DanielCerda/pid-object-detection
Updated • 55 • 2
How to use DanielCerda/pid_yolov8 with ultralytics:
from ultralytics import YOLOvv8
model = YOLOvv8.from_pretrained("DanielCerda/pid_yolov8")
source = 'http://images.cocodataset.org/val2017/000000039769.jpg'
model.predict(source=source, save=True)['ball-valve', 'butterfly-valve', 'centrifugal-pump', 'check-valve', 'gate-valve']
pip install ultralyticsplus==0.0.29 ultralytics==8.0.239
from ultralyticsplus import YOLO, render_result
# load model
model = YOLO('DanielCerda/pid_yolov8')
# 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()