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add ultralytics model card

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  1. README.md +83 -0
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+
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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: 8.0.20
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+ inference: false
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+
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+ datasets:
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+ - keremberke/pothole-segmentation
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+
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+ model-index:
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+ - name: keremberke/yolov8m-pothole-segmentation
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+ results:
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+ - task:
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+ type: image-segmentation
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+
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+ dataset:
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+ type: keremberke/pothole-segmentation
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+ name: pothole-segmentation
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+ split: validation
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+
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+ metrics:
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+ - type: precision # since [email protected] is not available on hf.co/metrics
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+ value: 0.82441 # min: 0.0 - max: 1.0
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+ name: [email protected](box)
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+ - type: precision # since [email protected] is not available on hf.co/metrics
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+ value: 0.8037 # min: 0.0 - max: 1.0
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+ name: [email protected](mask)
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+ ---
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+
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+ <div align="center">
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+ <img width="640" alt="keremberke/yolov8m-pothole-segmentation" src="https://huggingface.co/keremberke/yolov8m-pothole-segmentation/resolve/main/thumbnail.jpg">
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+ </div>
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+
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+ ### Supported Labels
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+
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+ ```
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+ ['pothole']
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+ ```
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+
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+ ### How to use
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+
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+ - Install [ultralyticsplus](https://github.com/fcakyon/ultralyticsplus):
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+
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+ ```bash
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+ pip install ultralyticsplus==0.0.21
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+ ```
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+
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+ - Load model and perform prediction:
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+
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+ ```python
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+ from ultralyticsplus import YOLO, render_result
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+
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+ # load model
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+ model = YOLO('keremberke/yolov8m-pothole-segmentation')
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+
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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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+
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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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+
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+ # perform inference
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+ results = model.predict(image)
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+
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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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+