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36 lines
1.5 KiB
Markdown
36 lines
1.5 KiB
Markdown
ultralytics
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===========
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[Ultralytics][1] creates cutting-edge, state-of-the-art (SOTA) YOLO models built on
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years of foundational research in computer vision and AI.
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> Models can be downloaded from [here][2].
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```bash
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$ docker run --rm -it --ipc=host ultralytics/ultralytics:latest-arm64 python
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>>> from ultralytics import YOLO
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>>> model = YOLO("yolo11n.pt", save_txt=True)
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>>> print(model.names)
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{0: 'person', 1: 'bicycle', 2: 'car', 3: 'motorcycle', 4: 'airplane', 5: 'bus', 6: 'train', 7: 'truck', ...}
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>>> results = model("https://ultralytics.com/images/bus.jpg")
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>>> for r in results: print(r.boxes.xywh)
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tensor([[400.0137, 478.8882, 792.3618, 499.0482],
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[740.4135, 636.7728, 138.7925, 483.8793],
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[143.3527, 651.8801, 191.8959, 504.6299],
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[283.7633, 634.5621, 121.4087, 451.7472],
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[ 34.4536, 714.2138, 68.8638, 316.2908]])
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```
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```bash
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$ docker run --rm -it --ipc=host ultralytics/ultralytics:latest-arm64 bash
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>>> yolo classify predict model=yolo11n-cls.pt source=https://ultralytics.com/images/bus.jpg save_txt=True
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>>> ls /ultralytics/runs/classify
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>>> yolo detect predict model=yolo11n.pt source=https://ultralytics.com/images/bus.jpg save_txt=True
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>>> ls /ultralytics/runs/detect
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>>> yolo solutions count model=yolo11n.pt classes="[2,5,7]" source=https://basicai-asset.s3.amazonaws.com/www/blogs/yolov8-object-counting/street.mp4
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>>> ls /ultralytics/runs/solutions
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```
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[1]: https://github.com/ultralytics/ultralytics
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[2]: https://github.com/ultralytics/assets/releases/latest
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