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modelee / yolov8n-csgo-player-detection

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README

tags:

  • ultralyticsplus
  • yolov8
  • ultralytics
  • yolo
  • vision
  • object-detection
  • pytorch
  • awesome-yolov8-models library_name: ultralytics library_version: 8.0.21 inference: false

datasets:

  • keremberke/csgo-object-detection

model-index:

  • name: keremberke/yolov8n-csgo-player-detection results:
    • task: type: object-detection

      dataset: type: keremberke/csgo-object-detection name: csgo-object-detection split: validation

      metrics:

      • type: precision # since mAP@0.5 is not available on hf.co/metrics value: 0.84441 # min: 0.0 - max: 1.0 name: mAP@0.5(box)

keremberke/yolov8n-csgo-player-detection

Supported Labels

['ct', 'cthead', 't', 'thead']

How to use

pip install ultralyticsplus==0.0.23 ultralytics==8.0.21
  • Load model and perform prediction:
from ultralyticsplus import YOLO, render_result

# load model
model = YOLO('keremberke/yolov8n-csgo-player-detection')

# 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()

More models available at: awesome-yolov8-models

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