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[English ](README.md ) | [简体中文 ](README.zh-CN.md )
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[Ultralytics YOLOv8 ](https://github.com/ultralytics/ultralytics ), developed by [Ultralytics ](https://ultralytics.com ),
is a cutting-edge, state-of-the-art (SOTA) model that builds upon the success of previous YOLO versions and introduces
new features and improvements to further boost performance and flexibility. YOLOv8 is designed to be fast, accurate, and
easy to use, making it an excellent choice for a wide range of object detection, image segmentation and image
classification tasks.
To request an Enterprise License please complete the form at [Ultralytics Licensing ](https://ultralytics.com/license ).
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## <div align="center">Documentation</div>
See below for a quickstart installation and usage example, and see the [YOLOv8 Docs ](https://docs.ultralytics.com ) for
full documentation on training, validation, prediction and deployment.
< details open >
< summary > Install< / summary >
Pip install the ultralytics package including
all [requirements.txt ](https://github.com/ultralytics/ultralytics/blob/main/requirements.txt ) in a
[**3.10>=Python>=3.7** ](https://www.python.org/ ) environment, including
[**PyTorch>=1.7** ](https://pytorch.org/get-started/locally/ ).
```bash
pip install ultralytics
```
< / details >
< details open >
< summary > Usage< / summary >
#### CLI
YOLOv8 may be used directly in the Command Line Interface (CLI) with a `yolo` command:
```bash
yolo predict model=yolov8n.pt source="https://ultralytics.com/images/bus.jpg"
```
`yolo` can be used for a variety of tasks and modes and accepts additional arguments, i.e. `imgsz=640` . See the YOLOv8
[CLI Docs ](https://docs.ultralytics.com/cli ) for examples.
#### Python
YOLOv8 may also be used directly in a Python environment, and accepts the
same [arguments ](https://docs.ultralytics.com/cfg/ ) as in the CLI example above:
```python
from ultralytics import YOLO
# Load a model
model = YOLO("yolov8n.yaml") # build a new model from scratch
model = YOLO("yolov8n.pt") # load a pretrained model (recommended for training)
# Use the model
model.train(data="coco128.yaml", epochs=3) # train the model
metrics = model.val() # evaluate model performance on the validation set
results = model("https://ultralytics.com/images/bus.jpg") # predict on an image
success = model.export(format="onnx") # export the model to ONNX format
```
[Models ](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/models ) download automatically from the latest
Ultralytics [release ](https://github.com/ultralytics/assets/releases ). See
YOLOv8 [Python Docs ](https://docs.ultralytics.com/python ) for more examples.
#### Known Issues / TODOs
We are still working on several parts of YOLOv8! We aim to have these completed soon to bring the YOLOv8 feature set up
to par with YOLOv5, including export and inference to all the same formats. We are also writing a YOLOv8 paper which we
will submit to [arxiv.org ](https://arxiv.org ) once complete.
- [ ] TensorFlow exports
- [ ] DDP resume
- [ ] [arxiv.org ](https://arxiv.org ) paper
< / details >
## <div align="center">Models</div>
All YOLOv8 pretrained models are available here. Detection and Segmentation models are pretrained on the COCO dataset,
while Classification models are pretrained on the ImageNet dataset.
[Models ](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/models ) download automatically from the latest
Ultralytics [release ](https://github.com/ultralytics/assets/releases ) on first use.
< details open > < summary > Detection< / summary >
See [Detection Docs ](https://docs.ultralytics.com/tasks/detection/ ) for usage examples with these models.
| 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) |
| ------------------------------------------------------------------------------------ | --------------------- | -------------------- | ------------------------------ | ----------------------------------- | ------------------ | ----------------- |
| [YOLOv8n ](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n.pt ) | 640 | 37.3 | 80.4 | 0.99 | 3.2 | 8.7 |
| [YOLOv8s ](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8s.pt ) | 640 | 44.9 | 128.4 | 1.20 | 11.2 | 28.6 |
| [YOLOv8m ](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8m.pt ) | 640 | 50.2 | 234.7 | 1.83 | 25.9 | 78.9 |
| [YOLOv8l ](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8l.pt ) | 640 | 52.9 | 375.2 | 2.39 | 43.7 | 165.2 |
| [YOLOv8x ](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x.pt ) | 640 | 53.9 | 479.1 | 3.53 | 68.2 | 257.8 |
- **mAP< sup > val</ sup > ** values are for single-model single-scale on [COCO val2017 ](http://cocodataset.org ) dataset.
< br > Reproduce by `yolo val detect data=coco.yaml device=0`
- **Speed** averaged over COCO val images using an [Amazon EC2 P4d ](https://aws.amazon.com/ec2/instance-types/p4/ )
instance.
< br > Reproduce by `yolo val detect data=coco128.yaml batch=1 device=0/cpu`
< / details >
< details > < summary > Segmentation< / summary >
See [Segmentation Docs ](https://docs.ultralytics.com/tasks/segmentation/ ) for usage examples with these models.
| 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) |
| ---------------------------------------------------------------------------------------- | --------------------- | -------------------- | --------------------- | ------------------------------ | ----------------------------------- | ------------------ | ----------------- |
| [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 |
| [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 |
| [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 |
| [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 |
| [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 |
- **mAP< sup > val</ sup > ** values are for single-model single-scale on [COCO val2017 ](http://cocodataset.org ) dataset.
< br > Reproduce by `yolo val segment data=coco.yaml device=0`
- **Speed** averaged over COCO val images using an [Amazon EC2 P4d ](https://aws.amazon.com/ec2/instance-types/p4/ )
instance.
< br > Reproduce by `yolo val segment data=coco128-seg.yaml batch=1 device=0/cpu`
< / details >
< details > < summary > Classification< / summary >
See [Classification Docs ](https://docs.ultralytics.com/tasks/classification/ ) for usage examples with these models.
| 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 |
| ---------------------------------------------------------------------------------------- | --------------------- | ---------------- | ---------------- | ------------------------------ | ----------------------------------- | ------------------ | ------------------------ |
| [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 |
| [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 |
| [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 |
| [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 |
| [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 |
- **acc** values are model accuracies on the [ImageNet ](https://www.image-net.org/ ) dataset validation set.
< br > Reproduce by `yolo val classify data=path/to/ImageNet device=0`
- **Speed** averaged over ImageNet val images using an [Amazon EC2 P4d ](https://aws.amazon.com/ec2/instance-types/p4/ )
instance.
< br > Reproduce by `yolo val classify data=path/to/ImageNet batch=1 device=0/cpu`
< / details >
## <div align="center">Integrations</div>
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< a href = "https://bit.ly/ultralytics_hub" target = "_blank" >
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< div align = "center" >
< a href = "https://roboflow.com/?ref=ultralytics" >
< img src = "https://github.com/ultralytics/assets/raw/main/partners/logo-roboflow.png" width = "10%" / > < / a >
< img src = "https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width = "15%" height = "0" alt = "" / >
< a href = "https://cutt.ly/yolov5-readme-clearml" >
< img src = "https://github.com/ultralytics/assets/raw/main/partners/logo-clearml.png" width = "10%" / > < / a >
< img src = "https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width = "15%" height = "0" alt = "" / >
< a href = "https://bit.ly/yolov5-readme-comet" >
< img src = "https://github.com/ultralytics/assets/raw/main/partners/logo-comet.png" width = "10%" / > < / a >
< img src = "https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width = "15%" height = "0" alt = "" / >
< a href = "https://bit.ly/yolov5-neuralmagic" >
< img src = "https://github.com/ultralytics/assets/raw/main/partners/logo-neuralmagic.png" width = "10%" / > < / a >
< / div >
| Roboflow | ClearML ⭐ NEW | Comet ⭐ NEW | Neural Magic ⭐ NEW |
| :--------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------: |
| 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 ) |
## <div align="center">Ultralytics HUB</div>
[Ultralytics HUB ](https://bit.ly/ultralytics_hub ) is our ⭐ **NEW** no-code solution to visualize datasets, train YOLOv8
🚀 models, and deploy to the real world in a seamless experience. Get started for **Free** now! Also run YOLOv8 models on
your iOS or Android device by downloading the [Ultralytics App ](https://ultralytics.com/app_install )!
< a href = "https://bit.ly/ultralytics_hub" target = "_blank" >
< img width = "100%" src = "https://github.com/ultralytics/assets/raw/main/im/ultralytics-hub.png" > < / a >
## <div align="center">Contribute</div>
We love your input! YOLOv5 and YOLOv8 would not be possible without help from our community. Please see
our [Contributing Guide ](CONTRIBUTING.md ) to get started, and fill out
our [Survey ](https://ultralytics.com/survey?utm_source=github&utm_medium=social&utm_campaign=Survey ) to send us feedback
on your experience. Thank you 🙏 to all our contributors!
<!-- SVG image from https://opencollective.com/ultralytics/contributors.svg?width=990 -->
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< img src = "https://github.com/ultralytics/assets/raw/main/im/image-contributors.png" / > < / a >
## <div align="center">License</div>
YOLOv8 is available under two different licenses:
- **GPL-3.0 License**: See [LICENSE ](https://github.com/ultralytics/ultralytics/blob/main/LICENSE ) file for details.
- **Enterprise License**: Provides greater flexibility for commercial product development without the open-source
requirements of GPL-3.0. Typical use cases are embedding Ultralytics software and AI models in commercial products and
applications. Request an Enterprise License at [Ultralytics Licensing ](https://ultralytics.com/license ).
## <div align="center">Contact</div>
For YOLOv8 bugs and feature requests please visit [GitHub Issues ](https://github.com/ultralytics/ultralytics/issues ).
For professional support please [Contact Us ](https://ultralytics.com/contact ).
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