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rbgirshick / py-faster-rcnn

Faster R-CNN (Python implementation) -- see https://github.com/ShaoqingRen/faster_rcnn for the official MATLAB version
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Changing stride length decreased object detection accuracy #294

Open ravikantb opened 8 years ago

ravikantb commented 8 years ago

Hi,

As per the paper, 'accuracy may be further improved with a smaller stride'. I changed the default 16 stride during anchor generation to 5 pixels. But it decreased the overall object detection accuracy. Does anyone have any idea as to what might cause this? I have trained my model on a single object and my test images also have many instances of that object only albeit in different sizes.

aceimnorstuvwxz commented 7 years ago

where did you change it ? I am do the same thing too, I set the first conv layer's stride from 2 to 1, but things get very bad in final testing mAP, from 58 to 10, I think some where is wrong...

ravikantb commented 7 years ago

The paper mentions that:

On the re-scaled images, the total stride for both ZF and VGG nets on the last convolutional layer is 16 pixels, and thus is ∼10 pixels on a typical PASCAL image before resizing (∼500×375). Even such a large stride provides good results, though accuracy may be further improved with a smaller stride.

But the only place where I could 16 stride was in ROI proposal layer named 'proposal'. I tried to play with it and didn't get good results. Do let me know what kind of results you get if you change it :)

janismdhanbad commented 6 years ago

Hi @ravikantb @fateleak , did you get any logical way to choose strides for a particular network?

ravikantb commented 6 years ago

@janismdhanbad : sorry but I change this 'stride' parameter based on the dataset I am working with. Its more of intuition based which comes from playing with more and more datasets and failing and trying time and again :) If your objects are small (not covering a lot of image) and sparse (spaced across image, not closely packed) then I suggest you to try longer strides. Will speed up the processing as well. Good luck, let us know what approach worked for your work.