Object Detection
ultralytics
YOLOv10
tracking
instance-segmentation
image-classification
pose-estimation
obb
yolo
yolov8
yolov3
yolov5
yolov9
Instructions to use Ultralytics/YOLOv8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ultralytics
How to use Ultralytics/YOLOv8 with ultralytics:
from ultralytics import YOLOvv8 model = YOLOvv8.from_pretrained("Ultralytics/YOLOv8") source = 'http://images.cocodataset.org/val2017/000000039769.jpg' model.predict(source=source, save=True) - YOLOv10
How to use Ultralytics/YOLOv8 with YOLOv10:
from ultralytics import YOLOvv8 model = YOLOvv8.from_pretrained("Ultralytics/YOLOv8") source = 'http://images.cocodataset.org/val2017/000000039769.jpg' model.predict(source=source, save=True) - Notebooks
- Google Colab
- Kaggle
Restore Colab/Kaggle/Binder/Gradient badges as fixed-width shields (markdown)
Browse files
README.md
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[](https://github.com/ultralytics/ultralytics/actions/workflows/ci.yml) [](https://clickpy.clickhouse.com/dashboard/ultralytics) [](https://discord.com/invite/ultralytics) [](https://community.ultralytics.com/) [](https://www.reddit.com/r/ultralytics/)
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[Ultralytics](https://www.ultralytics.com/) creates cutting-edge, state-of-the-art (SOTA) [YOLO models](https://www.ultralytics.com/yolo) built on years of foundational research in computer vision and AI. Constantly updated for performance and flexibility, our models are **fast**, **accurate**, and **easy to use**. 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), [semantic segmentation](https://docs.ultralytics.com/tasks/semantic), [image classification](https://docs.ultralytics.com/tasks/classify), and [pose estimation](https://docs.ultralytics.com/tasks/pose) tasks.
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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/)!
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[](https://github.com/ultralytics/ultralytics/actions/workflows/ci.yml) [](https://clickpy.clickhouse.com/dashboard/ultralytics) [](https://discord.com/invite/ultralytics) [](https://community.ultralytics.com/) [](https://www.reddit.com/r/ultralytics/)
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[](https://console.paperspace.com/github/ultralytics/ultralytics) [](https://colab.research.google.com/github/ultralytics/ultralytics/blob/main/examples/tutorial.ipynb) [](https://www.kaggle.com/models/ultralytics/yolov8) [](https://mybinder.org/v2/gh/ultralytics/ultralytics/HEAD?labpath=examples%2Ftutorial.ipynb)
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[Ultralytics](https://www.ultralytics.com/) creates cutting-edge, state-of-the-art (SOTA) [YOLO models](https://www.ultralytics.com/yolo) built on years of foundational research in computer vision and AI. Constantly updated for performance and flexibility, our models are **fast**, **accurate**, and **easy to use**. 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), [semantic segmentation](https://docs.ultralytics.com/tasks/semantic), [image classification](https://docs.ultralytics.com/tasks/classify), and [pose estimation](https://docs.ultralytics.com/tasks/pose) tasks.
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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/)!
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