yolov8x-visdrone

<div align="center"> <img width="640" alt="mshamrai/yolov8x-visdrone" src="https://huggingface.co/mshamrai/yolov8x-visdrone/resolve/main/thumbnail.jpg"> </div>

Supported Labels

['pedestrian', 'people', 'bicycle', 'car', 'van', 'truck', 'tricycle', 'awning-tricycle', 'bus', 'motor']

How to use

• Install ultralyticsplus:

pip install ultralyticsplus==0.0.28 ultralytics==8.0.43

• Load model and perform prediction:

from ultralyticsplus import YOLO, render_result

# load model
model = YOLO('mshamrai/yolov8x-visdrone')

# 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()