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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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- image-segmentation |
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- pytorch |
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- awesome-yolov8-models |
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library_name: ultralytics |
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library_version: 8.0.23 |
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inference: false |
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datasets: |
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- keremberke/pcb-defect-segmentation |
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model-index: |
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- name: keremberke/yolov8m-pcb-defect-segmentation |
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results: |
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- task: |
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type: image-segmentation |
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dataset: |
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type: keremberke/pcb-defect-segmentation |
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name: pcb-defect-segmentation |
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split: validation |
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metrics: |
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- type: precision |
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value: 0.56836 |
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name: mAP@0.5(box) |
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- type: precision |
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value: 0.5573 |
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name: mAP@0.5(mask) |
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--- |
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<div align="center"> |
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<img width="640" alt="keremberke/yolov8m-pcb-defect-segmentation" src="https://huggingface.co/keremberke/yolov8m-pcb-defect-segmentation/resolve/main/thumbnail.jpg"> |
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</div> |
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### Supported Labels |
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``` |
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['Dry_joint', 'Incorrect_installation', 'PCB_damage', 'Short_circuit'] |
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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==0.0.24 ultralytics==8.0.23 |
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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('keremberke/yolov8m-pcb-defect-segmentation') |
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# set model parameters |
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model.overrides['conf'] = 0.25 # NMS confidence threshold |
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model.overrides['iou'] = 0.45 # NMS IoU threshold |
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model.overrides['agnostic_nms'] = False # NMS class-agnostic |
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model.overrides['max_det'] = 1000 # 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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print(results[0].masks) |
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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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**More models available at: [awesome-yolov8-models](https://yolov8.xyz)** |
