YOLOv8-16bit/examples/tutorial.ipynb

{
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  "metadata": {
    "colab": {
      "name": "YOLOv8 Tutorial",
      "provenance": [],
      "toc_visible": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t6MPjfT5NrKQ"
      },
      "source": [
        "<div align=\"center\">\n",
        "\n",
        "  <a href=\"https://ultralytics.com/yolov8\" target=\"_blank\">\n",
        "    <img width=\"1024\", src=\"https://raw.githubusercontent.com/ultralytics/assets/main/yolov8/banner-yolov8.png\"></a>\n",
        "\n",
        "\n",
        "<br>\n",
        "  <a href=\"https://console.paperspace.com/github/ultralytics/ultralytics\"><img src=\"https://assets.paperspace.io/img/gradient-badge.svg\" alt=\"Run on Gradient\"/></a>\n",
        "  <a href=\"https://colab.research.google.com/github/ultralytics/ultralytics/blob/main/examples/tutorial.ipynb\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
        "  <a href=\"https://www.kaggle.com/ultralytics/yolov8\"><img src=\"https://kaggle.com/static/images/open-in-kaggle.svg\" alt=\"Open In Kaggle\"></a>\n",
        "<br>\n",
        "\n",
        "Welcome to the Ultralytics YOLOv8 🚀 notebook! <a href=\"https://github.com/ultralytics/ultralytics\">YOLOv8</a> is the latest version of the YOLO (You Only Look Once) object detection and image segmentation model developed by <a href=\"https://ultralytics.com\">Ultralytics</a>. This notebook serves as the starting point for exploring the various resources available to help you get started with YOLOv8 and understand its features and capabilities.\n",
        "\n",
        "The YOLOv8 models are designed to be fast, accurate, and easy to use, making them an excellent choice for a wide range of object detection and image segmentation tasks. They can be trained on large datasets and are capable of running on a variety of hardware platforms, from CPUs to GPUs.\n",
        "\n",
        "Whether you are a seasoned machine learning practitioner or new to the field, we hope that the resources in this notebook will help you get the most out of YOLOv8. Please feel free to browse the <a href=\"https://docs.ultralytics.com/\">YOLOv8 Docs</a> and reach out to us with any questions or feedback.\n",
        "\n",
        "</div>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7mGmQbAO5pQb"
      },
      "source": [
        "# Setup\n",
        "\n",
        "Pip install `ultralytics` and [dependencies](https://github.com/ultralytics/ultralytics/blob/main/requirements.txt) and check PyTorch and GPU."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wbvMlHd_QwMG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9bda69d4-e57f-404b-b6fe-117234e24677"
      },
      "source": [
        "# Pip install method (recommended)\n",
        "%pip install ultralytics\n",
        "import ultralytics\n",
        "ultralytics.checks()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Ultralytics YOLOv8.0.24 🚀 Python-3.8.10 torch-1.13.1+cu116 CUDA:0 (Tesla T4, 15110MiB)\n",
            "Setup complete ✅ (2 CPUs, 12.7 GB RAM, 30.8/166.8 GB disk)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Git clone method (for development)\n",
        "!git clone https://github.com/ultralytics/ultralytics\n",
        "%pip install -qe ultralytics"
      ],
      "metadata": {
        "id": "TUFPge7f_1ms"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4JnkELT0cIJg"
      },
      "source": [
        "# 1. Predict\n",
        "\n",
        "YOLOv8 may be used directly in the Command Line Interface (CLI) with a `yolo` command for a variety of tasks and modes and accepts additional arguments, i.e. `imgsz=640`. See a full list of available `yolo` [arguments](https://docs.ultralytics.com/cfg/) in the YOLOv8 [Docs](https://docs.ultralytics.com).\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zR9ZbuQCH7FX",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "abe002b5-3df9-4324-9e50-1587394398a2"
      },
      "source": [
        "# Run inference on an image with YOLOv8n\n",
        "!yolo predict model=yolov8n.pt source='https://ultralytics.com/images/zidane.jpg'"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n.pt to yolov8n.pt...\n",
            "\r  0% 0.00/6.23M [00:00<?, ?B/s]\r100% 6.23M/6.23M [00:00<00:00, 266MB/s]\n",
            "Ultralytics YOLOv8.0.24 🚀 Python-3.8.10 torch-1.13.1+cu116 CUDA:0 (Tesla T4, 15110MiB)\n",
            "YOLOv8n summary (fused): 168 layers, 3151904 parameters, 0 gradients, 8.7 GFLOPs\n",
            "\n",
            "Downloading https://ultralytics.com/images/zidane.jpg to zidane.jpg...\n",
            "100% 165k/165k [00:00<00:00, 87.4MB/s]\n",
            "image 1/1 /content/zidane.jpg: 384x640 2 persons, 1 tie, 13.3ms\n",
            "Speed: 0.5ms pre-process, 13.3ms inference, 43.5ms postprocess per image at shape (1, 3, 640, 640)\n",
            "Results saved to \u001b[1mruns/detect/predict\u001b[0m\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hkAzDWJ7cWTr"
      },
      "source": [
        "&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;\n",
        "<img align=\"left\" src=\"https://user-images.githubusercontent.com/26833433/212889447-69e5bdf1-5800-4e29-835e-2ed2336dede2.jpg\" width=\"600\">"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0eq1SMWl6Sfn"
      },
      "source": [
        "# 2. Val\n",
        "Validate a model's accuracy on the [COCO](https://cocodataset.org/#home) dataset's `val` or `test` splits. The latest YOLOv8 [models](https://github.com/ultralytics/ultralytics#models) are downloaded automatically the first time they are used."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WQPtK1QYVaD_"
      },
      "source": [
        "# Download COCO val\n",
        "import torch\n",
        "torch.hub.download_url_to_file('https://ultralytics.com/assets/coco2017val.zip', 'tmp.zip')  # download (780M - 5000 images)\n",
        "!unzip -q tmp.zip -d ../datasets && rm tmp.zip  # unzip"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X58w8JLpMnjH",
        "outputId": "df71d7a8-f42f-473a-c143-75f033c58433",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# Validate YOLOv8n on COCO128 val\n",
        "!yolo val model=yolov8n.pt data=coco128.yaml"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Ultralytics YOLOv8.0.24 🚀 Python-3.8.10 torch-1.13.1+cu116 CUDA:0 (Tesla T4, 15110MiB)\n",
            "YOLOv8n summary (fused): 168 layers, 3151904 parameters, 0 gradients, 8.7 GFLOPs\n",
            "\n",
            "Dataset 'coco128.yaml' not found ⚠️, missing paths ['/content/datasets/coco128/images/train2017']\n",
            "Downloading https://ultralytics.com/assets/coco128.zip to /content/datasets/coco128.zip...\n",
            "100% 6.66M/6.66M [00:00<00:00, 50.5MB/s]\n",
            "Unzipping /content/datasets/coco128.zip...\n",
            "Dataset download success ✅ (0.5s), saved to \u001b[1m/content/datasets\u001b[0m\n",
            "\n",
            "Downloading https://ultralytics.com/assets/Arial.ttf to /root/.config/Ultralytics/Arial.ttf...\n",
            "100% 755k/755k [00:00<00:00, 99.8MB/s]\n",
            "\u001b[34m\u001b[1mval: \u001b[0mScanning /content/datasets/coco128/labels/train2017... 126 images, 2 backgrounds, 0 corrupt: 100% 128/128 [00:00<00:00, 1290.29it/s]\n",
            "\u001b[34m\u001b[1mval: \u001b[0mNew cache created: /content/datasets/coco128/labels/train2017.cache\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 8/8 [00:06<00:00,  1.22it/s]\n",
            "                   all        128        929       0.64      0.537      0.605      0.446\n",
            "                person        128        254      0.797      0.677      0.764      0.538\n",
            "               bicycle        128          6      0.514      0.333      0.315      0.264\n",
            "                   car        128         46      0.813      0.217      0.273      0.168\n",
            "            motorcycle        128          5      0.687      0.887      0.898      0.685\n",
            "              airplane        128          6       0.82      0.833      0.927      0.675\n",
            "                   bus        128          7      0.491      0.714      0.728      0.671\n",
            "                 train        128          3      0.534      0.667      0.706      0.604\n",
            "                 truck        128         12          1      0.332      0.473      0.297\n",
            "                  boat        128          6      0.226      0.167      0.316      0.134\n",
            "         traffic light        128         14      0.734        0.2      0.202      0.139\n",
            "             stop sign        128          2          1      0.992      0.995      0.701\n",
            "                 bench        128          9      0.839      0.582       0.62      0.365\n",
            "                  bird        128         16      0.921      0.728      0.864       0.51\n",
            "                   cat        128          4      0.875          1      0.995      0.791\n",
            "                   dog        128          9      0.603      0.889      0.785      0.585\n",
            "                 horse        128          2      0.597          1      0.995      0.518\n",
            "              elephant        128         17      0.849      0.765        0.9      0.679\n",
            "                  bear        128          1      0.593          1      0.995      0.995\n",
            "                 zebra        128          4      0.848          1      0.995      0.965\n",
            "               giraffe        128          9       0.72          1      0.951      0.722\n",
            "              backpack        128          6      0.589      0.333      0.376      0.232\n",
            "              umbrella        128         18      0.804        0.5      0.643      0.414\n",
            "               handbag        128         19      0.424     0.0526      0.165     0.0889\n",
            "                   tie        128          7      0.804      0.714      0.674      0.476\n",
            "              suitcase        128          4      0.635      0.883      0.745      0.534\n",
            "               frisbee        128          5      0.675        0.8      0.759      0.688\n",
            "                  skis        128          1      0.567          1      0.995      0.497\n",
            "             snowboard        128          7      0.742      0.714      0.747        0.5\n",
            "           sports ball        128          6      0.716      0.433      0.485      0.278\n",
            "                  kite        128         10      0.817       0.45      0.569      0.184\n",
            "          baseball bat        128          4      0.551       0.25      0.353      0.175\n",
            "        baseball glove        128          7      0.624      0.429      0.429      0.293\n",
            "            skateboard        128          5      0.846        0.6        0.6       0.41\n",
            "         tennis racket        128          7      0.726      0.387      0.487       0.33\n",
            "                bottle        128         18      0.448      0.389      0.376      0.208\n",
            "            wine glass        128         16      0.743      0.362      0.584      0.333\n",
            "                   cup        128         36       0.58      0.278      0.404       0.29\n",
            "                  fork        128          6      0.527      0.167      0.246      0.184\n",
            "                 knife        128         16      0.564        0.5       0.59       0.36\n",
            "                 spoon        128         22      0.597      0.182      0.328       0.19\n",
            "                  bowl        128         28      0.648      0.643      0.618      0.491\n",
            "                banana        128          1          0          0      0.124     0.0379\n",
            "              sandwich        128          2      0.249        0.5      0.308      0.308\n",
            "                orange        128          4          1       0.31      0.995      0.623\n",
            "              broccoli        128         11      0.374      0.182      0.249      0.203\n",
            "                carrot        128         24      0.648      0.458      0.572      0.362\n",
            "               hot dog        128          2      0.351      0.553      0.745      0.721\n",
            "                 pizza        128          5      0.644          1      0.995      0.843\n",
            "                 donut        128         14      0.657          1       0.94      0.864\n",
            "                  cake        128          4      0.618          1      0.945      0.845\n",
            "                 chair        128         35      0.506      0.514      0.442      0.239\n",
            "                 couch        128          6      0.463        0.5      0.706      0.555\n",
            "          potted plant        128         14       0.65      0.643      0.711      0.472\n",
            "                   bed        128          3      0.698      0.667      0.789      0.625\n",
            "          dining table        128         13      0.432      0.615      0.485      0.366\n",
            "                toilet        128          2      0.615        0.5      0.695      0.676\n",
            "                    tv        128          2      0.373       0.62      0.745      0.696\n",
            "                laptop        128          3          1          0      0.451      0.361\n",
            "                 mouse        128          2          1          0     0.0625    0.00625\n",
            "                remote        128          8      0.843        0.5      0.605      0.529\n",
            "            cell phone        128          8          0          0     0.0549     0.0393\n",
            "             microwave        128          3      0.435      0.667      0.806      0.718\n",
            "                  oven        128          5      0.412        0.4      0.339       0.27\n",
            "                  sink        128          6       0.35      0.167      0.182      0.129\n",
            "          refrigerator        128          5      0.589        0.4      0.604      0.452\n",
            "                  book        128         29      0.629      0.103      0.346      0.178\n",
            "                 clock        128          9      0.788       0.83      0.875       0.74\n",
            "                  vase        128          2      0.376          1      0.828      0.795\n",
            "              scissors        128          1          1          0      0.249     0.0746\n",
            "            teddy bear        128         21      0.877      0.333      0.591      0.394\n",
            "            toothbrush        128          5      0.743        0.6      0.638      0.374\n",
            "Speed: 2.4ms pre-process, 7.8ms inference, 0.0ms loss, 3.3ms post-process per image\n"
          ]
        }
      ]
    },
    {
      "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://github.com/ultralytics/assets/raw/main/yolov8/banner-integrations.png\"/></a></p>\n",
        "\n",
        "Train YOLOv8 on [Detection](https://docs.ultralytics.com/tasks/detection/), [Segmentation](https://docs.ultralytics.com/tasks/segmentation/) and [Classification](https://docs.ultralytics.com/tasks/classification/) datasets."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1NcFxRcFdJ_O",
        "outputId": "e0978a9a-ef1d-4a20-8082-19c8049d8c7e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# Train YOLOv8n on COCO128 for 3 epochs\n",
        "!yolo train model=yolov8n.pt data=coco128.yaml epochs=3 imgsz=640"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Ultralytics YOLOv8.0.24 🚀 Python-3.8.10 torch-1.13.1+cu116 CUDA:0 (Tesla T4, 15110MiB)\n",
            "\u001b[34m\u001b[1myolo/engine/trainer: \u001b[0mtask=detect, mode=train, model=yolov8n.pt, data=coco128.yaml, epochs=3, patience=50, batch=16, imgsz=640, save=True, cache=False, device=None, workers=8, project=None, name=None, exist_ok=False, pretrained=False, optimizer=SGD, verbose=True, seed=0, deterministic=True, single_cls=False, image_weights=False, rect=False, cos_lr=False, close_mosaic=10, resume=False, overlap_mask=True, mask_ratio=4, dropout=0.0, val=True, save_json=False, save_hybrid=False, conf=None, iou=0.7, max_det=300, half=False, dnn=False, plots=True, source=None, show=False, save_txt=False, save_conf=False, save_crop=False, hide_labels=False, hide_conf=False, vid_stride=1, line_thickness=3, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, boxes=True, format=torchscript, keras=False, optimize=False, int8=False, dynamic=False, simplify=False, opset=None, workspace=4, nms=False, lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=7.5, cls=0.5, dfl=1.5, fl_gamma=0.0, label_smoothing=0.0, nbs=64, 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, cfg=None, v5loader=False, save_dir=runs/detect/train\n",
            "\n",
            "                   from  n    params  module                                       arguments                     \n",
            "  0                  -1  1       464  ultralytics.nn.modules.Conv                  [3, 16, 3, 2]                 \n",
            "  1                  -1  1      4672  ultralytics.nn.modules.Conv                  [16, 32, 3, 2]                \n",
            "  2                  -1  1      7360  ultralytics.nn.modules.C2f                   [32, 32, 1, True]             \n",
            "  3                  -1  1     18560  ultralytics.nn.modules.Conv                  [32, 64, 3, 2]                \n",
            "  4                  -1  2     49664  ultralytics.nn.modules.C2f                   [64, 64, 2, True]             \n",
            "  5                  -1  1     73984  ultralytics.nn.modules.Conv                  [64, 128, 3, 2]               \n",
            "  6                  -1  2    197632  ultralytics.nn.modules.C2f                   [128, 128, 2, True]           \n",
            "  7                  -1  1    295424  ultralytics.nn.modules.Conv                  [128, 256, 3, 2]              \n",
            "  8                  -1  1    460288  ultralytics.nn.modules.C2f                   [256, 256, 1, True]           \n",
            "  9                  -1  1    164608  ultralytics.nn.modules.SPPF                  [256, 256, 5]                 \n",
            " 10                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          \n",
            " 11             [-1, 6]  1         0  ultralytics.nn.modules.Concat                [1]                           \n",
            " 12                  -1  1    148224  ultralytics.nn.modules.C2f                   [384, 128, 1]                 \n",
            " 13                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          \n",
            " 14             [-1, 4]  1         0  ultralytics.nn.modules.Concat                [1]                           \n",
            " 15                  -1  1     37248  ultralytics.nn.modules.C2f                   [192, 64, 1]                  \n",
            " 16                  -1  1     36992  ultralytics.nn.modules.Conv                  [64, 64, 3, 2]                \n",
            " 17            [-1, 12]  1         0  ultralytics.nn.modules.Concat                [1]                           \n",
            " 18                  -1  1    123648  ultralytics.nn.modules.C2f                   [192, 128, 1]                 \n",
            " 19                  -1  1    147712  ultralytics.nn.modules.Conv                  [128, 128, 3, 2]              \n",
            " 20             [-1, 9]  1         0  ultralytics.nn.modules.Concat                [1]                           \n",
            " 21                  -1  1    493056  ultralytics.nn.modules.C2f                   [384, 256, 1]                 \n",
            " 22        [15, 18, 21]  1    897664  ultralytics.nn.modules.Detect                [80, [64, 128, 256]]          \n",
            "Model summary: 225 layers, 3157200 parameters, 3157184 gradients, 8.9 GFLOPs\n",
            "\n",
            "Transferred 355/355 items from pretrained weights\n",
            "\u001b[34m\u001b[1moptimizer:\u001b[0m SGD(lr=0.01) with parameter groups 57 weight(decay=0.0), 64 weight(decay=0.0005), 63 bias\n",
            "\u001b[34m\u001b[1mtrain: \u001b[0mScanning /content/datasets/coco128/labels/train2017.cache... 126 images, 2 backgrounds, 0 corrupt: 100% 128/128 [00:00<?, ?it/s]\n",
            "\u001b[34m\u001b[1malbumentations: \u001b[0mBlur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01), CLAHE(p=0.01, clip_limit=(1, 4.0), tile_grid_size=(8, 8))\n",
            "\u001b[34m\u001b[1mval: \u001b[0mScanning /content/datasets/coco128/labels/train2017.cache... 126 images, 2 backgrounds, 0 corrupt: 100% 128/128 [00:00<?, ?it/s]\n",
            "Image sizes 640 train, 640 val\n",
            "Using 2 dataloader workers\n",
            "Logging results to \u001b[1mruns/detect/train\u001b[0m\n",
            "Starting training for 3 epochs...\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "        1/3      4.31G      1.221      1.429      1.241        196        640: 100% 8/8 [00:08<00:00,  1.01s/it]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 4/4 [00:01<00:00,  2.18it/s]\n",
            "                   all        128        929      0.671      0.516      0.617      0.457\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "        2/3      5.31G      1.186      1.306      1.255        287        640: 100% 8/8 [00:04<00:00,  1.63it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 4/4 [00:01<00:00,  2.23it/s]\n",
            "                   all        128        929      0.668      0.582      0.637      0.473\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "        3/3      5.31G       1.17      1.408      1.267        189        640: 100% 8/8 [00:04<00:00,  1.64it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 4/4 [00:03<00:00,  1.03it/s]\n",
            "                   all        128        929      0.638      0.601      0.645      0.483\n",
            "\n",
            "3 epochs completed in 0.009 hours.\n",
            "Optimizer stripped from runs/detect/train/weights/last.pt, 6.5MB\n",
            "Optimizer stripped from runs/detect/train/weights/best.pt, 6.5MB\n",
            "\n",
            "Validating runs/detect/train/weights/best.pt...\n",
            "Ultralytics YOLOv8.0.24 🚀 Python-3.8.10 torch-1.13.1+cu116 CUDA:0 (Tesla T4, 15110MiB)\n",
            "Model summary (fused): 168 layers, 3151904 parameters, 0 gradients, 8.7 GFLOPs\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 4/4 [00:04<00:00,  1.05s/it]\n",
            "                   all        128        929      0.638      0.602      0.644      0.483\n",
            "                person        128        254      0.703      0.709      0.769      0.548\n",
            "               bicycle        128          6      0.455      0.333      0.322      0.254\n",
            "                   car        128         46      0.773      0.217      0.291      0.184\n",
            "            motorcycle        128          5      0.551        0.8      0.895      0.724\n",
            "              airplane        128          6      0.743      0.833      0.927       0.73\n",
            "                   bus        128          7      0.692      0.714        0.7      0.636\n",
            "                 train        128          3      0.733      0.931      0.913      0.797\n",
            "                 truck        128         12      0.752        0.5      0.497      0.324\n",
            "                  boat        128          6       0.41      0.333      0.492      0.344\n",
            "         traffic light        128         14      0.682      0.214      0.202      0.139\n",
            "             stop sign        128          2      0.933          1      0.995      0.671\n",
            "                 bench        128          9      0.752      0.556      0.603      0.416\n",
            "                  bird        128         16      0.875      0.876      0.957      0.641\n",
            "                   cat        128          4      0.863          1      0.995       0.76\n",
            "                   dog        128          9      0.554      0.778      0.855      0.664\n",
            "                 horse        128          2      0.706          1      0.995      0.561\n",
            "              elephant        128         17      0.761      0.882      0.929      0.722\n",
            "                  bear        128          1      0.595          1      0.995      0.995\n",
            "                 zebra        128          4       0.85          1      0.995      0.966\n",
            "               giraffe        128          9      0.891          1      0.995      0.683\n",
            "              backpack        128          6      0.487      0.333      0.354      0.224\n",
            "              umbrella        128         18       0.54      0.667      0.687      0.461\n",
            "               handbag        128         19      0.496      0.105      0.212      0.125\n",
            "                   tie        128          7      0.611      0.714      0.615      0.432\n",
            "              suitcase        128          4      0.469          1      0.745      0.529\n",
            "               frisbee        128          5      0.622        0.8      0.733       0.64\n",
            "                  skis        128          1      0.721          1      0.995      0.531\n",
            "             snowboard        128          7      0.687      0.714      0.751       0.51\n",
            "           sports ball        128          6       0.71       0.42      0.503      0.282\n",
            "                  kite        128         10       0.81        0.5       0.59      0.197\n",
            "          baseball bat        128          4      0.474      0.461      0.261      0.115\n",
            "        baseball glove        128          7       0.67      0.429       0.43      0.317\n",
            "            skateboard        128          5      0.751        0.6      0.599      0.387\n",
            "         tennis racket        128          7      0.742      0.415      0.507      0.378\n",
            "                bottle        128         18      0.409      0.333      0.354      0.235\n",
            "            wine glass        128         16      0.562        0.5      0.597      0.356\n",
            "                   cup        128         36       0.67      0.306      0.411      0.296\n",
            "                  fork        128          6       0.57      0.167      0.229      0.203\n",
            "                 knife        128         16      0.608      0.562      0.634      0.405\n",
            "                 spoon        128         22      0.529      0.358      0.369      0.201\n",
            "                  bowl        128         28      0.594      0.679      0.671       0.56\n",
            "                banana        128          1     0.0625      0.312      0.199     0.0513\n",
            "              sandwich        128          2      0.638      0.913      0.828      0.828\n",
            "                orange        128          4      0.743      0.728      0.895      0.595\n",
            "              broccoli        128         11       0.49      0.264      0.278      0.232\n",
            "                carrot        128         24      0.547      0.667      0.704       0.47\n",
            "               hot dog        128          2      0.578          1      0.828      0.796\n",
            "                 pizza        128          5      0.835          1      0.995       0.84\n",
            "                 donut        128         14      0.537          1      0.891      0.788\n",
            "                  cake        128          4      0.807          1      0.995      0.904\n",
            "                 chair        128         35      0.401      0.514      0.485      0.277\n",
            "                 couch        128          6      0.795      0.649      0.746      0.504\n",
            "          potted plant        128         14      0.563      0.643      0.676      0.471\n",
            "                   bed        128          3      0.777          1      0.995      0.735\n",
            "          dining table        128         13      0.425      0.692      0.578       0.48\n",
            "                toilet        128          2      0.508        0.5      0.745      0.721\n",
            "                    tv        128          2       0.55      0.649      0.828      0.762\n",
            "                laptop        128          3          1          0      0.741      0.653\n",
            "                 mouse        128          2          1          0     0.0454    0.00907\n",
            "                remote        128          8       0.83        0.5      0.569      0.449\n",
            "            cell phone        128          8          0          0     0.0819     0.0266\n",
            "             microwave        128          3      0.475      0.667       0.83      0.699\n",
            "                  oven        128          5        0.5        0.4      0.348      0.275\n",
            "                  sink        128          6      0.354      0.187      0.368      0.217\n",
            "          refrigerator        128          5      0.518        0.4      0.729      0.571\n",
            "                  book        128         29      0.583      0.241      0.396      0.204\n",
            "                 clock        128          9      0.891      0.889       0.91      0.773\n",
            "                  vase        128          2      0.506          1      0.828      0.745\n",
            "              scissors        128          1          1          0      0.142     0.0426\n",
            "            teddy bear        128         21      0.587      0.476       0.63      0.458\n",
            "            toothbrush        128          5      0.784      0.736      0.898      0.544\n",
            "Speed: 2.0ms pre-process, 4.0ms inference, 0.0ms loss, 2.5ms post-process per image\n",
            "Results saved to \u001b[1mruns/detect/train\u001b[0m\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 4. Export\n",
        "\n",
        "Export a YOLOv8 model to any supported format with the `format` argument, i.e. `format=onnx`.\n",
        "\n",
        "- 💡 ProTip: Export to [ONNX](https://onnx.ai/) or [OpenVINO](https://docs.openvino.ai/latest/index.html) for up to 3x CPU speedup.  \n",
        "- 💡 ProTip: Export to [TensorRT](https://developer.nvidia.com/tensorrt) for up to 5x GPU speedup.\n",
        "\n",
        "\n",
        "| Format                                                                     | `format=`          | Model                     |\n",
        "|----------------------------------------------------------------------------|--------------------|---------------------------|\n",
        "| [PyTorch](https://pytorch.org/)                                            | -                  | `yolov8n.pt`              |\n",
        "| [TorchScript](https://pytorch.org/docs/stable/jit.html)                    | `torchscript`      | `yolov8n.torchscript`     |\n",
        "| [ONNX](https://onnx.ai/)                                                   | `onnx`             | `yolov8n.onnx`            |\n",
        "| [OpenVINO](https://docs.openvino.ai/latest/index.html)                     | `openvino`         | `yolov8n_openvino_model/` |\n",
        "| [TensorRT](https://developer.nvidia.com/tensorrt)                          | `engine`           | `yolov8n.engine`          |\n",
        "| [CoreML](https://github.com/apple/coremltools)                             | `coreml`           | `yolov8n.mlmodel`         |\n",
        "| [TensorFlow SavedModel](https://www.tensorflow.org/guide/saved_model)      | `saved_model`      | `yolov8n_saved_model/`    |\n",
        "| [TensorFlow GraphDef](https://www.tensorflow.org/api_docs/python/tf/Graph) | `pb`               | `yolov8n.pb`              |\n",
        "| [TensorFlow Lite](https://www.tensorflow.org/lite)                         | `tflite`           | `yolov8n.tflite`          |\n",
        "| [TensorFlow Edge TPU](https://coral.ai/docs/edgetpu/models-intro/)         | `edgetpu`          | `yolov8n_edgetpu.tflite`  |\n",
        "| [TensorFlow.js](https://www.tensorflow.org/js)                             | `tfjs`             | `yolov8n_web_model/`      |\n",
        "| [PaddlePaddle](https://github.com/PaddlePaddle)                            | `paddle`           | `yolov8n_paddle_model/`   |\n",
        "\n"
      ],
      "metadata": {
        "id": "nPZZeNrLCQG6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!yolo export model=yolov8n.pt format=torchscript"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CYIjW4igCjqD",
        "outputId": "69cab2fb-cbfa-4acf-8e29-9c4fb6f4a38f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Ultralytics YOLOv8.0.24 🚀 Python-3.8.10 torch-1.13.1+cu116 CPU\n",
            "YOLOv8n summary (fused): 168 layers, 3151904 parameters, 0 gradients, 8.7 GFLOPs\n",
            "\n",
            "\u001b[34m\u001b[1mPyTorch:\u001b[0m starting from yolov8n.pt with output shape (1, 84, 8400) (6.2 MB)\n",
            "\n",
            "\u001b[34m\u001b[1mTorchScript:\u001b[0m starting export with torch 1.13.1+cu116...\n",
            "\u001b[34m\u001b[1mTorchScript:\u001b[0m export success ✅ 1.7s, saved as yolov8n.torchscript (12.4 MB)\n",
            "\n",
            "Export complete (2.2s)\n",
            "Results saved to \u001b[1m/content\u001b[0m\n",
            "Predict:         yolo task=detect mode=predict model=yolov8n.torchscript -WARNING ⚠️ not yet supported for YOLOv8 exported models\n",
            "Validate:        yolo task=detect mode=val model=yolov8n.torchscript -WARNING ⚠️ not yet supported for YOLOv8 exported models\n",
            "Visualize:       https://netron.app\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 5. Python Usage\n",
        "\n",
        "YOLOv8 was reimagined using Python-first principles for the most seamless Python YOLO experience yet. YOLOv8 models can be loaded from a trained checkpoint or created from scratch. Then methods are used to train, val, predict, and export the model. See a detailed Python usage examples in the YOLOv8 [Docs](https://docs.ultralytics.com/python/)."
      ],
      "metadata": {
        "id": "kUMOQ0OeDBJG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from ultralytics import YOLO\n",
        "\n",
        "# Load a model\n",
        "model = YOLO('yolov8n.yaml')  # build a new model from scratch\n",
        "model = YOLO('yolov8n.pt')  # load a pretrained model (recommended for training)\n",
        "\n",
        "# Use the model\n",
        "results = model.train(data='coco128.yaml', epochs=3)  # train the model\n",
        "results = model.val()  # evaluate model performance on the validation set\n",
        "results = model('https://ultralytics.com/images/bus.jpg')  # predict on an image\n",
        "success = model.export(format='onnx')  # export the model to ONNX format"
      ],
      "metadata": {
        "id": "bpF9-vS_DAaf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 6. Tasks\n",
        "\n",
        "YOLOv8 can train, val, predict and export models for the 3 primary tasks in vision AI: detection, segmentation and classification.\n",
        "\n",
        "<img width=\"1024\" src=\"https://user-images.githubusercontent.com/26833433/212094133-6bb8c21c-3d47-41df-a512-81c5931054ae.png\">\n"
      ],
      "metadata": {
        "id": "Phm9ccmOKye5"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 1. Detection\n",
        "\n",
        "YOLOv8 _detection_ models have no suffix and are the default YOLOv8 models, i.e. `yolov8n.pt` and are pretrained on COCO. See [Detection Docs](https://docs.ultralytics.com/tasks/detection/) for full details.\n"
      ],
      "metadata": {
        "id": "yq26lwpYK1lq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Load YOLOv8n, train it on COCO128 for 3 epochs and predict an image with it\n",
        "from ultralytics import YOLO\n",
        "\n",
        "model = YOLO('yolov8n.pt')  # load a pretrained YOLOv8n detection model\n",
        "model.train(data='coco128.yaml', epochs=3)  # train the model\n",
        "model('https://ultralytics.com/images/bus.jpg')  # predict on an image"
      ],
      "metadata": {
        "id": "8Go5qqS9LbC5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 2. Segmentation\n",
        "\n",
        "YOLOv8 _segmentation_ models use the `-seg` suffix, i.e. `yolov8n-seg.pt` and are pretrained on COCO. See [Segmentation Docs](https://docs.ultralytics.com/tasks/segmentation/) for full details.\n"
      ],
      "metadata": {
        "id": "7ZW58jUzK66B"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Load YOLOv8n-seg, train it on COCO128-seg for 3 epochs and predict an image with it\n",
        "from ultralytics import YOLO\n",
        "\n",
        "model = YOLO('yolov8n-seg.pt')  # load a pretrained YOLOv8n segmentation model\n",
        "model.train(data='coco128-seg.yaml', epochs=3)  # train the model\n",
        "model('https://ultralytics.com/images/bus.jpg')  # predict on an image"
      ],
      "metadata": {
        "id": "WFPJIQl_L5HT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 3. Classification\n",
        "\n",
        "YOLOv8 _classification_ models use the `-cls` suffix, i.e. `yolov8n-cls.pt` and are pretrained on ImageNet. See [Classification Docs](https://docs.ultralytics.com/tasks/classification/) for full details.\n"
      ],
      "metadata": {
        "id": "ax3p94VNK9zR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Load YOLOv8n-cls, train it on mnist160 for 3 epochs and predict an image with it\n",
        "from ultralytics import YOLO\n",
        "\n",
        "model = YOLO('yolov8n-cls.pt')  # load a pretrained YOLOv8n classification model\n",
        "model.train(data='mnist160', epochs=3)  # train the model\n",
        "model('https://ultralytics.com/images/bus.jpg')  # predict on an image"
      ],
      "metadata": {
        "id": "5q9Zu6zlL5rS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IEijrePND_2I"
      },
      "source": [
        "# Appendix\n",
        "\n",
        "Additional content below."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GMusP4OAxFu6"
      },
      "source": [
        "# Run YOLOv8 tests (git clone install only)\n",
        "!pytest ultralytics/tests"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Validate multiple models\n",
        "for x in 'nsmlx':\n",
        "  !yolo val model=yolov8{x}.pt data=coco.yaml"
      ],
      "metadata": {
        "id": "Wdc6t_bfzDDk"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}