misoda/yolo-mlw

Supported Labels

['lemma']

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('misoda/yolo-mlw')

# 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()