YOLOv8追踪内存占用过大问题

Question

yolov8追踪时，30s帧率为30的一个视频，在追踪过程中提交内存达到了40G，12G显存也快占满了，是yolov8的计算量就这么大吗，有什么不降低速度和精度的办法来减少占用内存吗？麻烦解答一下

用的是yolov8官方提供的代码：

import cv2
from ultralytics import YOLO

# Load the YOLOv8 model
model = YOLO('yolov8n.pt')

# Open the video file
video_path = "path/to/video.mp4"
cap = cv2.VideoCapture(video_path)

# Loop through the video frames
while cap.isOpened():
    # Read a frame from the video
    success, frame = cap.read()

    if success:
        # Run YOLOv8 tracking on the frame, persisting tracks between frames
        results = model.track(frame, persist=True)

        # Visualize the results on the frame
        annotated_frame = results[0].plot()

        # Display the annotated frame
        cv2.imshow("YOLOv8 Tracking", annotated_frame)

        # Break the loop if 'q' is pressed
        if cv2.waitKey(1) & 0xFF == ord("q"):
            break
    else:
        # Break the loop if the end of the video is reached
        break

# Release the video capture object and close the display window
cap.release()
cv2.destroyAllWindows()

Answer 1

results可能会占用大量内存，尤其是当results包含大量检测和追踪信息时。如果你确信某个对象之后不会再被使用，可以使用del关键字来删除这个对象，之后调用gc.collect()来显式触发垃圾回收。

del results
del annotated_frame  
gc.collect()