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uisikdag/yolo-v8-football-players-detection

sourceHugging Faceupdated 4y agoView on Hugging Face
9likes580downloads
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tags:

  • —ultralyticsplus
  • —yolov8
  • —ultralytics
  • —yolo
  • —vision
  • —object-detection
  • —pytorch

libraryname: ultralytics libraryversion: 8.0.25 inference: false

model-index:

  • —name: uisikdag/footballplayersrf results:
  • —task: type: object-detection

metrics:

  • —type: precision # since mAP@0.5 is not available on hf.co/metrics value: 0.78517 # min: 0.0 - max: 1.0 name: mAP@0.5(box) ---

<div align="center"> <img width="640" alt="uisikdag/footballplayersrf" src="https://huggingface.co/uisikdag/footballplayersrf/resolve/main/thumbnail.jpg"> </div>

Supported Labels

['ball', 'goalkeeper', 'player', 'referee']

How to use

bash
pip install ultralyticsplus==0.0.25 ultralytics==8.0.25
  • —Load model and perform prediction:
python
from ultralyticsplus import YOLO, render_result

# load model
model = YOLO('uisikdag/football_players_rf')

# set model parameters
model.overrides['conf'] = 0.25  # NMS confidence threshold
model.overrides['iou'] = 0.45  # NMS IoU threshold
model.overrides['agnostic_nms'] = False  # NMS class-agnostic
model.overrides['max_det'] = 1000  # maximum number of detections per image

# set image
image = 'https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg'

# perform inference
results = model.predict(image)

# observe results
print(results[0].boxes)
render = render_result(model=model, image=image, result=results[0])
render.show()