I'm using yolov11 to train a model to recognize a toy truck. I followed the steps in this EXCELLENT youtube video, and I have a working model build off of yolov11m.pt. I'm using the following admittedly naaive script to launch a stream from my webcam and pass captured images through the model from ultralytics import YOLO import cv2 # load hyper tuned model model = YOLO('/home/ultralytics/runs/detect/train4/weights/best.pt') model.to("cuda") # establish and open webcam feed cap = cv2.VideoCapture(2) if not cap.isOpened(): print("Cannot open camera") exit(1) while True: ret, frame = cap.read() if not ret: print("Cannot read camera") exit(2) # pass frame through model frame_resized = cv2.resize(frame, (640, 480)) res = model.predict(source=frame_resized, show=True, conf=0.45) #Display resulting frame cv2.imshow('Stream', res[0].plot()) # Break loop on 'q' for quit if cv2.waitKey(1) == ord('q'): break I can't help but notice just how agonizingly choppy the resulting feed looks. Can anyone tell me why? For example, when I run the regular yolov5 repo using a simple call to detect.py, it seems flawlessly fast. I'm looking to build some intuition as to how i could render my model's detection boxes at a similar rate of speed. Any and all help is appreciated. I've got a pretty heavy duty graphics card behind this which runs yolov5 flawlessly. Continue reading...
