Comments (1)
Yes that's true. As you say, since I train it like that, it shouldn't be an issue. I did also experiment at some point on using different average images (though this was before NetVLAD) and didn't see much difference. All recent architectures (VGG, ResNet..) tend to not have the average image but just do a VGG-like input image normalization.
One major reason for doing it this way is that it enables you to run the network convolutionally, for any image resolution. If you wanted to use the mean image for arbitrary image sizes, then you would need to resize (usually downscale) and crop, loosing valuable detail.
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Related Issues (20)
- the TokyoTM database HOT 2
- pitts30k datasets HOT 9
- pitts30k datasets HOT 1
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- Applying to Indoor usage HOT 2
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- Link to download Pittsburgh 250k: available on request here showing invalid link HOT 11
- Converting MATLAB trained model .mat files (off the shelf and trained on Pittsburg 30k) in Pytorch HOT 1
- Pitts30k datasets HOT 1
- NetVLAD effectiveness in Gazebo Environments HOT 3
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