ptrblck:

Did you check, if the green color channel of the input images was “adjusted”?


Good idea. The models are trained on imagenet but I don’t have this dataset so I can’t (yet) do this. I guess you could compare the relative values of the image channels, similarily as to was done with the weights. And it would be of interest to do this after the transformations/normalizations that are applied to the input images, as this is what actually goes into the models.
To my knowledge, all the models were trained with this normalization.
transforms.Normalize(mean=[0.485, 0.456, 0.406],
                             std=[0.229, 0.224, 0.225])

I’m not sure, but I believe that these parameter values were derived from the imagenet dataset so that normalized images would resemble a certain distribution, e.g. mean=0, std=1. If we assume that this is true, then ->
The normalization parameters are quite similar for the different channels which suggest that imagenet doesn’t have large differences for the RGB-channels. Idk if my reasoning holds here, but it makes sense to me.



 ptrblck:

However, as a red-green color blind person, I cannot confirm the assumption of a higher sensitivity towards red and green.