chanelcolgate/tennis-analysis-v1

tags:

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

libraryname: ultralytics libraryversion: 8.0.239 inference: false

datasets:

• —chanelcolgate/yenthienviet

model-index:

• —name: chanelcolgate/tennis-analysis-v1 results:
• —task: type: object-detection

dataset: type: chanelcolgate/yenthienviet name: yenthienviet split: validation

metrics:

• —type: precision # since mAP@0.5 is not available on hf.co/metrics value: 0.86718 # min: 0.0 - max: 1.0 name: mAP@0.5(box) ---

<div align="center"> <img width="640" alt="chanelcolgate/tennis-analysis-v1" src="https://huggingface.co/chanelcolgate/tennis-analysis-v1/resolve/main/thumbnail.jpg"> </div>

Supported Labels

['tennis ball']

How to use

• —Install ultralyticsplus:

pip install ultralyticsplus==0.1.0 ultralytics==8.0.239

• —Load model and perform prediction:

from ultralyticsplus import YOLO, render_result

# load model
model = YOLO('chanelcolgate/tennis-analysis-v1')

# 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()