay), 75 bias\n",
            "\u001b[34m\u001b[1malbumentations: \u001b[0mversion 1.0.3 required by YOLOv3, but version 0.1.12 is currently installed\n",
            "\u001b[34m\u001b[1mtrain: \u001b[0mScanning '../datasets/coco128/labels/train2017.cache' images and labels... 128 found, 0 missing, 2 empty, 0 corrupted: 100% 128/128 [00:00<?, ?it/s]\n",
            "\u001b[34m\u001b[1mtrain: \u001b[0mCaching images (0.1GB ram): 100% 128/128 [00:00<00:00, 1333.45it/s]\n",
            "\u001b[34m\u001b[1mval: \u001b[0mScanning '../datasets/coco128/labels/train2017.cache' images and labels... 128 found, 0 missing, 2 empty, 0 corrupted: 100% 128/128 [00:00<?, ?it/s]\n",
            "\u001b[34m\u001b[1mval: \u001b[0mCaching images (0.1GB ram): 100% 128/128 [00:00<00:00, 377.72it/s]\n",
            "Plotting labels to runs/train/exp/labels.jpg... \n",
            "\n",
            "\u001b[34m\u001b[1mAutoAnchor: \u001b[0m4.27 anchors/target, 0.994 Best Possible Recall (BPR). Current anchors are a good fit to dataset ✅\n",
            "Image sizes 640 train, 640 val\n",
            "Using 8 dataloader workers\n",
            "Logging results to \u001b[1mruns/train/exp\u001b[0m\n",
            "Starting training for 3 epochs...\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "       0/2     12.2G   0.03673   0.05445    0.0102       186       640: 100% 8/8 [00:04<00:00,  1.93it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 4/4 [00:00<00:00,  5.11it/s]\n",
            "                 all        128        929      0.769      0.747      0.822      0.584\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "       1/2     16.3G   0.03681   0.05345   0.01067       164       640: 100% 8/8 [00:01<00:00,  4.77it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 4/4 [00:00<00:00,  4.98it/s]\n",
            "                 all        128        929      0.745       0.77      0.827      0.589\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "       2/2     16.3G   0.03496   0.04829   0.01105       192       640: 100% 8/8 [00:01<00:00,  4.77it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 4/4 [00:00<00:00,  5.20it/s]\n",
            "                 all        128        929      0.779      0.752      0.829      0.588\n",
            "\n",
            "3 epochs completed in 0.005 hours.\n",
            "Optimizer stripped from runs/train/exp/weights/last.pt, 124.4MB\n",
            "Optimizer stripped from runs/train/exp/weights/best.pt, 124.4MB\n",
            "\n",
            "Validating runs/train/exp/weights/best.pt...\n",
            "Fusing layers... \n",
            "Model Summary: 261 layers, 61922845 parameters, 0 gradients, 156.1 GFLOPs\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 4/4 [00:02<00:00,  1.36it/s]\n",
            "                 all        128        929      0.746       0.77      0.827      0.589\n",
            "              person        128        254      0.862      0.787      0.863      0.624\n",
            "             bicycle        128          6      0.723      0.667      0.723       0.47\n",
            "                 car        128         46      0.851      0.435      0.681      0.305\n",
            "          motorcycle        128          5      0.816          1      0.962      0.784\n",
            "            airplane        128          6      0.916          1      0.995      0.786\n",
            "                 bus        128          7          1      0.684      0.937      0.797\n",
            "               train        128          3      0.989          1      0.995      0.863\n",
            "               truck        128         12      0.728        0.5       0.61      0.414\n",
            "                boat        128          6        0.7        0.5      0.582      0.337\n",
            "       traffic light        128         14          1      0.452       0.58      0.268\n",
            "           stop sign        128          2      0.712          1      0.995      0.747\n",
            "               bench        128          9      0.793      0.853      0.848      0.507\n",
            "                bird        128         16      0.966          1      0.995      0.655\n",
            "                 cat        128          4      0.862          1      0.995      0.958\n",
            "                 dog        128          9      0.787          1      0.995      0.774\n",
            "               horse        128          2      0.695          1      0.995      0.697\n",
            "            elephant        128         17      0.938      0.894      0.943      0.767\n",
            "                bear        128          1      0.672          1      0.995      0.895\n",
            "               zebra        128          4      0.869          1      0.995      0.995\n",
            "             giraffe        128          9      0.942          1      0.995      0.733\n",
            "            backpack        128          6      0.668       0.67      0.754      0.505\n",
            "            umbrella        128         18      0.893      0.889      0.948      0.646\n",
            "             handbag        128         19       0.71      0.526       0.55      0.336\n",
            "                 tie        128          7      0.905      0.857      0.857        0.6\n",
            "            suitcase        128          4      0.857          1      0.995      0.722\n",
            "             frisbee        128          5      0.729        0.8      0.761      0.658\n",
            "                skis        128          1      0.685          1      0.995      0.497\n",
            "           snowboard        128          7      0.923      0.714      0.824      0.628\n",
            "         sports ball        128          6      0.565      0.667      0.712      0.435\n",
            "                kite        128         10      0.668        0.6      0.671      0.188\n",
            "        baseball bat        128          4      0.663          1      0.945      0.422\n",
            "      baseball glove        128          7      0.697      0.663      0.649      0.331\n",
            "          skateboard        128          5      0.792        0.8      0.766      0.455\n",
            "       tennis racket        128          7      0.862      0.714      0.718      0.359\n",
            "              bottle        128         18       0.69      0.722      0.764      0.497\n",
            "          wine glass        128         16      0.759      0.875      0.891      0.559\n",
            "                 cup        128         36      0.865      0.889      0.916      0.635\n",
            "                fork        128          6      0.671        0.5       0.76      0.474\n",
            "               knife        128         16      0.751      0.812      0.842      0.512\n",
            "               spoon        128         22      0.832      0.636      0.687      0.473\n",
            "                bowl        128         28      0.873       0.75      0.774      0.625\n",
            "              banana        128          1      0.767          1      0.995      0.597\n",
            "            sandwich        128          2      0.134      0.134      0.448      0.408\n",
            "              orange        128          4      0.534          1      0.995      0.746\n",
            "            broccoli        128         11      0.474      0.364       0.36        0.3\n",
            "              carrot        128         24      0.762       0.75      0.861      0.573\n",
            "             hot dog        128          2      0.561          1      0.995      0.995\n",
            "               pizza        128          5      0.893          1      0.995      0.771\n",
            "               donut        128         14      0.735          1      0.959      0.903\n",
            "                cake        128          4      0.711          1      0.995      0.908\n",
            "               chair        128         35      0.639      0.771      0.793      0.505\n",
            "               couch        128          6      0.702      0.667      0.796      0.599\n",
            "        potted plant        128         14      0.697      0.714      0.827      0.555\n",
            "                 bed        128          3          1          1      0.995      0.643\n",
            "        dining table        128         13      0.566      0.403      0.521       0.32\n",
            "              toilet        128          2        0.8          1      0.995      0.945\n",
            "                  tv        128          2      0.473          1      0.995      0.821\n",
            "              laptop        128          3      0.507      0.347       0.72      0.481\n",
            "               mouse        128          2          1          0      0.828        0.5\n",
            "              remote        128          8      0.845      0.625      0.751      0.628\n",
            "          cell phone        128          8      0.503      0.375      0.571       0.35\n",
            "           microwave        128          3      0.643          1      0.995      0.816\n",
            "                oven        128          5       0.47        0.6      0.615      0.465\n",
            "                sink        128          6      0.403      0.333      0.465       0.32\n",
            "        refrigerator        128          5      0.923        0.8      0.862      0.659\n",
            "                book        128         29      0.552      0.241      0.354      0.161\n",
            "               clock        128          9      0.915          1      0.995        0.8\n",
            "                vase        128          2       0.44          1      0.663      0.622\n",
            "            scissors        128          1      0.634          1      0.995     0.0995\n",
            "          teddy bear        128         21      0.874      0.658      0.926      0.621\n",
            "          toothbrush        128          5      0.902          1      0.995      0.794\n",
            "Results saved to \u001b[1mruns/train/exp\u001b[0m\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "15glLzbQx5u0"
      },
      "source": [
        "# 4. Visualize"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DLI1JmHU7B0l"
      },
      "source": [
        "## Weights & Biases Logging 🌟 NEW\n",
        "\n",
        "[Weights & Biases](https://wandb.ai/site?utm_campaign=repo_yolo_notebook) (W&B) is now integrated with YOLOv3 for real-time visualization and cloud logging of training runs. This allows for better run comparison and introspection, as well improved visibility and collaboration for teams. To enable W&B `pip install wandb`, and then train normally (you will be guided through setup on first use). \n",
        "\n",
        "During training you will see live updates at [https://wandb.ai/home](https://wandb.ai/home?utm_campaign=repo_yolo_notebook), and you can create and share detailed [Reports](https://wandb.ai/glenn-jocher/yolov5_tutorial/reports/YOLOv5-COCO128-Tutorial-Results--VmlldzozMDI5OTY) of your results. For more information see the [YOLOv5 Weights & Biases Tutorial](https://github.com/ultralytics/yolov5/issues/1289). \n",
        "\n",
        "<p align=\"left\"><img width=\"900\" alt=\"Weights & Biases dashboard\" src=\"https://user-images.githubusercontent.com/26833433/135390767-c28b050f-8455-4004-adb0-3b730386e2b2.png\"></p>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-WPvRbS5Swl6"
      },
      "source": [
        "## Local Logging\n",
        "\n",
        "All results are logged by default to `runs/train`, with a new experiment directory created for each new training as `runs/train/exp2`, `runs/train/exp3`, etc. View train and val jpgs to see mosaics, labels, predictions and augmentation effects. Note an Ultralytics **Mosaic Dataloader** is used for training (shown below), which combines 4 images into 1 mosaic during training.\n",
        "\n",
        "> <img src=\"https://user-images.githubusercontent.com/26833433/131255960-b536647f-7c61-4f60-bbc5-cb2544d71b2a.jpg\" width=\"700\">  \n",
        "`train_batch0.jpg` shows train batch 0 mosaics and labels\n",
        "\n",
        "> <img src=\"https://user-images.githubusercontent.com/26833433/131256748-603cafc7-55d1-4e58-ab26-83657761aed9.jpg\" width=\"700\">  \n",
        "`test_batch0_labels.jpg` shows val batch 0 labels\n",
        "\n",
        "> <img src=\"https://user-images.githubusercontent.com/26833433/131256752-3f25d7a5-7b0f-4bb3-ab78-46343c3800fe.jpg\" width=\"700\">  \n",
        "`test_batch0_pred.jpg` shows val batch 0 _predictions_\n",
        "\n",
        "Training results are automatically logged to [Tensorboard](https://www.tensorflow.org/tensorboard) and [CSV](https://github.com/ultralytics/yolov5/pull/4148) as `results.csv`, which is plotted as `results.png` (below) after training completes. You can also plot any `results.csv` file manually:\n",
        "\n",
        "```python\n",
        "from utils.plots import plot_results \n",
        "plot_results('path/to/results.csv')  # plot 'results.csv' as 'results.png'\n",
        "```\n",
        "\n",
        "<img align=\"left\" width=\"800\" alt=\"COCO128 Training Results\" src=\"https://user-images.githubusercontent.com/26833433/126906780-8c5e2990-6116-4de6-b78a-367244a33ccf.png\">"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Zelyeqbyt3GD"
      },
      "source": [
        "# Environments\n",
        "\n",
        "YOLOv3 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",
        "- **Google Colab and Kaggle** notebooks with free GPU: <a href=\"https://colab.research.google.com/github/ultralytics/yolov3/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/yolov3\"><img src=\"https://kaggle.com/static/images/open-in-kaggle.svg\" alt=\"Open In Kaggle\"></a>\n",
        "- **Google Cloud** Deep Learning VM. See [GCP Quickstart Guide](https://github.com/ultralytics/yolov3/wiki/GCP-Quickstart)\n",
        "- **Amazon** Deep Learning AMI. See [AWS Quickstart Guide](https://github.com/ultralytics/yolov3/wiki/AWS-Quickstart)\n",
        "- **Docker Image**. See [Docker Quickstart Guide](https://github.com/ultralytics/yolov3/wiki/Docker-Quickstart) <a href=\"https://hub.docker.com/r/ultralytics/yolov3\"><img src=\"https://img.shields.io/docker/pulls/ultralytics/yolov3?logo=docker\" alt=\"Docker Pulls\"></a>\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6Qu7Iesl0p54"
      },
      "source": [
        "# Status\n",
        "\n",
        "![CI CPU testing](https://github.com/ultralytics/yolov3/workflows/CI%20CPU%20testing/badge.svg)\n",
        "\n",
        "If this badge is green, all [YOLOv3 GitHub Actions](https://github.com/ultralytics/yolov3/actions) Continuous Integration (CI) tests are currently passing. CI tests verify correct operation of YOLOv3 training ([train.py](https://github.com/ultralytics/yolov3/blob/master/train.py)), testing ([val.py](https://github.com/ultralytics/yolov3/blob/master/val.py)), inference ([detect.py](https://github.com/ultralytics/yolov3/blob/master/detect.py)) and export ([export.py](https://github.com/ultralytics/yolov3/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",
        "Optional extras below. Unit tests validate repo functionality and should be run on any PRs submitted.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mcKoSIK2WSzj"
      },
      "source": [
        "# Reproduce\n",
        "for x in 'yolov3', 'yolov3-spp', 'yolov3-tiny':\n",
        "  !python val.py --weights {x}.pt --data coco.yaml --img 640 --task speed  # speed\n",
        "  !python val.py --weights {x}.pt --data coco.yaml --img 640 --conf 0.001 --iou 0.65  # mAP"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GMusP4OAxFu6"
      },
      "source": [
        "# PyTorch Hub\n",
        "import torch\n",
        "\n",
        "# Model\n",
        "model = torch.hub.load('ultralytics/yolov3', 'yolov3')\n",
        "\n",
        "# Images\n",
        "dir = 'https://ultralytics.com/images/'\n",
        "imgs = [dir + f for f in ('zidane.jpg', 'bus.jpg')]  # batch of images\n",
        "\n",
        "# Inference\n",
        "results = model(imgs)\n",
        "results.print()  # or .show(), .save()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FGH0ZjkGjejy"
      },
      "source": [
        "# CI Checks\n",
        "%%shell\n",
        "export PYTHONPATH=\"$PWD\"  # to run *.py. files in subdirectories\n",
        "rm -rf runs  # remove runs/\n",
        "for m in yolov3-tiny; do  # models\n",
        "  python train.py --img 64 --batch 32 --weights $m.pt --epochs 1 --device 0  # train pretrained\n",
        "  python train.py --img 64 --batch 32 --weights '' --cfg $m.yaml --epochs 1 --device 0  # train scratch\n",
        "  for d in 0 cpu; do  # devices\n",
        "    python val.py --weights $m.pt --device $d # val official\n",
        "    python val.py --weights runs/train/exp/weights/best.pt --device $d # val custom\n",
        "    python detect.py --weights $m.pt --device $d  # detect official\n",
        "    python detect.py --weights runs/train/exp/weights/best.pt --device $d  # detect custom\n",
        "  done\n",
        "  python hubconf.py  # hub\n",
        "  python models/yolo.py --cfg $m.yaml  # build PyTorch model\n",
        "  python models/tf.py --weights $m.pt  # build TensorFlow model\n",
        "  python export.py --img 64 --batch 1 --weights $m.pt --include torchscript onnx  # export\n",
        "done"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gogI-kwi3Tye"
      },
      "source": [
        "# Profile\n",
        "from utils.torch_utils import profile\n",
        "\n",
        "m1 = lambda x: x * torch.sigmoid(x)\n",
        "m2 = torch.nn.SiLU()\n",
        "results = profile(input=torch.randn(16, 3, 640, 640), ops=[m1, m2], n=100)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RVRSOhEvUdb5"
      },
      "source": [
        "# Evolve\n",
        "!python train.py --img 640 --batch 64 --epochs 100 --data coco128.yaml --weights yolov3.pt --cache --noautoanchor --evolve\n",
        "!d=runs/train/evolve && cp evolve.* $d && zip -r evolve.zip $d && gsutil mv evolve.zip gs://bucket  # upload results (optional)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BSgFCAcMbk1R"
      },
      "source": [
        "# VOC\n",
        "for b, m in zip([24, 24, 64], ['yolov3', 'yolov3-spp', 'yolov3-tiny']):  # zip(batch_size, model)\n",
        "  !python train.py --batch {b} --weights {m}.pt --data VOC.yaml --epochs 50 --cache --img 512 --nosave --hyp hyp.finetune.yaml --project VOC --name {m}"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}