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yolov8_test/examples/tutorial.ipynb

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{
"nbformat": 4,
"nbformat_minor": 0,
"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": 1,
"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": 2,
"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": 3,
"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": 4,
"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": 5,
"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": []
}
]
}