Unless it's overfit on some particular inputs, but if so-- it's bad science.
Ideally they would have trained the network on a non-overlapping collection of images from their testing but if they did that I don't see it mentioned in the paper.
The model is only 15.72 MiB (after compression with xz), so it would amortize pretty quickly... even if it was trained on the input it looks like it still may be pretty competitive at a fairly modest collection size.
Ideally they would have trained the network on a non-overlapping collection of images from their testing but if they did that I don't see it mentioned in the paper.
The model is only 15.72 MiB (after compression with xz), so it would amortize pretty quickly... even if it was trained on the input it looks like it still may be pretty competitive at a fairly modest collection size.