YOLOv5_Classification_Tutorial.ipynb

{
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      "source": [
        "<a href=\"https://colab.research.google.com/github/danielsoy/ALOCC-CVPR2018/blob/master/YOLOv5_Classification_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": "5GYQX3of4QiW"
      },
      "source": [
        "## YOLOv5 Classification Tutorial\n",
        "\n",
        "YOLOv5 supports classification tasks too. This is the official YOLOv5 classification notebook tutorial. YOLOv5 is maintained by [Ultralytics](https://github.com/ultralytics/yolov5).\n",
        "\n",
        "This notebook covers:\n",
        "\n",
        "*   Inference with out-of-the-box YOLOv5 classification on ImageNet\n",
        "*  [Training YOLOv5 classification](https://blog.roboflow.com//train-YOLOv5-classification-custom-data) on custom data\n",
        "\n",
        "*Looking for custom data? Explore over 66M community datasets on [Roboflow Universe](https://universe.roboflow.com).*\n",
        "\n",
        "This notebook was created with Google Colab. [Click here](https://colab.research.google.com/drive/1FiSNz9f_nT8aFtDEU3iDAQKlPT8SCVni?usp=sharing) to run it."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-PJ8vlYXbWtN"
      },
      "source": [
        "# Setup\n",
        "\n",
        "Pull in respective libraries to prepare the notebook environment."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pIM7fOwm8A7l",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "349b32a7-bd19-4de9-cf92-2e3e8a2af583"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "YOLOv5 🚀 v6.2-109-g23701ea Python-3.7.13 torch-1.12.1+cu113 CUDA:0 (Tesla P100-PCIE-16GB, 16281MiB)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Setup complete ✅ (2 CPUs, 12.7 GB RAM, 37.4/166.8 GB disk)\n"
          ]
        }
      ],
      "source": [
        "!git clone https://github.com/ultralytics/yolov5  # clone\n",
        "%cd yolov5\n",
        "%pip install -qr requirements.txt  # install\n",
        "\n",
        "import torch\n",
        "import utils\n",
        "display = utils.notebook_init()  # checks"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!git clone https://github.com/DarvinX/trash_classifier.git"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3yKByrPp7rGD",
        "outputId": "6d498805-601e-4853-c97f-af914d180600"
      },
      "execution_count": null,
      "outputs": [
        {
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          "name": "stdout",
          "text": [
            "Cloning into 'trash_classifier'...\n",
            "remote: Enumerating objects: 2574, done.\u001b[K\n",
            "remote: Counting objects: 100% (3/3), done.\u001b[K\n",
            "remote: Compressing objects: 100% (3/3), done.\u001b[K\n",
            "remote: Total 2574 (delta 0), reused 0 (delta 0), pack-reused 2571\u001b[K\n",
            "Receiving objects: 100% (2574/2574), 43.30 MiB | 20.59 MiB/s, done.\n",
            "Resolving deltas: 100% (9/9), done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i_DrUi2nmF40"
      },
      "source": [
        "# 1. Infer on ImageNet\n",
        "\n",
        "To demonstrate YOLOv5 classification, we'll leverage an already trained model. In this case, we'll download the ImageNet trained models pretrained on ImageNet using YOLOv5 Utils."
      ]
    },
    {
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Downloading https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5n-cls.pt to weights/yolov5n-cls.pt...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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          "metadata": {}
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        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n",
            "Downloading https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5s-cls.pt to weights/yolov5s-cls.pt...\n"
          ]
        },
        {
          "output_type": "display_data",
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          "metadata": {}
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        {
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          "name": "stderr",
          "text": [
            "\n",
            "Downloading https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5m-cls.pt to weights/yolov5m-cls.pt...\n"
          ]
        },
        {
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          "metadata": {}
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        {
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          "name": "stderr",
          "text": [
            "\n",
            "Downloading https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5l-cls.pt to weights/yolov5l-cls.pt...\n"
          ]
        },
        {
          "output_type": "display_data",
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        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n",
            "Downloading https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5x-cls.pt to weights/yolov5x-cls.pt...\n"
          ]
        },
        {
          "output_type": "display_data",
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        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        }
      ],
      "source": [
        "from utils.downloads import attempt_download\n",
        "\n",
        "p5 = ['n', 's', 'm', 'l', 'x']  # P5 models\n",
        "cls = [f'{x}-cls' for x in p5]  # classification models\n",
        "\n",
        "for x in cls:\n",
        "    attempt_download(f'weights/yolov5{x}.pt')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Fn2_a38DmZ2H"
      },
      "source": [
        "Now, we can infer on an example image from the ImageNet dataset."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-5z7Yv42FGrK"
      },
      "source": [
        "### Train On Custom Data 🎉\n",
        "Here, we use the DATASET_NAME environment variable to pass our dataset to the `--data` parameter.\n",
        "\n",
        "Note: we're training for 100 epochs here. We're also starting training from the pretrained weights. Larger datasets will likely benefit from longer training. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "MXWTTN2BEaqe",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a6bc7891-0a1e-4fd9-ee88-5f094f84b123"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/yolov5\n",
            "\u001b[34m\u001b[1mclassify/train: \u001b[0mmodel=yolov5n-cls.pt, data=trash_classifier/dataset, epochs=30, batch_size=64, imgsz=224, nosave=False, cache=None, device=, workers=8, project=runs/train-cls, name=exp, exist_ok=False, pretrained=weights/yolov5s-cls.pt, optimizer=Adam, lr0=0.001, decay=5e-05, label_smoothing=0.1, cutoff=None, dropout=None, verbose=False, seed=0, local_rank=-1\n",
            "\u001b[34m\u001b[1mgithub: \u001b[0mup to date with https://github.com/ultralytics/yolov5 ✅\n",
            "YOLOv5 🚀 v6.2-109-g23701ea Python-3.7.13 torch-1.12.1+cu113 CUDA:0 (Tesla P100-PCIE-16GB, 16281MiB)\n",
            "\n",
            "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/train-cls', view at http://localhost:6006/\n",
            "\u001b[34m\u001b[1malbumentations: \u001b[0mRandomResizedCrop(p=1.0, height=224, width=224, scale=(0.08, 1.0), ratio=(0.75, 1.3333333333333333), interpolation=1), HorizontalFlip(p=0.5), ColorJitter(p=0.5, brightness=[0.6, 1.4], contrast=[0.6, 1.4], saturation=[0.6, 1.4], hue=[0, 0]), Normalize(p=1.0, mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225), max_pixel_value=255.0), ToTensorV2(always_apply=True, p=1.0, transpose_mask=False)\n",
            "Downloading https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5n-cls.pt to yolov5n-cls.pt...\n",
            "100% 4.87M/4.87M [00:00<00:00, 14.3MB/s]\n",
            "\n",
            "Model summary: 149 layers, 1218405 parameters, 1218405 gradients, 3.0 GFLOPs\n",
            "\u001b[34m\u001b[1moptimizer:\u001b[0m Adam(lr=0.001) with parameter groups 32 weight(decay=0.0), 33 weight(decay=5e-05), 33 bias\n",
            "Image sizes 224 train, 224 test\n",
            "Using 1 dataloader workers\n",
            "Logging results to \u001b[1mruns/train-cls/exp4\u001b[0m\n",
            "Starting yolov5n-cls.pt training on trash_classifier/dataset dataset with 5 classes for 30 epochs...\n",
            "\n",
            "     Epoch   GPU_mem  train_loss   test_loss    top1_acc    top5_acc\n",
            "      1/30    0.805G        1.48           2           0           1: 100% 36/36 [00:10<00:00,  3.35it/s]\n",
            "      2/30     1.57G        1.37        2.26           0           1: 100% 36/36 [00:10<00:00,  3.45it/s]\n",
            "      3/30     1.57G        1.35        2.43      0.0365           1: 100% 36/36 [00:10<00:00,  3.37it/s]\n",
            "      4/30     1.57G        1.31        1.73       0.168           1: 100% 36/36 [00:10<00:00,  3.37it/s]\n",
            "      5/30     1.57G        1.28        2.26      0.0803           1: 100% 36/36 [00:10<00:00,  3.36it/s]\n",
            "      6/30     1.57G        1.28        2.03       0.139           1: 100% 36/36 [00:10<00:00,  3.37it/s]\n",
            "      7/30     1.57G        1.25        2.03       0.102           1: 100% 36/36 [00:10<00:00,  3.33it/s]\n",
            "      8/30     1.57G        1.26        2.29       0.117           1: 100% 36/36 [00:10<00:00,  3.39it/s]\n",
            "      9/30     1.57G        1.26        1.96       0.146           1: 100% 36/36 [00:10<00:00,  3.33it/s]\n",
            "     10/30     1.57G        1.21        2.36       0.073           1: 100% 36/36 [00:10<00:00,  3.31it/s]\n",
            "     11/30     1.57G         1.2           2       0.212           1: 100% 36/36 [00:10<00:00,  3.33it/s]\n",
            "     12/30     1.57G        1.16        2.46      0.0803           1: 100% 36/36 [00:10<00:00,  3.33it/s]\n",
            "     13/30     1.57G        1.17        2.15       0.168           1: 100% 36/36 [00:10<00:00,  3.27it/s]\n",
            "     14/30     1.57G        1.14        2.68      0.0438           1: 100% 36/36 [00:10<00:00,  3.28it/s]\n",
            "     15/30     1.57G        1.08        2.97       0.073           1: 100% 36/36 [00:11<00:00,  3.24it/s]\n",
            "     16/30     1.57G         1.1        2.51       0.117           1: 100% 36/36 [00:10<00:00,  3.28it/s]\n",
            "     17/30     1.57G        1.09        2.06       0.168           1: 100% 36/36 [00:10<00:00,  3.29it/s]\n",
            "     18/30     1.57G        1.06        2.77      0.0584           1: 100% 36/36 [00:11<00:00,  3.25it/s]\n",
            "     19/30     1.57G        1.03        2.52       0.073           1: 100% 36/36 [00:11<00:00,  3.25it/s]\n",
            "     20/30     1.57G        1.02        2.93      0.0438           1: 100% 36/36 [00:11<00:00,  3.23it/s]\n",
            "     21/30     1.57G        1.02        2.68       0.117           1: 100% 36/36 [00:11<00:00,  3.25it/s]\n",
            "     22/30     1.57G       0.996        2.75      0.0365           1: 100% 36/36 [00:11<00:00,  3.24it/s]\n",
            "     23/30     1.57G       0.977        2.59      0.0511           1: 100% 36/36 [00:10<00:00,  3.28it/s]\n",
            "     24/30     1.57G        0.99        2.42       0.073           1: 100% 36/36 [00:10<00:00,  3.29it/s]\n",
            "     25/30     1.57G       0.965        2.78      0.0876           1: 100% 36/36 [00:11<00:00,  3.26it/s]\n",
            "     26/30     1.57G        0.96        2.87      0.0657           1: 100% 36/36 [00:11<00:00,  3.21it/s]\n",
            "     27/30     1.57G       0.943        2.96       0.073           1: 100% 36/36 [00:11<00:00,  3.27it/s]\n",
            "     28/30     1.57G       0.939        3.19      0.0511           1: 100% 36/36 [00:11<00:00,  3.27it/s]\n",
            "     29/30     1.57G       0.923        3.08      0.0803           1: 100% 36/36 [00:11<00:00,  3.24it/s]\n",
            "     30/30     1.57G       0.913        2.42       0.109           1: 100% 36/36 [00:10<00:00,  3.28it/s]\n",
            "\n",
            "Training complete (0.092 hours)\n",
            "Results saved to \u001b[1mruns/train-cls/exp4\u001b[0m\n",
            "Predict:         python classify/predict.py --weights runs/train-cls/exp4/weights/best.pt --source im.jpg\n",
            "Validate:        python classify/val.py --weights runs/train-cls/exp4/weights/best.pt --data trash_classifier/dataset\n",
            "Export:          python export.py --weights runs/train-cls/exp4/weights/best.pt --include onnx\n",
            "PyTorch Hub:     model = torch.hub.load('ultralytics/yolov5', 'custom', 'runs/train-cls/exp4/weights/best.pt')\n",
            "Visualize:       https://netron.app\n",
            "\n"
          ]
        }
      ],
      "source": [
        "%cd ../yolov5\n",
        "\n",
        "from utils.downloads import attempt_download\n",
        "\n",
        "p5 = ['n', 's', 'm', 'l', 'x']  # P5 models\n",
        "cls = [f'{x}-cls' for x in p5]  # classification models\n",
        "\n",
        "for x in cls:\n",
        "    attempt_download(f'weights/yolov5{x}.pt')\n",
        "!python classify/train.py --model yolov5n-cls.pt --data trash_classifier/dataset --epochs 30 --img 224 --pretrained weights/yolov5s-cls.pt"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HHUFGeLbGd98"
      },
      "source": [
        "### Validate Your Custom Model\n",
        "\n",
        "Repeat step 2 from above to test and validate your custom model."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "DIV7ydyKGZFL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2d9878ea-3763-4eba-bd58-3d01b3ae00c4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[34m\u001b[1mclassify/val: \u001b[0mdata=./trash_classifier/dataset/, weights=['runs/train-cls/exp2/weights/best.pt'], batch_size=128, imgsz=224, device=, workers=8, verbose=True, project=runs/val-cls, name=exp, exist_ok=False, half=False, dnn=False\n",
            "YOLOv5 🚀 v6.2-109-g23701ea Python-3.7.13 torch-1.12.1+cu113 CUDA:0 (Tesla P100-PCIE-16GB, 16281MiB)\n",
            "\n",
            "Fusing layers... \n",
            "Model summary: 117 layers, 4173093 parameters, 0 gradients, 10.4 GFLOPs\n",
            "testing: 100% 2/2 [00:00<00:00,  3.05it/s]\n",
            "                   Class      Images    top1_acc    top5_acc\n",
            "                     all         137       0.628           1\n",
            "               cardboard         137       0.628           1\n",
            "                   glass           0         nan         nan\n",
            "                   metal           0         nan         nan\n",
            "                   paper           0         nan         nan\n",
            "                 plastic           0         nan         nan\n",
            "Speed: 0.1ms pre-process, 0.3ms inference, 0.0ms post-process per image at shape (1, 3, 224, 224)\n",
            "Results saved to \u001b[1mruns/val-cls/exp8\u001b[0m\n"
          ]
        }
      ],
      "source": [
        "!python classify/val.py --weights runs/train-cls/exp2/weights/best.pt --data ./trash_classifier/dataset/"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!tensorboard --logdir=runs/train-cls/exp"
      ],
      "metadata": {
        "id": "MiJSss_r4T9B"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!ls runs/val-cls/exp"
      ],
      "metadata": {
        "id": "YfVJBMd3DO_2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import os"
      ],
      "metadata": {
        "id": "Qx1D3HZELmhb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uH5tJNpEsi6g"
      },
      "source": [
        "### Infer With Your Custom Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "81lK1hU_sk54"
      },
      "outputs": [],
      "source": [
        "#Get the path of an image from the test or validation set\n",
        "if os.path.exists(os.path.join(datasets/.location, \"test\")):\n",
        "  split_path = os.path.join(datasets.location, \"test\")\n",
        "else:\n",
        "  split_path = os.path.join(datasets.location, \"val\")\n",
        "example_class = os.listdir(split_path)[0]\n",
        "example_image_name = os.listdir(os.path.join(split_path, example_class))[0]\n",
        "example_image_path = os.path.join(split_path, example_class, example_image_name)\n",
        "os.environ[\"TEST_IMAGE_PATH\"] = example_image_path\n",
        "\n",
        "print(f\"Inferring on an example of the class '{example_class}'\")\n",
        "\n",
        "#Infer\n",
        "!python classify/predict.py --weights runs/train-cls/exp4/weights/best.pt --source /content/drive/MyDrive/IMG-20200222-WA0052.jpg"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!python classify/predict.py --weights runs/train-cls/exp4/weights/best.pt --source /content/drive/MyDrive/image1.jpg"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oNKcyTytMNNQ",
        "outputId": "03696dd8-c2ba-4334-aa3a-54ec237b14a9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[34m\u001b[1mclassify/predict: \u001b[0mweights=['runs/train-cls/exp4/weights/best.pt'], source=/content/drive/MyDrive/image1.jpg, data=data/coco128.yaml, imgsz=[224, 224], device=, view_img=False, save_txt=False, nosave=False, augment=False, visualize=False, update=False, project=runs/predict-cls, name=exp, exist_ok=False, half=False, dnn=False\n",
            "YOLOv5 🚀 v6.2-78-g5f1000a Python-3.7.13 torch-1.12.1+cu113 CUDA:0 (Tesla T4, 15110MiB)\n",
            "\n",
            "Fusing layers... \n",
            "Model summary: 117 layers, 4173093 parameters, 0 gradients, 10.4 GFLOPs\n",
            "image 1/1 /content/drive/MyDrive/image1.jpg: 224x224 grade_4 0.46, grade_3 0.37, grade_2 0.11, grade_1 0.04, healthy_plant 0.02, 4.2ms\n",
            "Speed: 0.5ms pre-process, 4.2ms inference, 0.1ms NMS per image at shape (1, 3, 224, 224)\n",
            "Results saved to \u001b[1mruns/predict-cls/exp9\u001b[0m\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DdGuG-1kNjWT"
      },
      "source": [
        "We can see the inference results show ~3ms inference and the respective classes predicted probabilities."
      ]
    }
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