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--- |
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tags: |
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- ultralyticsplus |
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- yolov8 |
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- ultralytics |
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- yolo |
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- vision |
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- object-detection |
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- pytorch |
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library_name: ultralytics |
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library_version: 8.0.43 |
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inference: false |
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model-index: |
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- name: eeshawn11/naruto_hand_seal_detection |
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results: |
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- task: |
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type: object-detection |
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metrics: |
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- type: precision |
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value: 0.995 |
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name: mAP@0.5(box) |
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--- |
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<div align="center"> |
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<img width="640" alt="eeshawn11/naruto_hand_seal_detection" src="https://huggingface.co/eeshawn11/naruto_hand_seal_detection/resolve/main/thumbnail.jpg"> |
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</div> |
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### Supported Labels |
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``` |
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['bird', 'boar', 'dog', 'dragon', 'hare', 'horse', 'monkey', 'ox', 'ram', 'rat', 'snake', 'tiger'] |
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``` |
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### How to use |
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- Install [ultralyticsplus](https://github.com/fcakyon/ultralyticsplus): |
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```bash |
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pip install ultralyticsplus==0.0.28 |
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``` |
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- Load model and perform prediction: |
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```python |
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from ultralyticsplus import YOLO, render_result |
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# load model |
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model = YOLO('eeshawn11/naruto_hand_seal_detection') |
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# set model parameters |
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model.overrides['conf'] = 0.50 # NMS confidence threshold |
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model.overrides['iou'] = 0.70 # NMS IoU threshold |
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model.overrides['agnostic_nms'] = False # NMS class-agnostic |
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model.overrides['max_det'] = 10 # maximum number of detections per image |
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# set image |
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image = 'https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg' |
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# perform inference |
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results = model.predict(image) |
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# observe results |
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print(results[0].boxes) |
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render = render_result(model=model, image=image, result=results[0]) |
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render.show() |
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``` |
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