keremberke/yolov8n-csgo-player-detection

tags:

• —ultralyticsplus
• —yolov8
• —ultralytics
• —yolo
• —vision
• —object-detection
• —pytorch
• —awesome-yolov8-models libraryname: ultralytics libraryversion: 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) ---

<div align="center"> <img width="640" alt="keremberke/yolov8n-csgo-player-detection" src="https://huggingface.co/keremberke/yolov8n-csgo-player-detection/resolve/main/thumbnail.jpg"> </div>

Supported Labels

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

How to use

• —Install ultralyticsplus:

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](https://yolov8.xyz)