great overview, nice to see Xception is mentioned as an emerging backbone network - this is what i'm working with right nowhttps://twitter.com/graphific/status/1041627931842420742 …
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Hi Helena, all modern CNNs operate downsampling on their inputs, which means they start with large feature maps that get smaller as you do down the network. In general, convolution is a downsampling operating unless you explicit pad your inputs.
Thats means that all CNNs have a minimum input size, which is the minimum size you can call the network on without resulting in empty feature maps. At that size, the output of your network is a 1x1 feature map.
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For most nets, this tends to be about ~50% of the input size the network was designed for. Downsampling is absolutely necessary when you have large inputs. To get rid of this limitation, you need to create your own network, where you would do less downsampling.
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For instance, you could edit Xception and 1) remove max pooling layers, 2) use "same" padding in all convolution layers. This would create a version of Xception that would be compatible with smaller input sizes.
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