<p>When you train an image recognition model, you train it for a specific image size (and resolution), let's say n_dims = <code>[256, 256]</code>. Now, in the prediction phase, you have images of different sizes (with respect to pixels), e.g. <code>[1024, 1024]</code>. You extract patches (you can resize the image first by lowering the resolution) and hover over the image patches with your model, and for each patch, you obtain a prediction for all classes (in a patch, more than one of the objects might be present), which you have to average somehow for the whole image at the end.</p>

<p>See <a href="https://arxiv.org/abs/1312.6229" rel="nofollow noreferrer">OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks</a>.</p>

<blockquote>
  <p>Instead, we explore the entire image by densely running the network at each location and at multiple
  scales. While the sliding window approach may be computationally prohibitive for certain types
  of model, it is inherently efficient in the case of ConvNets (see section 3.5). This approach yields
  significantly more views for voting, which increases robustness while remaining efficient. The result
  of convolving a ConvNet on an image of arbitrary size is a spatial map of C-dimensional vectors at
  each scale.</p>
</blockquote>
what is class score map?
<p>I was going through <a href="https://arxiv.org/pdf/1409.1556.pdf" rel="nofollow noreferrer">vggnet</a> paper and i came across the testing phase of vggnet. </p>

<p>During the testing phase, test image goes through the vggnet and  a class score map is obtained. This class score map is spatially averaged to produce a fixed size vector. </p>

<p>I have googled class score map, but then i couldn't find any relevant results. I wish to know what is the role of class score map.</p>

<p>Any hint would be greatly helpful. Thanks</p>

<p><a href="https://i.stack.imgur.com/EaRaY.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/EaRaY.png" alt="enter image description here"></a></p>