<p>Looking at the TensorFlow definition of Spatial pyramid pooling (<a href="https://github.com/tensorflow/addons/blob/v0.11.2/tensorflow_addons/layers/spatial_pyramid_pooling.py" rel="nofollow noreferrer">link</a>), it seems like the output is flattened. So in that case, only 1D convolution would make sense. However, if there are any spatial information left for a convolution to learn I don't know.</p>
<p><strong>EDIT:</strong> After a quick investigation of what the Spatial pyramid pooling algorithm does, I do not think that it is a good idea to use any convolution. That is because there is no real spatial information left in the data. That is also how it is used, see for example <a href="https://arxiv.org/abs/1406.4729" rel="nofollow noreferrer">this article</a>.</p>
Fully connected after Spatial Pyramid Pooling
<p>Must there be a fully connected layer after a Spatial pyramid pooling layer when doing classification? Or can convolutional layers be used after the Spatial pyramid pooling layer?</p>