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275 lines
21 KiB
275 lines
21 KiB
<div align="center">
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<p>
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<a href="https://ultralytics.com/yolov8" target="_blank">
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<img width="100%" src="https://raw.githubusercontent.com/ultralytics/assets/main/yolov8/banner-yolov8.png"></a>
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</p>
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[English](README.md) | [简体中文](README.zh-CN.md)
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<br>
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<div>
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<a href="https://github.com/ultralytics/ultralytics/actions/workflows/ci.yaml"><img src="https://github.com/ultralytics/ultralytics/actions/workflows/ci.yaml/badge.svg" alt="Ultralytics CI"></a>
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<a href="https://zenodo.org/badge/latestdoi/264818686"><img src="https://zenodo.org/badge/264818686.svg" alt="YOLOv8 Citation"></a>
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<a href="https://hub.docker.com/r/ultralytics/ultralytics"><img src="https://img.shields.io/docker/pulls/ultralytics/ultralytics?logo=docker" alt="Docker Pulls"></a>
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<br>
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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>
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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>
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<a href="https://www.kaggle.com/ultralytics/yolov8"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a>
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</div>
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<br>
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[Ultralytics YOLOv8](https://github.com/ultralytics/ultralytics), developed by [Ultralytics](https://ultralytics.com),
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is a cutting-edge, state-of-the-art (SOTA) model that builds upon the success of previous YOLO versions and introduces
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new features and improvements to further boost performance and flexibility. YOLOv8 is designed to be fast, accurate, and
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easy to use, making it an excellent choice for a wide range of object detection, image segmentation and image
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classification tasks.
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To request an Enterprise License please complete the form at [Ultralytics Licensing](https://ultralytics.com/license).
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<img width="100%" src="https://raw.githubusercontent.com/ultralytics/assets/main/yolov8/yolo-comparison-plots.png"></a>
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<div align="center">
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<a href="https://github.com/ultralytics" style="text-decoration:none;">
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<img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-github.png" width="2%" alt="" /></a>
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<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="" />
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<a href="https://www.linkedin.com/company/ultralytics" style="text-decoration:none;">
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<img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-linkedin.png" width="2%" alt="" /></a>
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<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="" />
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<a href="https://twitter.com/ultralytics" style="text-decoration:none;">
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<img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-twitter.png" width="2%" alt="" /></a>
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<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="" />
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<a href="https://www.producthunt.com/@glenn_jocher" style="text-decoration:none;">
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<img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-producthunt.png" width="2%" alt="" /></a>
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<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="" />
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<a href="https://youtube.com/ultralytics" style="text-decoration:none;">
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<img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-youtube.png" width="2%" alt="" /></a>
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<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="" />
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<a href="https://www.facebook.com/ultralytics" style="text-decoration:none;">
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<img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-facebook.png" width="2%" alt="" /></a>
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<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="" />
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<a href="https://www.instagram.com/ultralytics/" style="text-decoration:none;">
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<img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-instagram.png" width="2%" alt="" /></a>
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</div>
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</div>
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## <div align="center">Documentation</div>
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See below for a quickstart installation and usage example, and see the [YOLOv8 Docs](https://docs.ultralytics.com) for
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full documentation on training, validation, prediction and deployment.
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<details open>
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<summary>Install</summary>
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Pip install the ultralytics package including
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all [requirements.txt](https://github.com/ultralytics/ultralytics/blob/main/requirements.txt) in a
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[**3.10>=Python>=3.7**](https://www.python.org/) environment, including
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[**PyTorch>=1.7**](https://pytorch.org/get-started/locally/).
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```bash
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pip install ultralytics
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```
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</details>
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<details open>
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<summary>Usage</summary>
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#### CLI
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YOLOv8 may be used directly in the Command Line Interface (CLI) with a `yolo` command:
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```bash
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yolo predict model=yolov8n.pt source="https://ultralytics.com/images/bus.jpg"
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```
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`yolo` can be used for a variety of tasks and modes and accepts additional arguments, i.e. `imgsz=640`. See the YOLOv8
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[CLI Docs](https://docs.ultralytics.com/cli) for examples.
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#### Python
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YOLOv8 may also be used directly in a Python environment, and accepts the
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same [arguments](https://docs.ultralytics.com/cfg/) as in the CLI example above:
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```python
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from ultralytics import YOLO
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# Load a model
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model = YOLO("yolov8n.yaml") # build a new model from scratch
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model = YOLO("yolov8n.pt") # load a pretrained model (recommended for training)
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# Use the model
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model.train(data="coco128.yaml", epochs=3) # train the model
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metrics = model.val() # evaluate model performance on the validation set
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results = model("https://ultralytics.com/images/bus.jpg") # predict on an image
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success = model.export(format="onnx") # export the model to ONNX format
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```
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[Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/models) download automatically from the latest
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Ultralytics [release](https://github.com/ultralytics/assets/releases). See
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YOLOv8 [Python Docs](https://docs.ultralytics.com/python) for more examples.
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#### Known Issues / TODOs
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We are still working on several parts of YOLOv8! We aim to have these completed soon to bring the YOLOv8 feature set up
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to par with YOLOv5, including export and inference to all the same formats. We are also writing a YOLOv8 paper which we
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will submit to [arxiv.org](https://arxiv.org) once complete.
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- [ ] TensorFlow exports
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- [ ] DDP resume
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- [ ] [arxiv.org](https://arxiv.org) paper
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</details>
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## <div align="center">Models</div>
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All YOLOv8 pretrained models are available here. Detection and Segmentation models are pretrained on the COCO dataset,
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while Classification models are pretrained on the ImageNet dataset.
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[Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/models) download automatically from the latest
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Ultralytics [release](https://github.com/ultralytics/assets/releases) on first use.
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<details open><summary>Detection</summary>
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See [Detection Docs](https://docs.ultralytics.com/tasks/detection/) for usage examples with these models.
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| Model | size<br><sup>(pixels) | mAP<sup>val<br>50-95 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>A100 TensorRT<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) |
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| ------------------------------------------------------------------------------------ | --------------------- | -------------------- | ------------------------------ | ----------------------------------- | ------------------ | ----------------- |
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| [YOLOv8n](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n.pt) | 640 | 37.3 | 80.4 | 0.99 | 3.2 | 8.7 |
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| [YOLOv8s](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8s.pt) | 640 | 44.9 | 128.4 | 1.20 | 11.2 | 28.6 |
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| [YOLOv8m](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8m.pt) | 640 | 50.2 | 234.7 | 1.83 | 25.9 | 78.9 |
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| [YOLOv8l](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8l.pt) | 640 | 52.9 | 375.2 | 2.39 | 43.7 | 165.2 |
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| [YOLOv8x](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x.pt) | 640 | 53.9 | 479.1 | 3.53 | 68.2 | 257.8 |
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- **mAP<sup>val</sup>** values are for single-model single-scale on [COCO val2017](http://cocodataset.org) dataset.
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<br>Reproduce by `yolo val detect data=coco.yaml device=0`
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- **Speed** averaged over COCO val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/)
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instance.
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<br>Reproduce by `yolo val detect data=coco128.yaml batch=1 device=0/cpu`
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</details>
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<details><summary>Segmentation</summary>
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See [Segmentation Docs](https://docs.ultralytics.com/tasks/segmentation/) for usage examples with these models.
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| Model | size<br><sup>(pixels) | mAP<sup>box<br>50-95 | mAP<sup>mask<br>50-95 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>A100 TensorRT<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) |
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| ---------------------------------------------------------------------------------------- | --------------------- | -------------------- | --------------------- | ------------------------------ | ----------------------------------- | ------------------ | ----------------- |
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| [YOLOv8n](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n-seg.pt) | 640 | 36.7 | 30.5 | 96.1 | 1.21 | 3.4 | 12.6 |
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| [YOLOv8s](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8s-seg.pt) | 640 | 44.6 | 36.8 | 155.7 | 1.47 | 11.8 | 42.6 |
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| [YOLOv8m](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8m-seg.pt) | 640 | 49.9 | 40.8 | 317.0 | 2.18 | 27.3 | 110.2 |
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| [YOLOv8l](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8l-seg.pt) | 640 | 52.3 | 42.6 | 572.4 | 2.79 | 46.0 | 220.5 |
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| [YOLOv8x](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x-seg.pt) | 640 | 53.4 | 43.4 | 712.1 | 4.02 | 71.8 | 344.1 |
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- **mAP<sup>val</sup>** values are for single-model single-scale on [COCO val2017](http://cocodataset.org) dataset.
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<br>Reproduce by `yolo val segment data=coco.yaml device=0`
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- **Speed** averaged over COCO val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/)
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instance.
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<br>Reproduce by `yolo val segment data=coco128-seg.yaml batch=1 device=0/cpu`
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</details>
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<details><summary>Classification</summary>
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See [Classification Docs](https://docs.ultralytics.com/tasks/classification/) for usage examples with these models.
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| Model | size<br><sup>(pixels) | acc<br><sup>top1 | acc<br><sup>top5 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>A100 TensorRT<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) at 640 |
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| ---------------------------------------------------------------------------------------- | --------------------- | ---------------- | ---------------- | ------------------------------ | ----------------------------------- | ------------------ | ------------------------ |
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| [YOLOv8n](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n-cls.pt) | 224 | 66.6 | 87.0 | 12.9 | 0.31 | 2.7 | 4.3 |
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| [YOLOv8s](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8s-cls.pt) | 224 | 72.3 | 91.1 | 23.4 | 0.35 | 6.4 | 13.5 |
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| [YOLOv8m](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8m-cls.pt) | 224 | 76.4 | 93.2 | 85.4 | 0.62 | 17.0 | 42.7 |
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| [YOLOv8l](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8l-cls.pt) | 224 | 78.0 | 94.1 | 163.0 | 0.87 | 37.5 | 99.7 |
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| [YOLOv8x](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x-cls.pt) | 224 | 78.4 | 94.3 | 232.0 | 1.01 | 57.4 | 154.8 |
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- **acc** values are model accuracies on the [ImageNet](https://www.image-net.org/) dataset validation set.
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<br>Reproduce by `yolo val classify data=path/to/ImageNet device=0`
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- **Speed** averaged over ImageNet val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/)
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instance.
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<br>Reproduce by `yolo val classify data=path/to/ImageNet batch=1 device=0/cpu`
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</details>
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## <div align="center">Integrations</div>
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<br>
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<a href="https://bit.ly/ultralytics_hub" target="_blank">
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<img width="100%" src="https://github.com/ultralytics/assets/raw/main/yolov8/banner-integrations.png"></a>
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<br>
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<br>
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<div align="center">
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<a href="https://roboflow.com/?ref=ultralytics">
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<img src="https://github.com/ultralytics/assets/raw/main/partners/logo-roboflow.png" width="10%" /></a>
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<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="15%" height="0" alt="" />
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<a href="https://cutt.ly/yolov5-readme-clearml">
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<img src="https://github.com/ultralytics/assets/raw/main/partners/logo-clearml.png" width="10%" /></a>
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<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="15%" height="0" alt="" />
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<a href="https://bit.ly/yolov5-readme-comet">
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<img src="https://github.com/ultralytics/assets/raw/main/partners/logo-comet.png" width="10%" /></a>
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<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="15%" height="0" alt="" />
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<a href="https://bit.ly/yolov5-neuralmagic">
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<img src="https://github.com/ultralytics/assets/raw/main/partners/logo-neuralmagic.png" width="10%" /></a>
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</div>
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| Roboflow | ClearML ⭐ NEW | Comet ⭐ NEW | Neural Magic ⭐ NEW |
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| :--------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------: |
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| Label and export your custom datasets directly to YOLOv8 for training with [Roboflow](https://roboflow.com/?ref=ultralytics) | Automatically track, visualize and even remotely train YOLOv8 using [ClearML](https://cutt.ly/yolov5-readme-clearml) (open-source!) | Free forever, [Comet](https://bit.ly/yolov5-readme-comet2) lets you save YOLOv8 models, resume training, and interactively visualize and debug predictions | Run YOLOv8 inference up to 6x faster with [Neural Magic DeepSparse](https://bit.ly/yolov5-neuralmagic) |
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## <div align="center">Ultralytics HUB</div>
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[Ultralytics HUB](https://bit.ly/ultralytics_hub) is our ⭐ **NEW** no-code solution to visualize datasets, train YOLOv8
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🚀 models, and deploy to the real world in a seamless experience. Get started for **Free** now! Also run YOLOv8 models on
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your iOS or Android device by downloading the [Ultralytics App](https://ultralytics.com/app_install)!
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<a href="https://bit.ly/ultralytics_hub" target="_blank">
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<img width="100%" src="https://github.com/ultralytics/assets/raw/main/im/ultralytics-hub.png"></a>
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## <div align="center">Contribute</div>
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We love your input! YOLOv5 and YOLOv8 would not be possible without help from our community. Please see
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our [Contributing Guide](CONTRIBUTING.md) to get started, and fill out
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our [Survey](https://ultralytics.com/survey?utm_source=github&utm_medium=social&utm_campaign=Survey) to send us feedback
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on your experience. Thank you 🙏 to all our contributors!
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<!-- SVG image from https://opencollective.com/ultralytics/contributors.svg?width=990 -->
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<a href="https://github.com/ultralytics/yolov5/graphs/contributors">
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<img src="https://github.com/ultralytics/assets/raw/main/im/image-contributors.png" /></a>
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## <div align="center">License</div>
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YOLOv8 is available under two different licenses:
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- **GPL-3.0 License**: See [LICENSE](https://github.com/ultralytics/ultralytics/blob/main/LICENSE) file for details.
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- **Enterprise License**: Provides greater flexibility for commercial product development without the open-source
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requirements of GPL-3.0. Typical use cases are embedding Ultralytics software and AI models in commercial products and
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applications. Request an Enterprise License at [Ultralytics Licensing](https://ultralytics.com/license).
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## <div align="center">Contact</div>
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For YOLOv8 bugs and feature requests please visit [GitHub Issues](https://github.com/ultralytics/ultralytics/issues).
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For professional support please [Contact Us](https://ultralytics.com/contact).
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<br>
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<div align="center">
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<a href="https://github.com/ultralytics" style="text-decoration:none;">
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<img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-github.png" width="3%" alt="" /></a>
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<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="" />
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<a href="https://www.linkedin.com/company/ultralytics" style="text-decoration:none;">
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<img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-linkedin.png" width="3%" alt="" /></a>
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<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="" />
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<a href="https://twitter.com/ultralytics" style="text-decoration:none;">
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<img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-twitter.png" width="3%" alt="" /></a>
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<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="" />
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<a href="https://www.producthunt.com/@glenn_jocher" style="text-decoration:none;">
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<img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-producthunt.png" width="3%" alt="" /></a>
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<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="" />
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<a href="https://youtube.com/ultralytics" style="text-decoration:none;">
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<img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-youtube.png" width="3%" alt="" /></a>
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<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="" />
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<a href="https://www.facebook.com/ultralytics" style="text-decoration:none;">
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<img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-facebook.png" width="3%" alt="" /></a>
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<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="" />
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<a href="https://www.instagram.com/ultralytics/" style="text-decoration:none;">
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<img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-instagram.png" width="3%" alt="" /></a>
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</div>
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