You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
705 lines
44 KiB
705 lines
44 KiB
{
|
|
"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) AI models 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",
|
|
"YOLOv8 models are fast, accurate, and easy to use, making them ideal for various object detection and image segmentation tasks. They can be trained on large datasets and run on diverse hardware platforms, from CPUs to GPUs.\n",
|
|
"\n",
|
|
"We hope that the resources in this notebook will help you get the most out of YOLOv8. Please browse the YOLOv8 <a href=\"https://docs.ultralytics.com/\">Docs</a> for details, raise an issue on <a href=\"https://github.com/ultralytics/ultralytics\">GitHub</a> for support, and join our <a href=\"https://discord.gg/7aegy5d8\">Discord</a> community for questions and discussions!\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 software and hardware."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"metadata": {
|
|
"id": "wbvMlHd_QwMG",
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/"
|
|
},
|
|
"outputId": "2ea6e0b9-1a62-4355-c246-5e8b7b1dafff"
|
|
},
|
|
"source": [
|
|
"%pip install ultralytics\n",
|
|
"import ultralytics\n",
|
|
"ultralytics.checks()"
|
|
],
|
|
"execution_count": 1,
|
|
"outputs": [
|
|
{
|
|
"output_type": "stream",
|
|
"name": "stderr",
|
|
"text": [
|
|
"Ultralytics YOLOv8.0.71 🚀 Python-3.9.16 torch-2.0.0+cu118 CUDA:0 (Tesla T4, 15102MiB)\n",
|
|
"Setup complete ✅ (2 CPUs, 12.7 GB RAM, 23.3/166.8 GB disk)\n"
|
|
]
|
|
}
|
|
]
|
|
},
|
|
{
|
|
"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/usage/cfg/) and other details in the [YOLOv8 Predict Docs](https://docs.ultralytics.com/modes/train/).\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"metadata": {
|
|
"id": "zR9ZbuQCH7FX",
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/"
|
|
},
|
|
"outputId": "c578afbd-47cd-4d11-beec-8b5c31fcfba8"
|
|
},
|
|
"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",
|
|
"100% 6.23M/6.23M [00:00<00:00, 195MB/s]\n",
|
|
"Ultralytics YOLOv8.0.71 🚀 Python-3.9.16 torch-2.0.0+cu118 CUDA:0 (Tesla T4, 15102MiB)\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, 51.7MB/s]\n",
|
|
"image 1/1 /content/zidane.jpg: 384x640 2 persons, 1 tie, 60.9ms\n",
|
|
"Speed: 0.6ms preprocess, 60.9ms inference, 301.3ms 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": [
|
|
" \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. See [YOLOv8 Val Docs](https://docs.ultralytics.com/modes/val/) for more information."
|
|
]
|
|
},
|
|
{
|
|
"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": "3e5a9c48-8eba-45eb-d92f-8456cf94b60e",
|
|
"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 YOLOv8.0.71 🚀 Python-3.9.16 torch-2.0.0+cu118 CUDA:0 (Tesla T4, 15102MiB)\n",
|
|
"YOLOv8n summary (fused): 168 layers, 3151904 parameters, 0 gradients, 8.7 GFLOPs\n",
|
|
"\n",
|
|
"Dataset 'coco128.yaml' images 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 [00:01<00:00, 6.80MB/s]\n",
|
|
"Unzipping /content/datasets/coco128.zip to /content/datasets...\n",
|
|
"Dataset download success ✅ (2.2s), 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, 107MB/s]\n",
|
|
"\u001b[34m\u001b[1mval: \u001b[0mScanning /content/datasets/coco128/labels/train2017... 126 images, 2 backgrounds, 80 corrupt: 100% 128/128 [00:00<00:00, 1183.28it/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 [00:12<00:00, 1.54s/it]\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: 5.3ms preprocess, 20.1ms inference, 0.0ms loss, 11.7ms postprocess per image\n",
|
|
"Results saved to \u001b[1mruns/detect/val\u001b[0m\n"
|
|
]
|
|
}
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"id": "ZY2VXXXu74w5"
|
|
},
|
|
"source": [
|
|
"# 3. Train\n",
|
|
"\n",
|
|
"<p align=\"\"><a href=\"https://bit.ly/ultralytics_hub\"><img width=\"1000\" src=\"https://github.com/ultralytics/assets/raw/main/yolov8/banner-integrations.png\"/></a></p>\n",
|
|
"\n",
|
|
"Train YOLOv8 on [Detect](https://docs.ultralytics.com/tasks/detect/), [Segment](https://docs.ultralytics.com/tasks/segment/), [Classify](https://docs.ultralytics.com/tasks/classify/) and [Pose](https://docs.ultralytics.com/tasks/pose/) datasets. See [YOLOv8 Train Docs](https://docs.ultralytics.com/modes/train/) for more information."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"metadata": {
|
|
"id": "1NcFxRcFdJ_O",
|
|
"outputId": "b60a1f74-8035-4f9e-b4b0-604f9cf76231",
|
|
"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 YOLOv8.0.71 🚀 Python-3.9.16 torch-2.0.0+cu118 CUDA:0 (Tesla T4, 15102MiB)\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, save_period=-1, 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=0, resume=False, amp=True, overlap_mask=True, mask_ratio=4, dropout=0.0, val=True, split=val, 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, show_labels=True, show_conf=True, vid_stride=1, line_width=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, pose=12.0, kobj=1.0, 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, tracker=botsort.yaml, 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[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/detect/train', view at http://localhost:6006/\n",
|
|
"\u001b[34m\u001b[1mAMP: \u001b[0mrunning Automatic Mixed Precision (AMP) checks with YOLOv8n...\n",
|
|
"\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\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, 80 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, 80 corrupt: 100% 128/128 [00:00<?, ?it/s]\n",
|
|
"Plotting labels to runs/detect/train/labels.jpg... \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 2.78G 1.177 1.338 1.25 230 640: 100% 8/8 [00:06<00:00, 1.21it/s]\n",
|
|
" Class Images Instances Box(P R mAP50 mAP50-95): 100% 4/4 [00:04<00:00, 1.21s/it]\n",
|
|
" all 128 929 0.631 0.549 0.614 0.455\n",
|
|
"\n",
|
|
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
|
|
" 2/3 2.69G 1.131 1.405 1.24 179 640: 100% 8/8 [00:02<00:00, 3.13it/s]\n",
|
|
" Class Images Instances Box(P R mAP50 mAP50-95): 100% 4/4 [00:02<00:00, 1.51it/s]\n",
|
|
" all 128 929 0.669 0.569 0.634 0.478\n",
|
|
"\n",
|
|
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
|
|
" 3/3 2.84G 1.151 1.281 1.212 214 640: 100% 8/8 [00:02<00:00, 3.27it/s]\n",
|
|
" Class Images Instances Box(P R mAP50 mAP50-95): 100% 4/4 [00:09<00:00, 2.42s/it]\n",
|
|
" all 128 929 0.687 0.58 0.65 0.488\n",
|
|
"\n",
|
|
"3 epochs completed in 0.010 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 YOLOv8.0.71 🚀 Python-3.9.16 torch-2.0.0+cu118 CUDA:0 (Tesla T4, 15102MiB)\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 [00:06<00:00, 1.63s/it]\n",
|
|
" all 128 929 0.689 0.578 0.65 0.486\n",
|
|
" person 128 254 0.763 0.673 0.769 0.544\n",
|
|
" bicycle 128 6 1 0.328 0.379 0.332\n",
|
|
" car 128 46 0.84 0.217 0.292 0.18\n",
|
|
" motorcycle 128 5 0.612 0.8 0.872 0.709\n",
|
|
" airplane 128 6 0.766 0.833 0.894 0.694\n",
|
|
" bus 128 7 0.748 0.714 0.721 0.675\n",
|
|
" train 128 3 0.686 1 0.913 0.83\n",
|
|
" truck 128 12 0.889 0.5 0.529 0.342\n",
|
|
" boat 128 6 0.393 0.333 0.44 0.216\n",
|
|
" traffic light 128 14 1 0.21 0.224 0.142\n",
|
|
" stop sign 128 2 1 0.977 0.995 0.697\n",
|
|
" bench 128 9 0.795 0.434 0.658 0.418\n",
|
|
" bird 128 16 0.933 0.868 0.955 0.656\n",
|
|
" cat 128 4 0.796 1 0.995 0.786\n",
|
|
" dog 128 9 0.713 0.889 0.823 0.608\n",
|
|
" horse 128 2 0.576 1 0.995 0.547\n",
|
|
" elephant 128 17 0.786 0.824 0.911 0.719\n",
|
|
" bear 128 1 0.432 1 0.995 0.895\n",
|
|
" zebra 128 4 0.86 1 0.995 0.935\n",
|
|
" giraffe 128 9 0.966 1 0.995 0.727\n",
|
|
" backpack 128 6 0.534 0.333 0.399 0.227\n",
|
|
" umbrella 128 18 0.757 0.519 0.665 0.447\n",
|
|
" handbag 128 19 0.939 0.105 0.25 0.14\n",
|
|
" tie 128 7 0.677 0.602 0.682 0.505\n",
|
|
" suitcase 128 4 0.636 1 0.995 0.646\n",
|
|
" frisbee 128 5 1 0.789 0.799 0.689\n",
|
|
" skis 128 1 0.794 1 0.995 0.497\n",
|
|
" snowboard 128 7 0.575 0.714 0.762 0.48\n",
|
|
" sports ball 128 6 0.703 0.407 0.514 0.288\n",
|
|
" kite 128 10 0.645 0.4 0.506 0.206\n",
|
|
" baseball bat 128 4 0.436 0.404 0.253 0.125\n",
|
|
" baseball glove 128 7 0.786 0.429 0.43 0.303\n",
|
|
" skateboard 128 5 0.752 0.6 0.6 0.433\n",
|
|
" tennis racket 128 7 0.707 0.286 0.508 0.313\n",
|
|
" bottle 128 18 0.484 0.389 0.43 0.271\n",
|
|
" wine glass 128 16 0.471 0.562 0.584 0.327\n",
|
|
" cup 128 36 0.569 0.278 0.404 0.286\n",
|
|
" fork 128 6 0.529 0.167 0.207 0.192\n",
|
|
" knife 128 16 0.697 0.562 0.594 0.377\n",
|
|
" spoon 128 22 0.68 0.182 0.376 0.213\n",
|
|
" bowl 128 28 0.623 0.679 0.653 0.536\n",
|
|
" banana 128 1 0 0 0.142 0.0363\n",
|
|
" sandwich 128 2 1 0 0.745 0.745\n",
|
|
" orange 128 4 1 0.457 0.849 0.56\n",
|
|
" broccoli 128 11 0.465 0.273 0.284 0.246\n",
|
|
" carrot 128 24 0.581 0.751 0.745 0.489\n",
|
|
" hot dog 128 2 0.654 0.961 0.828 0.763\n",
|
|
" pizza 128 5 0.631 1 0.995 0.854\n",
|
|
" donut 128 14 0.583 1 0.933 0.84\n",
|
|
" cake 128 4 0.643 1 0.995 0.88\n",
|
|
" chair 128 35 0.5 0.543 0.459 0.272\n",
|
|
" couch 128 6 0.488 0.5 0.624 0.47\n",
|
|
" potted plant 128 14 0.645 0.714 0.747 0.542\n",
|
|
" bed 128 3 0.718 1 0.995 0.798\n",
|
|
" dining table 128 13 0.448 0.615 0.538 0.437\n",
|
|
" toilet 128 2 1 0.884 0.995 0.946\n",
|
|
" tv 128 2 0.548 0.644 0.828 0.762\n",
|
|
" laptop 128 3 1 0.563 0.72 0.639\n",
|
|
" mouse 128 2 1 0 0.0623 0.0125\n",
|
|
" remote 128 8 0.697 0.5 0.578 0.496\n",
|
|
" cell phone 128 8 0 0 0.102 0.0471\n",
|
|
" microwave 128 3 0.651 0.667 0.863 0.738\n",
|
|
" oven 128 5 0.471 0.4 0.415 0.309\n",
|
|
" sink 128 6 0.45 0.284 0.268 0.159\n",
|
|
" refrigerator 128 5 0.679 0.4 0.695 0.537\n",
|
|
" book 128 29 0.656 0.133 0.424 0.227\n",
|
|
" clock 128 9 0.878 0.778 0.898 0.759\n",
|
|
" vase 128 2 0.413 1 0.828 0.745\n",
|
|
" scissors 128 1 1 0 0.199 0.0597\n",
|
|
" teddy bear 128 21 0.553 0.472 0.669 0.447\n",
|
|
" toothbrush 128 5 1 0.518 0.8 0.521\n",
|
|
"Speed: 2.7ms preprocess, 3.5ms inference, 0.0ms loss, 3.2ms postprocess 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 below with the `format` argument, i.e. `format=onnx`. See [YOLOv8 Export Docs](https://docs.ultralytics.com/modes/export/) for more information.\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": "fc41bf7a-0ea2-41a6-9ec5-dd0455af43bc"
|
|
},
|
|
"execution_count": 5,
|
|
"outputs": [
|
|
{
|
|
"output_type": "stream",
|
|
"name": "stdout",
|
|
"text": [
|
|
"Ultralytics YOLOv8.0.71 🚀 Python-3.9.16 torch-2.0.0+cu118 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 input shape (1, 3, 640, 640) BCHW and output shape(s) (1, 84, 8400) (6.2 MB)\n",
|
|
"\n",
|
|
"\u001b[34m\u001b[1mTorchScript:\u001b[0m starting export with torch 2.0.0+cu118...\n",
|
|
"\u001b[34m\u001b[1mTorchScript:\u001b[0m export success ✅ 2.3s, saved as yolov8n.torchscript (12.4 MB)\n",
|
|
"\n",
|
|
"Export complete (3.1s)\n",
|
|
"Results saved to \u001b[1m/content\u001b[0m\n",
|
|
"Predict: yolo predict task=detect model=yolov8n.torchscript imgsz=640 \n",
|
|
"Validate: yolo val task=detect model=yolov8n.torchscript imgsz=640 data=coco.yaml \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 detailed Python usage examples in the [YOLOv8 Python Docs](https://docs.ultralytics.com/usage/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 most common tasks in vision AI: [Detect](https://docs.ultralytics.com/tasks/detect/), [Segment](https://docs.ultralytics.com/tasks/segment/), [Classify](https://docs.ultralytics.com/tasks/classify/) and [Pose](https://docs.ultralytics.com/tasks/pose/). See [YOLOv8 Tasks Docs](https://docs.ultralytics.com/tasks/) for more information.\n",
|
|
"\n",
|
|
"<br><img width=\"1024\" src=\"https://raw.githubusercontent.com/ultralytics/assets/main/im/banner-tasks.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/detect/) 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/segment/) 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/classify/) 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",
|
|
"source": [
|
|
"## 4. Pose\n",
|
|
"\n",
|
|
"YOLOv8 _pose_ models use the `-pose` suffix, i.e. `yolov8n-pose.pt` and are pretrained on COCO Keypoints. See [Pose Docs](https://docs.ultralytics.com/tasks/pose/) for full details."
|
|
],
|
|
"metadata": {
|
|
"id": "SpIaFLiO11TG"
|
|
}
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"source": [
|
|
"# Load YOLOv8n-pose, train it on COCO8-pose for 3 epochs and predict an image with it\n",
|
|
"from ultralytics import YOLO\n",
|
|
"\n",
|
|
"model = YOLO('yolov8n-pose.pt') # load a pretrained YOLOv8n classification model\n",
|
|
"model.train(data='coco8-pose.yaml', epochs=3) # train the model\n",
|
|
"model('https://ultralytics.com/images/bus.jpg') # predict on an image"
|
|
],
|
|
"metadata": {
|
|
"id": "si4aKFNg19vX"
|
|
},
|
|
"execution_count": null,
|
|
"outputs": []
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"id": "IEijrePND_2I"
|
|
},
|
|
"source": [
|
|
"# Appendix\n",
|
|
"\n",
|
|
"Additional content below."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"source": [
|
|
"# Git clone and run tests on updates branch\n",
|
|
"!git clone https://github.com/ultralytics/ultralytics -b updates\n",
|
|
"%pip install -qe ultralytics\n",
|
|
"!pytest ultralytics/tests"
|
|
],
|
|
"metadata": {
|
|
"id": "uRKlwxSJdhd1"
|
|
},
|
|
"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": []
|
|
}
|
|
]
|
|
}
|