YOLOv5_Classification_Tutorial tomato 1.ipynb

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      "source": [
        "<a href=\"https://colab.research.google.com/github/danielsoy/ALOCC-CVPR2018/blob/master/YOLOv5_Classification_Tutorial%20tomato%201.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": 1,
      "metadata": {
        "id": "pIM7fOwm8A7l",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e9facc39-23f2-43ca-b393-33e86f064177"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "YOLOv5 🚀 v7.0-72-g064365d Python-3.8.10 torch-1.13.1+cu116 CUDA:0 (Tesla T4, 15110MiB)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Setup complete ✅ (2 CPUs, 12.7 GB RAM, 23.5/78.2 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": "f5d4799f-4099-4e45-a08f-7ee62a465da0"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "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 | 21.84 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": [
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          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Downloading https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5n-cls.pt to weights/yolov5n-cls.pt...\n"
          ]
        },
        {
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          "text": [
            "\n",
            "Downloading https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s-cls.pt to weights/yolov5s-cls.pt...\n"
          ]
        },
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          "text": [
            "\n",
            "Downloading https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5m-cls.pt to weights/yolov5m-cls.pt...\n"
          ]
        },
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          "name": "stderr",
          "text": [
            "\n",
            "Downloading https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5l-cls.pt to weights/yolov5l-cls.pt...\n"
          ]
        },
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          "name": "stderr",
          "text": [
            "\n",
            "Downloading https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5x-cls.pt to weights/yolov5x-cls.pt...\n"
          ]
        },
        {
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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": 4,
      "metadata": {
        "id": "MXWTTN2BEaqe",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7694efe1-67db-42c1-9178-dfac03013e11"
      },
      "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 🚀 v7.0-72-g064365d Python-3.8.10 torch-1.13.1+cu116 CUDA:0 (Tesla T4, 15110MiB)\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/v7.0/yolov5n-cls.pt to yolov5n-cls.pt...\n",
            "100% 4.87M/4.87M [00:00<00:00, 139MB/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/exp\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    0.692G         1.5        2.21           0           1: 100% 36/36 [00:12<00:00,  2.90it/s]\n",
            "      2/30    0.847G        1.39        2.34           0           1: 100% 36/36 [00:11<00:00,  3.08it/s]\n",
            "      3/30    0.847G        1.36        1.63        0.38           1: 100% 36/36 [00:11<00:00,  3.06it/s]\n",
            "      4/30    0.847G        1.34        1.92      0.0803           1: 100% 36/36 [00:11<00:00,  3.07it/s]\n",
            "      5/30    0.847G        1.31        2.06       0.139           1: 100% 36/36 [00:11<00:00,  3.01it/s]\n",
            "      6/30    0.847G        1.27        1.21       0.642           1: 100% 36/36 [00:12<00:00,  3.00it/s]\n",
            "      7/30    0.847G        1.26        2.11      0.0876           1: 100% 36/36 [00:11<00:00,  3.03it/s]\n",
            "      8/30    0.847G        1.28         1.3       0.526           1: 100% 36/36 [00:11<00:00,  3.01it/s]\n",
            "      9/30    0.847G        1.24        2.45      0.0292           1: 100% 36/36 [00:11<00:00,  3.02it/s]\n",
            "     10/30    0.847G        1.24        1.89       0.131           1: 100% 36/36 [00:12<00:00,  2.99it/s]\n",
            "     11/30    0.847G        1.19        2.09       0.241           1: 100% 36/36 [00:12<00:00,  2.99it/s]\n",
            "     12/30    0.847G        1.19        2.72      0.0219           1: 100% 36/36 [00:11<00:00,  3.02it/s]\n",
            "     13/30    0.847G        1.19         2.3       0.168           1: 100% 36/36 [00:11<00:00,  3.00it/s]\n",
            "     14/30    0.847G        1.14        2.21       0.124           1: 100% 36/36 [00:12<00:00,  2.99it/s]\n",
            "     15/30    0.847G        1.12        1.83       0.321           1: 100% 36/36 [00:11<00:00,  3.04it/s]\n",
            "     16/30    0.847G        1.09        2.25       0.124           1: 100% 36/36 [00:12<00:00,  2.98it/s]\n",
            "     17/30    0.847G        1.07        2.16       0.175           1: 100% 36/36 [00:11<00:00,  3.03it/s]\n",
            "     18/30    0.847G        1.06        2.53       0.139           1: 100% 36/36 [00:11<00:00,  3.04it/s]\n",
            "     19/30    0.847G        1.05        2.12      0.0876           1: 100% 36/36 [00:11<00:00,  3.02it/s]\n",
            "     20/30    0.847G        1.02        1.96       0.161           1: 100% 36/36 [00:11<00:00,  3.04it/s]\n",
            "     21/30    0.847G        1.03        2.36       0.131           1: 100% 36/36 [00:11<00:00,  3.04it/s]\n",
            "     22/30    0.847G           1        2.51       0.131           1: 100% 36/36 [00:11<00:00,  3.07it/s]\n",
            "     23/30    0.847G       0.984        2.39       0.131           1: 100% 36/36 [00:11<00:00,  3.03it/s]\n",
            "     24/30    0.847G       0.972        2.59      0.0949           1: 100% 36/36 [00:11<00:00,  3.03it/s]\n",
            "     25/30    0.847G       0.976         2.9      0.0657           1: 100% 36/36 [00:11<00:00,  3.03it/s]\n",
            "     26/30    0.847G       0.963        3.31      0.0511           1: 100% 36/36 [00:12<00:00,  3.00it/s]\n",
            "     27/30    0.847G       0.924        3.05       0.102           1: 100% 36/36 [00:11<00:00,  3.06it/s]\n",
            "     28/30    0.847G       0.926        2.94       0.117           1: 100% 36/36 [00:11<00:00,  3.06it/s]\n",
            "     29/30    0.847G       0.918        2.96       0.109           1: 100% 36/36 [00:11<00:00,  3.02it/s]\n",
            "     30/30    0.847G       0.891         2.9       0.146           1: 100% 36/36 [00:11<00:00,  3.06it/s]\n",
            "\n",
            "Training complete (0.100 hours)\n",
            "Results saved to \u001b[1mruns/train-cls/exp\u001b[0m\n",
            "Predict:         python classify/predict.py --weights runs/train-cls/exp/weights/best.pt --source im.jpg\n",
            "Validate:        python classify/val.py --weights runs/train-cls/exp/weights/best.pt --data trash_classifier/dataset\n",
            "Export:          python export.py --weights runs/train-cls/exp/weights/best.pt --include onnx\n",
            "PyTorch Hub:     model = torch.hub.load('ultralytics/yolov5', 'custom', 'runs/train-cls/exp/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": 5,
      "metadata": {
        "id": "DIV7ydyKGZFL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c749fa91-32b5-44cd-a010-e5ae5db05645"
      },
      "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 🚀 v7.0-72-g064365d Python-3.8.10 torch-1.13.1+cu116 CUDA:0 (Tesla T4, 15110MiB)\n",
            "\n",
            "Traceback (most recent call last):\n",
            "  File \"classify/val.py\", line 170, in <module>\n",
            "    main(opt)\n",
            "  File \"classify/val.py\", line 165, in main\n",
            "    run(**vars(opt))\n",
            "  File \"/usr/local/lib/python3.8/dist-packages/torch/autograd/grad_mode.py\", line 27, in decorate_context\n",
            "    return func(*args, **kwargs)\n",
            "  File \"classify/val.py\", line 77, in run\n",
            "    model = DetectMultiBackend(weights, device=device, dnn=dnn, fp16=half)\n",
            "  File \"/content/yolov5/models/common.py\", line 345, in __init__\n",
            "    model = attempt_load(weights if isinstance(weights, list) else w, device=device, inplace=True, fuse=fuse)\n",
            "  File \"/content/yolov5/models/experimental.py\", line 79, in attempt_load\n",
            "    ckpt = torch.load(attempt_download(w), map_location='cpu')  # load\n",
            "  File \"/usr/local/lib/python3.8/dist-packages/torch/serialization.py\", line 771, in load\n",
            "    with _open_file_like(f, 'rb') as opened_file:\n",
            "  File \"/usr/local/lib/python3.8/dist-packages/torch/serialization.py\", line 270, in _open_file_like\n",
            "    return _open_file(name_or_buffer, mode)\n",
            "  File \"/usr/local/lib/python3.8/dist-packages/torch/serialization.py\", line 251, in __init__\n",
            "    super(_open_file, self).__init__(open(name, mode))\n",
            "FileNotFoundError: [Errno 2] No such file or directory: 'runs/train-cls/exp2/weights/best.pt'\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",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3a0a8c75-0159-418a-8f99-83370d99d402"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "NOTE: Using experimental fast data loading logic. To disable, pass\n",
            "    \"--load_fast=false\" and report issues on GitHub. More details:\n",
            "    https://github.com/tensorflow/tensorboard/issues/4784\n",
            "\n",
            "Serving TensorBoard on localhost; to expose to the network, use a proxy or pass --bind_all\n",
            "TensorBoard 2.9.1 at http://localhost:6006/ (Press CTRL+C to quit)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!ls runs/val-cls/exp"
      ],
      "metadata": {
        "id": "YfVJBMd3DO_2"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import os"
      ],
      "metadata": {
        "id": "Qx1D3HZELmhb"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install roboflow\n",
        "\n",
        "from roboflow import Roboflow\n",
        "rf = Roboflow(api_key=\"9ORhusQxYFimZzVYfjdC\")\n",
        "project = rf.workspace(\"new-workspace-lzcyx\").project(\"bow-pasta-yolo_v5\")\n",
        "dataset = project.version(1).download(\"folder\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "vQQZtMtlrks1",
        "outputId": "d7dcea82-355e-402f-983d-f43d1012ff96"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting roboflow\n",
            "  Downloading roboflow-0.2.25-py3-none-any.whl (46 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m46.5/46.5 KB\u001b[0m \u001b[31m6.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: chardet==4.0.0 in /usr/local/lib/python3.8/dist-packages (from roboflow) (4.0.0)\n",
            "Requirement already satisfied: tqdm>=4.41.0 in /usr/local/lib/python3.8/dist-packages (from roboflow) (4.64.1)\n",
            "Collecting python-dotenv\n",
            "  Downloading python_dotenv-0.21.1-py3-none-any.whl (19 kB)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.8/dist-packages (from roboflow) (1.15.0)\n",
            "Requirement already satisfied: idna==2.10 in /usr/local/lib/python3.8/dist-packages (from roboflow) (2.10)\n",
            "Collecting urllib3==1.26.6\n",
            "  Downloading urllib3-1.26.6-py2.py3-none-any.whl (138 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m138.5/138.5 KB\u001b[0m \u001b[31m15.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting cycler==0.10.0\n",
            "  Downloading cycler-0.10.0-py2.py3-none-any.whl (6.5 kB)\n",
            "Requirement already satisfied: Pillow>=7.1.2 in /usr/local/lib/python3.8/dist-packages (from roboflow) (7.1.2)\n",
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            "Requirement already satisfied: kiwisolver>=1.3.1 in /usr/local/lib/python3.8/dist-packages (from roboflow) (1.4.4)\n",
            "Requirement already satisfied: PyYAML>=5.3.1 in /usr/local/lib/python3.8/dist-packages (from roboflow) (6.0)\n",
            "Collecting pyparsing==2.4.7\n",
            "  Downloading pyparsing-2.4.7-py2.py3-none-any.whl (67 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m67.8/67.8 KB\u001b[0m \u001b[31m9.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting wget\n",
            "  Downloading wget-3.2.zip (10 kB)\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.8/dist-packages (from roboflow) (3.2.2)\n",
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            "Collecting requests-toolbelt\n",
            "  Downloading requests_toolbelt-0.10.1-py2.py3-none-any.whl (54 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m54.5/54.5 KB\u001b[0m \u001b[31m7.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: opencv-python-headless>=4.5.1.48 in /usr/local/lib/python3.8/dist-packages (from roboflow) (4.7.0.68)\n",
            "Requirement already satisfied: numpy>=1.18.5 in /usr/local/lib/python3.8/dist-packages (from roboflow) (1.21.6)\n",
            "Building wheels for collected packages: wget\n",
            "  Building wheel for wget (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for wget: filename=wget-3.2-py3-none-any.whl size=9674 sha256=fb18b78bd89f7ac3f5ec085eb3a22360af919bada1e1d8200ad835a83ac52a46\n",
            "  Stored in directory: /root/.cache/pip/wheels/bd/a8/c3/3cf2c14a1837a4e04bd98631724e81f33f462d86a1d895fae0\n",
            "Successfully built wget\n",
            "Installing collected packages: wget, urllib3, python-dotenv, pyparsing, cycler, requests-toolbelt, roboflow\n",
            "  Attempting uninstall: urllib3\n",
            "    Found existing installation: urllib3 1.24.3\n",
            "    Uninstalling urllib3-1.24.3:\n",
            "      Successfully uninstalled urllib3-1.24.3\n",
            "  Attempting uninstall: pyparsing\n",
            "    Found existing installation: pyparsing 3.0.9\n",
            "    Uninstalling pyparsing-3.0.9:\n",
            "      Successfully uninstalled pyparsing-3.0.9\n",
            "  Attempting uninstall: cycler\n",
            "    Found existing installation: cycler 0.11.0\n",
            "    Uninstalling cycler-0.11.0:\n",
            "      Successfully uninstalled cycler-0.11.0\n",
            "Successfully installed cycler-0.10.0 pyparsing-2.4.7 python-dotenv-0.21.1 requests-toolbelt-0.10.1 roboflow-0.2.25 urllib3-1.26.6 wget-3.2\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "cycler",
                  "pyparsing",
                  "urllib3"
                ]
              }
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "loading Roboflow workspace...\n",
            "loading Roboflow project...\n",
            "Downloading Dataset Version Zip in bow-pasta-yolo_v5-1 to folder: 100% [2708925 / 2708925] bytes\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Extracting Dataset Version Zip to bow-pasta-yolo_v5-1 in folder:: 100%|██████████| 335/335 [00:00<00:00, 1024.44it/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%cd /content/yolov5/bow-pasta-yolo_v5-1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zQEVCMjSsk-g",
        "outputId": "108b0aac-2653-46eb-ee5f-e916d976d219"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/yolov5/bow-pasta-yolo_v5-1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uH5tJNpEsi6g"
      },
      "source": [
        "### Infer With Your Custom Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "81lK1hU_sk54",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 134
        },
        "outputId": "a6b1a584-85cb-4375-eb27-b3d63d11b6e2"
      },
      "outputs": [
        {
          "output_type": "error",
          "ename": "SyntaxError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-13-cae99d2bc011>\"\u001b[0;36m, line \u001b[0;32m2\u001b[0m\n\u001b[0;31m    if os.path.exists(os.path.join(datasets/.location, \"test\")):\u001b[0m\n\u001b[0m                                            ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
          ]
        }
      ],
      "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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