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I have a fully convolutional network that has been trained to classify cats and non-cats in small images (48x48). Because it is fully convolutional, I would expect that if I run it in bigger images, the result will be a classification map with overlapping 48x48 patches where I can predict bounding boxes. However, I am not sure how to convert this map (and the bounding boxes) to the original input dimensions.

I have tried something along the lines of: $$ x_i = (\left \lfloor{x_{c,1} * R}\right \rfloor, \left \lceil{x_{c,2} * R}\right \rceil) $$ where $x_{c}$ represents the x-coordinates of the bounding box in the classification map and $x_i$, in the input image space. $R$ is the ratio between the sizes of the classification map and input image. This doesn't work.

Can someone please explain how to do it? Thanks!

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