678 lines
37 KiB
Plaintext
678 lines
37 KiB
Plaintext
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{
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"nbformat": 4,
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"nbformat_minor": 0,
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"metadata": {
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"colab": {
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"name": "YOLO11 Tutorial",
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"provenance": [],
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"toc_visible": true
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},
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"kernelspec": {
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"name": "python3",
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"display_name": "Python 3"
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},
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"accelerator": "GPU"
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},
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "t6MPjfT5NrKQ"
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},
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"source": [
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"<div align=\"center\">\n",
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" <a href=\"https://ultralytics.com/yolo\" target=\"_blank\">\n",
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" <img width=\"1024\" src=\"https://raw.githubusercontent.com/ultralytics/assets/main/yolov8/banner-yolov8.png\">\n",
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" </a>\n",
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"\n",
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" [中文](https://docs.ultralytics.com/zh/) | [한국어](https://docs.ultralytics.com/ko/) | [日本語](https://docs.ultralytics.com/ja/) | [Русский](https://docs.ultralytics.com/ru/) | [Deutsch](https://docs.ultralytics.com/de/) | [Français](https://docs.ultralytics.com/fr/) | [Español](https://docs.ultralytics.com/es/) | [Português](https://docs.ultralytics.com/pt/) | [Türkçe](https://docs.ultralytics.com/tr/) | [Tiếng Việt](https://docs.ultralytics.com/vi/) | [العربية](https://docs.ultralytics.com/ar/)\n",
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"\n",
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" <a href=\"https://github.com/ultralytics/ultralytics/actions/workflows/ci.yml\"><img src=\"https://github.com/ultralytics/ultralytics/actions/workflows/ci.yml/badge.svg\" alt=\"Ultralytics CI\"></a>\n",
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" <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",
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" <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",
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" <a href=\"https://www.kaggle.com/models/ultralytics/yolo11\"><img src=\"https://kaggle.com/static/images/open-in-kaggle.svg\" alt=\"Open In Kaggle\"></a>\n",
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"\n",
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" <a href=\"https://ultralytics.com/discord\"><img alt=\"Discord\" src=\"https://img.shields.io/discord/1089800235347353640?logo=discord&logoColor=white&label=Discord&color=blue\"></a>\n",
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" <a href=\"https://community.ultralytics.com\"><img alt=\"Ultralytics Forums\" src=\"https://img.shields.io/discourse/users?server=https%3A%2F%2Fcommunity.ultralytics.com&logo=discourse&label=Forums&color=blue\"></a>\n",
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" <a href=\"https://reddit.com/r/ultralytics\"><img alt=\"Ultralytics Reddit\" src=\"https://img.shields.io/reddit/subreddit-subscribers/ultralytics?style=flat&logo=reddit&logoColor=white&label=Reddit&color=blue\"></a>\n",
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"</div>\n",
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"\n",
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"This **Ultralytics Colab Notebook** is the easiest way to get started with [YOLO models](https://www.ultralytics.com/yolo)—no installation needed. Built by [Ultralytics](https://www.ultralytics.com/), the creators of YOLO, this notebook walks you through running **state-of-the-art** models directly in your browser.\n",
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"\n",
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"Ultralytics models are constantly updated for performance and flexibility. They're **fast**, **accurate**, and **easy to use**, and they excel at [object detection](https://docs.ultralytics.com/tasks/detect/), [tracking](https://docs.ultralytics.com/modes/track/), [instance segmentation](https://docs.ultralytics.com/tasks/segment/), [image classification](https://docs.ultralytics.com/tasks/classify/), and [pose estimation](https://docs.ultralytics.com/tasks/pose/).\n",
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"\n",
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"Find detailed documentation in the [Ultralytics Docs](https://docs.ultralytics.com/). Get support via [GitHub Issues](https://github.com/ultralytics/ultralytics/issues/new/choose). Join discussions on [Discord](https://discord.com/invite/ultralytics), [Reddit](https://www.reddit.com/r/ultralytics/), and the [Ultralytics Community Forums](https://community.ultralytics.com/)!\n",
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"\n",
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"Request an Enterprise License for commercial use at [Ultralytics Licensing](https://www.ultralytics.com/license).\n",
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"\n",
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"<br>\n",
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"<div>\n",
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" <a href=\"https://www.youtube.com/watch?v=ZN3nRZT7b24\" target=\"_blank\">\n",
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" <img src=\"https://img.youtube.com/vi/ZN3nRZT7b24/maxresdefault.jpg\" alt=\"Ultralytics Video\" width=\"640\" style=\"border-radius: 10px; box-shadow: 0 4px 8px rgba(0, 0, 0, 0.2);\">\n",
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" </a>\n",
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"\n",
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" <p style=\"font-size: 16px; font-family: Arial, sans-serif; color: #555;\">\n",
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" <strong>Watch: </strong> How to Train\n",
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" <a href=\"https://github.com/ultralytics/ultralytics\">Ultralytics</a>\n",
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" <a href=\"https://docs.ultralytics.com/models/yolo11/\">YOLO11</a> Model on Custom Dataset using Google Colab Notebook 🚀\n",
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" </p>\n",
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"</div>"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "7mGmQbAO5pQb"
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},
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"source": [
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"# Setup\n",
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"\n",
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"pip install `ultralytics` and [dependencies](https://github.com/ultralytics/ultralytics/blob/main/pyproject.toml) and check software and hardware.\n",
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"\n",
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"[](https://pypi.org/project/ultralytics/) [](https://www.pepy.tech/projects/ultralytics) [](https://pypi.org/project/ultralytics/)"
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "wbvMlHd_QwMG",
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"outputId": "70b89fc7-b628-49a5-eafd-69791ee69846"
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},
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"source": [
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"%pip install ultralytics\n",
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"import ultralytics\n",
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"ultralytics.checks()"
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],
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"execution_count": null,
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"Ultralytics 8.3.110 🚀 Python-3.11.12 torch-2.6.0+cu124 CUDA:0 (Tesla T4, 15095MiB)\n",
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"Setup complete ✅ (2 CPUs, 12.7 GB RAM, 41.0/112.6 GB disk)\n"
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]
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}
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "4JnkELT0cIJg"
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},
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"source": [
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"# 1. Predict\n",
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"\n",
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"YOLO11 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 [YOLO11 Predict Docs](https://docs.ultralytics.com/modes/train/).\n"
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "zR9ZbuQCH7FX",
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"outputId": "0802415d-a088-43df-d944-ddb65bcea641"
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},
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"source": [
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"# Run inference on an image with YOLO11n\n",
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"!yolo predict model=yolo11n.pt source='https://ultralytics.com/images/zidane.jpg'"
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],
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"execution_count": null,
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"Downloading https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n.pt to 'yolo11n.pt'...\n",
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"100% 5.35M/5.35M [00:00<00:00, 266MB/s]\n",
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"Ultralytics 8.3.110 🚀 Python-3.11.12 torch-2.6.0+cu124 CUDA:0 (Tesla T4, 15095MiB)\n",
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"YOLO11n summary (fused): 100 layers, 2,616,248 parameters, 0 gradients, 6.5 GFLOPs\n",
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"\n",
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"Downloading https://ultralytics.com/images/zidane.jpg to 'zidane.jpg'...\n",
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"100% 49.2k/49.2k [00:00<00:00, 38.6MB/s]\n",
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"image 1/1 /content/zidane.jpg: 384x640 2 persons, 1 tie, 45.9ms\n",
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"Speed: 17.2ms preprocess, 45.9ms inference, 419.7ms postprocess per image at shape (1, 3, 384, 640)\n",
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"Results saved to \u001b[1mruns/detect/predict\u001b[0m\n",
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"💡 Learn more at https://docs.ultralytics.com/modes/predict\n"
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]
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}
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "hkAzDWJ7cWTr"
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},
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"source": [
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" \n",
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"<img align=\"left\" src=\"https://user-images.githubusercontent.com/26833433/212889447-69e5bdf1-5800-4e29-835e-2ed2336dede2.jpg\" width=\"600\">"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "0eq1SMWl6Sfn"
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},
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"source": [
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"# 2. Val\n",
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"Validate a model's accuracy on the [COCO](https://docs.ultralytics.com/datasets/detect/coco/) dataset's `val` or `test` splits. The latest YOLO11 [models](https://github.com/ultralytics/ultralytics#models) are downloaded automatically the first time they are used. See [YOLO11 Val Docs](https://docs.ultralytics.com/modes/val/) for more information."
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "WQPtK1QYVaD_"
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},
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"source": [
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"# Download COCO val\n",
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"import torch\n",
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"torch.hub.download_url_to_file('https://ultralytics.com/assets/coco2017val.zip', 'tmp.zip') # download (780M - 5000 images)\n",
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"!unzip -q tmp.zip -d datasets && rm tmp.zip # unzip"
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],
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"metadata": {
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||
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"id": "X58w8JLpMnjH",
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"outputId": "29e72136-aeff-4cd5-ae5b-e922e7602251",
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"colab": {
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||
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"base_uri": "https://localhost:8080/"
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}
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},
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"source": [
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"# Validate YOLO11n on COCO8 val\n",
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"!yolo val model=yolo11n.pt data=coco8.yaml"
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],
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"execution_count": null,
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"Ultralytics 8.3.110 🚀 Python-3.11.12 torch-2.6.0+cu124 CUDA:0 (Tesla T4, 15095MiB)\n",
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"YOLO11n summary (fused): 100 layers, 2,616,248 parameters, 0 gradients, 6.5 GFLOPs\n",
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"\n",
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"Dataset 'coco8.yaml' images not found ⚠️, missing path '/content/datasets/coco8/images/val'\n",
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"Downloading https://ultralytics.com/assets/coco8.zip to '/content/datasets/coco8.zip'...\n",
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"100% 433k/433k [00:00<00:00, 59.1MB/s]\n",
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"Unzipping /content/datasets/coco8.zip to /content/datasets/coco8...: 100% 25/25 [00:00<00:00, 2391.88file/s]\n",
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"Dataset download success ✅ (1.7s), saved to \u001b[1m/content/datasets\u001b[0m\n",
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"\n",
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"Downloading https://ultralytics.com/assets/Arial.ttf to '/root/.config/Ultralytics/Arial.ttf'...\n",
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"100% 755k/755k [00:00<00:00, 64.3MB/s]\n",
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"\u001b[34m\u001b[1mval: \u001b[0mFast image access ✅ (ping: 0.0±0.0 ms, read: 1415.9±718.7 MB/s, size: 54.0 KB)\n",
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"\u001b[34m\u001b[1mval: \u001b[0mScanning /content/datasets/coco8/labels/val... 4 images, 0 backgrounds, 0 corrupt: 100% 4/4 [00:00<00:00, 77.31it/s]\n",
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"\u001b[34m\u001b[1mval: \u001b[0mNew cache created: /content/datasets/coco8/labels/val.cache\n",
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" Class Images Instances Box(P R mAP50 mAP50-95): 100% 1/1 [00:01<00:00, 1.28s/it]\n",
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" all 4 17 0.57 0.85 0.847 0.632\n",
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" person 3 10 0.557 0.6 0.585 0.272\n",
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" dog 1 1 0.548 1 0.995 0.697\n",
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" horse 1 2 0.531 1 0.995 0.674\n",
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" elephant 1 2 0.371 0.5 0.516 0.256\n",
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" umbrella 1 1 0.569 1 0.995 0.995\n",
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" potted plant 1 1 0.847 1 0.995 0.895\n",
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"Speed: 0.3ms preprocess, 24.6ms inference, 0.0ms loss, 36.4ms postprocess per image\n",
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"Results saved to \u001b[1mruns/detect/val\u001b[0m\n",
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"💡 Learn more at https://docs.ultralytics.com/modes/val\n"
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||
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]
|
||
|
|
}
|
||
|
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]
|
||
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},
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||
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{
|
||
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"cell_type": "markdown",
|
||
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"metadata": {
|
||
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"id": "ZY2VXXXu74w5"
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||
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},
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||
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"source": [
|
||
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"# 3. Train\n",
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"\n",
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||
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"<p align=\"\"><a href=\"https://ultralytics.com/hub\"><img width=\"1000\" src=\"https://github.com/ultralytics/assets/raw/main/yolov8/banner-integrations.png\"/></a></p>\n",
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||
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"\n",
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||
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"Train YOLO11 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 [YOLO11 Train Docs](https://docs.ultralytics.com/modes/train/) for more information."
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||
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]
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||
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},
|
||
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{
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||
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"cell_type": "code",
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||
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"source": [
|
||
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"#@title Select YOLO11 🚀 logger {run: 'auto'}\n",
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"logger = 'TensorBoard' #@param ['TensorBoard', 'Weights & Biases']\n",
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"\n",
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"if logger == 'TensorBoard':\n",
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" !yolo settings tensorboard=True\n",
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" %load_ext tensorboard\n",
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" %tensorboard --logdir .\n",
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"elif logger == 'Weights & Biases':\n",
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" !yolo settings wandb=True"
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],
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||
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"metadata": {
|
||
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"id": "ktegpM42AooT"
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||
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},
|
||
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"execution_count": null,
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||
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"outputs": []
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||
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},
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||
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{
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||
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"cell_type": "code",
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||
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|
"metadata": {
|
||
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"id": "1NcFxRcFdJ_O",
|
||
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"outputId": "959f3bd3-e88f-41fd-cfe4-15680cf65533",
|
||
|
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"colab": {
|
||
|
|
"base_uri": "https://localhost:8080/"
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||
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}
|
||
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},
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||
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"source": [
|
||
|
|
"# Train YOLO11n on COCO8 for 3 epochs\n",
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||
|
|
"!yolo train model=yolo11n.pt data=coco8.yaml epochs=3 imgsz=640"
|
||
|
|
],
|
||
|
|
"execution_count": null,
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"output_type": "stream",
|
||
|
|
"name": "stdout",
|
||
|
|
"text": [
|
||
|
|
"Ultralytics 8.3.110 🚀 Python-3.11.12 torch-2.6.0+cu124 CUDA:0 (Tesla T4, 15095MiB)\n",
|
||
|
|
"\u001b[34m\u001b[1mengine/trainer: \u001b[0mtask=detect, mode=train, model=yolo11n.pt, data=coco8.yaml, epochs=3, time=None, patience=100, batch=16, imgsz=640, save=True, save_period=-1, cache=False, device=None, workers=8, project=None, name=train, exist_ok=False, pretrained=True, optimizer=auto, verbose=True, seed=0, deterministic=True, single_cls=False, rect=False, cos_lr=False, close_mosaic=10, resume=False, amp=True, fraction=1.0, profile=False, freeze=None, multi_scale=False, overlap_mask=True, mask_ratio=4, dropout=0.0, val=True, split=val, save_json=False, conf=None, iou=0.7, max_det=300, half=False, dnn=False, plots=True, source=None, vid_stride=1, stream_buffer=False, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, embed=None, show=False, save_frames=False, save_txt=False, save_conf=False, save_crop=False, show_labels=True, show_conf=True, show_boxes=True, line_width=None, format=torchscript, keras=False, optimize=False, int8=False, dynamic=False, simplify=True, opset=None, workspace=None, 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, 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, bgr=0.0, mosaic=1.0, mixup=0.0, copy_paste=0.0, copy_paste_mode=flip, auto_augment=randaugment, erasing=0.4, cfg=None, tracker=botsort.yaml, save_dir=runs/detect/train\n",
|
||
|
|
"\n",
|
||
|
|
" from n params module arguments \n",
|
||
|
|
" 0 -1 1 464 ultralytics.nn.modules.conv.Conv [3, 16, 3, 2] \n",
|
||
|
|
" 1 -1 1 4672 ultralytics.nn.modules.conv.Conv [16, 32, 3, 2] \n",
|
||
|
|
" 2 -1 1 6640 ultralytics.nn.modules.block.C3k2 [32, 64, 1, False, 0.25] \n",
|
||
|
|
" 3 -1 1 36992 ultralytics.nn.modules.conv.Conv [64, 64, 3, 2] \n",
|
||
|
|
" 4 -1 1 26080 ultralytics.nn.modules.block.C3k2 [64, 128, 1, False, 0.25] \n",
|
||
|
|
" 5 -1 1 147712 ultralytics.nn.modules.conv.Conv [128, 128, 3, 2] \n",
|
||
|
|
" 6 -1 1 87040 ultralytics.nn.modules.block.C3k2 [128, 128, 1, True] \n",
|
||
|
|
" 7 -1 1 295424 ultralytics.nn.modules.conv.Conv [128, 256, 3, 2] \n",
|
||
|
|
" 8 -1 1 346112 ultralytics.nn.modules.block.C3k2 [256, 256, 1, True] \n",
|
||
|
|
" 9 -1 1 164608 ultralytics.nn.modules.block.SPPF [256, 256, 5] \n",
|
||
|
|
" 10 -1 1 249728 ultralytics.nn.modules.block.C2PSA [256, 256, 1] \n",
|
||
|
|
" 11 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \n",
|
||
|
|
" 12 [-1, 6] 1 0 ultralytics.nn.modules.conv.Concat [1] \n",
|
||
|
|
" 13 -1 1 111296 ultralytics.nn.modules.block.C3k2 [384, 128, 1, False] \n",
|
||
|
|
" 14 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \n",
|
||
|
|
" 15 [-1, 4] 1 0 ultralytics.nn.modules.conv.Concat [1] \n",
|
||
|
|
" 16 -1 1 32096 ultralytics.nn.modules.block.C3k2 [256, 64, 1, False] \n",
|
||
|
|
" 17 -1 1 36992 ultralytics.nn.modules.conv.Conv [64, 64, 3, 2] \n",
|
||
|
|
" 18 [-1, 13] 1 0 ultralytics.nn.modules.conv.Concat [1] \n",
|
||
|
|
" 19 -1 1 86720 ultralytics.nn.modules.block.C3k2 [192, 128, 1, False] \n",
|
||
|
|
" 20 -1 1 147712 ultralytics.nn.modules.conv.Conv [128, 128, 3, 2] \n",
|
||
|
|
" 21 [-1, 10] 1 0 ultralytics.nn.modules.conv.Concat [1] \n",
|
||
|
|
" 22 -1 1 378880 ultralytics.nn.modules.block.C3k2 [384, 256, 1, True] \n",
|
||
|
|
" 23 [16, 19, 22] 1 464912 ultralytics.nn.modules.head.Detect [80, [64, 128, 256]] \n",
|
||
|
|
"YOLO11n summary: 181 layers, 2,624,080 parameters, 2,624,064 gradients, 6.6 GFLOPs\n",
|
||
|
|
"\n",
|
||
|
|
"Transferred 499/499 items from pretrained weights\n",
|
||
|
|
"\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/detect/train', view at http://localhost:6006/\n",
|
||
|
|
"Freezing layer 'model.23.dfl.conv.weight'\n",
|
||
|
|
"\u001b[34m\u001b[1mAMP: \u001b[0mrunning Automatic Mixed Precision (AMP) checks...\n",
|
||
|
|
"\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n",
|
||
|
|
"\u001b[34m\u001b[1mtrain: \u001b[0mFast image access ✅ (ping: 0.0±0.0 ms, read: 1512.9±379.4 MB/s, size: 50.0 KB)\n",
|
||
|
|
"\u001b[34m\u001b[1mtrain: \u001b[0mScanning /content/datasets/coco8/labels/train... 4 images, 0 backgrounds, 0 corrupt: 100% 4/4 [00:00<00:00, 1705.69it/s]\n",
|
||
|
|
"\u001b[34m\u001b[1mtrain: \u001b[0mNew cache created: /content/datasets/coco8/labels/train.cache\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, num_output_channels=3, method='weighted_average'), CLAHE(p=0.01, clip_limit=(1.0, 4.0), tile_grid_size=(8, 8))\n",
|
||
|
|
"\u001b[34m\u001b[1mval: \u001b[0mFast image access ✅ (ping: 0.0±0.0 ms, read: 328.7±28.2 MB/s, size: 54.0 KB)\n",
|
||
|
|
"\u001b[34m\u001b[1mval: \u001b[0mScanning /content/datasets/coco8/labels/val.cache... 4 images, 0 backgrounds, 0 corrupt: 100% 4/4 [00:00<?, ?it/s]\n",
|
||
|
|
"Plotting labels to runs/detect/train/labels.jpg... \n",
|
||
|
|
"\u001b[34m\u001b[1moptimizer:\u001b[0m 'optimizer=auto' found, ignoring 'lr0=0.01' and 'momentum=0.937' and determining best 'optimizer', 'lr0' and 'momentum' automatically... \n",
|
||
|
|
"\u001b[34m\u001b[1moptimizer:\u001b[0m AdamW(lr=0.000119, momentum=0.9) with parameter groups 81 weight(decay=0.0), 88 weight(decay=0.0005), 87 bias(decay=0.0)\n",
|
||
|
|
"\u001b[34m\u001b[1mTensorBoard: \u001b[0mmodel graph visualization added ✅\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 0.689G 0.9685 2.353 1.287 18 640: 100% 1/1 [00:01<00:00, 1.06s/it]\n",
|
||
|
|
" Class Images Instances Box(P R mAP50 mAP50-95): 100% 1/1 [00:00<00:00, 5.23it/s]\n",
|
||
|
|
" all 4 17 0.583 0.85 0.878 0.634\n",
|
||
|
|
"\n",
|
||
|
|
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
|
||
|
|
" 2/3 0.719G 1.249 3.262 1.643 22 640: 100% 1/1 [00:00<00:00, 7.37it/s]\n",
|
||
|
|
" Class Images Instances Box(P R mAP50 mAP50-95): 100% 1/1 [00:00<00:00, 10.81it/s]\n",
|
||
|
|
" all 4 17 0.588 0.85 0.886 0.634\n",
|
||
|
|
"\n",
|
||
|
|
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
|
||
|
|
" 3/3 0.725G 1.373 2.905 1.483 27 640: 100% 1/1 [00:00<00:00, 6.70it/s]\n",
|
||
|
|
" Class Images Instances Box(P R mAP50 mAP50-95): 100% 1/1 [00:00<00:00, 12.17it/s]\n",
|
||
|
|
" all 4 17 0.572 0.85 0.85 0.649\n",
|
||
|
|
"\n",
|
||
|
|
"3 epochs completed in 0.002 hours.\n",
|
||
|
|
"Optimizer stripped from runs/detect/train/weights/last.pt, 5.5MB\n",
|
||
|
|
"Optimizer stripped from runs/detect/train/weights/best.pt, 5.5MB\n",
|
||
|
|
"\n",
|
||
|
|
"Validating runs/detect/train/weights/best.pt...\n",
|
||
|
|
"Ultralytics 8.3.110 🚀 Python-3.11.12 torch-2.6.0+cu124 CUDA:0 (Tesla T4, 15095MiB)\n",
|
||
|
|
"YOLO11n summary (fused): 100 layers, 2,616,248 parameters, 0 gradients, 6.5 GFLOPs\n",
|
||
|
|
" Class Images Instances Box(P R mAP50 mAP50-95): 100% 1/1 [00:00<00:00, 20.72it/s]\n",
|
||
|
|
" all 4 17 0.571 0.85 0.85 0.649\n",
|
||
|
|
" person 3 10 0.569 0.6 0.594 0.274\n",
|
||
|
|
" dog 1 1 0.545 1 0.995 0.796\n",
|
||
|
|
" horse 1 2 0.546 1 0.995 0.674\n",
|
||
|
|
" elephant 1 2 0.359 0.5 0.524 0.259\n",
|
||
|
|
" umbrella 1 1 0.565 1 0.995 0.995\n",
|
||
|
|
" potted plant 1 1 0.844 1 0.995 0.895\n",
|
||
|
|
"Speed: 0.3ms preprocess, 4.6ms inference, 0.0ms loss, 1.5ms postprocess per image\n",
|
||
|
|
"Results saved to \u001b[1mruns/detect/train\u001b[0m\n",
|
||
|
|
"💡 Learn more at https://docs.ultralytics.com/modes/train\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"source": [
|
||
|
|
"# 4. Export\n",
|
||
|
|
"\n",
|
||
|
|
"Export a YOLO model to any supported format below with the `format` argument, i.e. `format=onnx`. See [Export Docs](https://docs.ultralytics.com/modes/export/) for more information.\n",
|
||
|
|
"\n",
|
||
|
|
"- 💡 ProTip: Export to [ONNX](https://docs.ultralytics.com/integrations/onnx/) or [OpenVINO](https://docs.ultralytics.com/integrations/openvino/) for up to 3x CPU speedup.\n",
|
||
|
|
"- 💡 ProTip: Export to [TensorRT](https://docs.ultralytics.com/integrations/tensorrt/) for up to 5x GPU speedup.\n",
|
||
|
|
"\n",
|
||
|
|
"| Format | `format` Argument | Model | Metadata | Arguments |\n",
|
||
|
|
"|--------|-----------------|-------|----------|------------|\n",
|
||
|
|
"| [PyTorch](https://pytorch.org/) | - | `yolo11n.pt` | ✅ | - |\n",
|
||
|
|
"| [TorchScript](https://docs.ultralytics.com/integrations/torchscript) | `torchscript` | `yolo11n.torchscript` | ✅ | `imgsz`, `batch`, `optimize`, `half`, `nms`, `device` |\n",
|
||
|
|
"| [ONNX](https://docs.ultralytics.com/integrations/onnx) | `onnx` | `yolo11n.onnx` | ✅ | `imgsz`, `batch`, `dynamic`, `half`, `opset`, `simplify`, `nms`, `device` |\n",
|
||
|
|
"| [OpenVINO](https://docs.ultralytics.com/integrations/openvino) | `openvino` | `yolo11n_openvino_model/` | ✅ | `imgsz`, `batch`, `dynamic`, `half`, `int8`, `nms`, `fraction`, `device`, `data` |\n",
|
||
|
|
"| [TensorRT](https://docs.ultralytics.com/integrations/tensorrt) | `engine` | `yolo11n.engine` | ✅ | `imgsz`, `batch`, `dynamic`, `half`, `int8`, `simplify`, `nms`, `fraction`, `device`, `data`, `workspace` |\n",
|
||
|
|
"| [CoreML](https://docs.ultralytics.com/integrations/coreml) | `coreml` | `yolo11n.mlpackage` | ✅ | `imgsz`, `batch`, `half`, `int8`, `nms`, `device` |\n",
|
||
|
|
"| [TF SavedModel](https://docs.ultralytics.com/integrations/tf-savedmodel) | `saved_model` | `yolo11n_saved_model/` | ✅ | `imgsz`, `batch`, `int8`, `keras`, `nms`, `device` |\n",
|
||
|
|
"| [TF GraphDef](https://docs.ultralytics.com/integrations/tf-graphdef) | `pb` | `yolo11n.pb` | ❌ | `imgsz`, `batch`, `device` |\n",
|
||
|
|
"| [TF Lite](https://docs.ultralytics.com/integrations/tflite) | `tflite` | `yolo11n.tflite` | ✅ | `imgsz`, `batch`, `half`, `int8`, `nms`, `fraction`, `device`, `data` |\n",
|
||
|
|
"| [TF Edge TPU](https://docs.ultralytics.com/integrations/edge-tpu) | `edgetpu` | `yolo11n_edgetpu.tflite` | ✅ | `imgsz`, `device` |\n",
|
||
|
|
"| [TF.js](https://docs.ultralytics.com/integrations/tfjs) | `tfjs` | `yolo11n_web_model/` | ✅ | `imgsz`, `batch`, `half`, `int8`, `nms`, `device` |\n",
|
||
|
|
"| [PaddlePaddle](https://docs.ultralytics.com/integrations/paddlepaddle) | `paddle` | `yolo11n_paddle_model/` | ✅ | `imgsz`, `batch`, `device` |\n",
|
||
|
|
"| [MNN](https://docs.ultralytics.com/integrations/mnn) | `mnn` | `yolo11n.mnn` | ✅ | `imgsz`, `batch`, `half`, `int8`, `device` |\n",
|
||
|
|
"| [NCNN](https://docs.ultralytics.com/integrations/ncnn) | `ncnn` | `yolo11n_ncnn_model/` | ✅ | `imgsz`, `batch`, `half`, `device` |\n",
|
||
|
|
"| [IMX500](https://docs.ultralytics.com/integrations/sony-imx500) | `imx` | `yolo11n_imx_model/` | ✅ | `imgsz`, `int8`, `fraction`, `device`, `data` |\n",
|
||
|
|
"| [RKNN](https://docs.ultralytics.com/integrations/rockchip-rknn) | `rknn` | `yolo11n_rknn_model/` | ✅ | `imgsz`, `batch`, `name`, `device` |"
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"id": "nPZZeNrLCQG6"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"source": [
|
||
|
|
"!yolo export model=yolo11n.pt format=torchscript"
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"colab": {
|
||
|
|
"base_uri": "https://localhost:8080/"
|
||
|
|
},
|
||
|
|
"id": "CYIjW4igCjqD",
|
||
|
|
"outputId": "2fd8de97-e864-48d1-afbb-ef6eca114bb4"
|
||
|
|
},
|
||
|
|
"execution_count": null,
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"output_type": "stream",
|
||
|
|
"name": "stdout",
|
||
|
|
"text": [
|
||
|
|
"Ultralytics 8.3.110 🚀 Python-3.11.12 torch-2.6.0+cu124 CPU (Intel Xeon 2.00GHz)\n",
|
||
|
|
"YOLO11n summary (fused): 100 layers, 2,616,248 parameters, 0 gradients, 6.5 GFLOPs\n",
|
||
|
|
"\n",
|
||
|
|
"\u001b[34m\u001b[1mPyTorch:\u001b[0m starting from 'yolo11n.pt' with input shape (1, 3, 640, 640) BCHW and output shape(s) (1, 84, 8400) (5.4 MB)\n",
|
||
|
|
"\n",
|
||
|
|
"\u001b[34m\u001b[1mTorchScript:\u001b[0m starting export with torch 2.6.0+cu124...\n",
|
||
|
|
"\u001b[34m\u001b[1mTorchScript:\u001b[0m export success ✅ 2.4s, saved as 'yolo11n.torchscript' (10.5 MB)\n",
|
||
|
|
"\n",
|
||
|
|
"Export complete (4.1s)\n",
|
||
|
|
"Results saved to \u001b[1m/content\u001b[0m\n",
|
||
|
|
"Predict: yolo predict task=detect model=yolo11n.torchscript imgsz=640 \n",
|
||
|
|
"Validate: yolo val task=detect model=yolo11n.torchscript imgsz=640 data=/usr/src/ultralytics/ultralytics/cfg/datasets/coco.yaml \n",
|
||
|
|
"Visualize: https://netron.app\n",
|
||
|
|
"💡 Learn more at https://docs.ultralytics.com/modes/export\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"source": [
|
||
|
|
"# 5. Python Usage\n",
|
||
|
|
"\n",
|
||
|
|
"YOLO11 was reimagined using Python-first principles for the most seamless Python YOLO experience yet. YOLO11 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 [YOLO11 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('yolo11n.yaml') # build a new model from scratch\n",
|
||
|
|
"model = YOLO('yolo11n.pt') # load a pretrained model (recommended for training)\n",
|
||
|
|
"\n",
|
||
|
|
"# Use the model\n",
|
||
|
|
"results = model.train(data='coco8.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",
|
||
|
|
"results = 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",
|
||
|
|
"YOLO11 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 [YOLO11 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",
|
||
|
|
"YOLO11 _detection_ models have no suffix and are the default YOLO11 models, i.e. `yolo11n.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 YOLO11n, train it on COCO128 for 3 epochs and predict an image with it\n",
|
||
|
|
"from ultralytics import YOLO\n",
|
||
|
|
"\n",
|
||
|
|
"model = YOLO('yolo11n.pt') # load a pretrained YOLO detection model\n",
|
||
|
|
"model.train(data='coco8.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",
|
||
|
|
"YOLO11 _segmentation_ models use the `-seg` suffix, i.e. `yolo11n-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 YOLO11n-seg, train it on COCO128-seg for 3 epochs and predict an image with it\n",
|
||
|
|
"from ultralytics import YOLO\n",
|
||
|
|
"\n",
|
||
|
|
"model = YOLO('yolo11n-seg.pt') # load a pretrained YOLO segmentation model\n",
|
||
|
|
"model.train(data='coco8-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",
|
||
|
|
"YOLO11 _classification_ models use the `-cls` suffix, i.e. `yolo11n-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 YOLO11n-cls, train it on mnist160 for 3 epochs and predict an image with it\n",
|
||
|
|
"from ultralytics import YOLO\n",
|
||
|
|
"\n",
|
||
|
|
"model = YOLO('yolo11n-cls.pt') # load a pretrained YOLO 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",
|
||
|
|
"YOLO11 _pose_ models use the `-pose` suffix, i.e. `yolo11n-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 YOLO11n-pose, train it on COCO8-pose for 3 epochs and predict an image with it\n",
|
||
|
|
"from ultralytics import YOLO\n",
|
||
|
|
"\n",
|
||
|
|
"model = YOLO('yolo11n-pose.pt') # load a pretrained YOLO pose 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",
|
||
|
|
"source": [
|
||
|
|
"## 4. Oriented Bounding Boxes (OBB)\n",
|
||
|
|
"\n",
|
||
|
|
"YOLO11 _OBB_ models use the `-obb` suffix, i.e. `yolo11n-obb.pt` and are pretrained on the DOTA dataset. See [OBB Docs](https://docs.ultralytics.com/tasks/obb/) for full details."
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"id": "cf5j_T9-B5F0"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"source": [
|
||
|
|
"# Load YOLO11n-obb, train it on DOTA8 for 3 epochs and predict an image with it\n",
|
||
|
|
"from ultralytics import YOLO\n",
|
||
|
|
"\n",
|
||
|
|
"model = YOLO('yolo11n-obb.pt') # load a pretrained YOLO OBB model\n",
|
||
|
|
"model.train(data='dota8.yaml', epochs=3) # train the model\n",
|
||
|
|
"model('https://ultralytics.com/images/boats.jpg') # predict on an image"
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"id": "IJNKClOOB5YS"
|
||
|
|
},
|
||
|
|
"execution_count": null,
|
||
|
|
"outputs": []
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {
|
||
|
|
"id": "IEijrePND_2I"
|
||
|
|
},
|
||
|
|
"source": [
|
||
|
|
"# Appendix\n",
|
||
|
|
"\n",
|
||
|
|
"Additional content below."
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"source": [
|
||
|
|
"# Pip install from source\n",
|
||
|
|
"!pip install git+https://github.com/ultralytics/ultralytics@main"
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"id": "pIdE6i8C3LYp"
|
||
|
|
},
|
||
|
|
"execution_count": null,
|
||
|
|
"outputs": []
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"source": [
|
||
|
|
"# Git clone and run tests on 'main' branch\n",
|
||
|
|
"!git clone https://github.com/ultralytics/ultralytics -b main\n",
|
||
|
|
"%pip install -qe ultralytics"
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"id": "uRKlwxSJdhd1"
|
||
|
|
},
|
||
|
|
"execution_count": null,
|
||
|
|
"outputs": []
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"source": [
|
||
|
|
"# Run tests (Git clone only)\n",
|
||
|
|
"!pytest ultralytics/tests"
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"id": "GtPlh7mcCGZX"
|
||
|
|
},
|
||
|
|
"execution_count": null,
|
||
|
|
"outputs": []
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"source": [
|
||
|
|
"# Validate multiple models\n",
|
||
|
|
"for x in 'nsmlx':\n",
|
||
|
|
" !yolo val model=yolo11{x}.pt data=coco.yaml"
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"id": "Wdc6t_bfzDDk"
|
||
|
|
},
|
||
|
|
"execution_count": null,
|
||
|
|
"outputs": []
|
||
|
|
}
|
||
|
|
]
|
||
|
|
}
|