支持的标签

['Dry_joint', 'Incorrect_installation', 'PCB_damage', 'Short_circuit']

如何使用

• 安装 ultralyticsplus :

pip install ultralyticsplus==0.0.24 ultralytics==8.0.23

• 加载模型并进行预测：

from ultralyticsplus import YOLO, render_result

# load model
model = YOLO('keremberke/yolov8m-pcb-defect-segmentation')

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

更多可用模型，请访问： awesome-yolov8-models