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YOLOv3 Wrong Bounding Box Prediction

I trained YOLOv3 for 1 class. When I try to predict find my object in the picture it gives me wrong bounding box coordinates and shape.

I've trained yolov3-tiny version with 70 608x608 images. It was working perfect. Even I trained it with CPU and around 100 images it detects my object accurately.

yolov3-tiny cfg settings: batch=64 subdivisions=2 width=416 height=416

yolov3-tiny detection

I found 70 images more. However they are 640x480 images. I resized all images to 640x480 size. Then I trained yolov3-tiny and yolov3 on Google Colab with 170 images. Although the training results show 0.08 accuracy I get terrible detection results.

yolov3.cfg settings:

batch=1 subdivisions=1 width=640 height=480

yolov3 detection

I wonder that why results got worse. How can I have accurate detection?

Solution

  • [Updated]

    I recall YOLO handles resizing for you keeping aspect ratio. Have you tried running it without changing width and height in cfg?

    You should be able to run the training without rescaling your input images. Try running that previous config that worked for you but use new images?

    Just noticed you also changed batch size and subdivisions. The values you provided in the second config might be too low for mode to learn anything. Have you tried going back to previous settings? (batch=64, subdivisions=2)