In inception v3 model, the input is transformed:
I don’t understand why pytorch does like this.
When we normalize this input , we always transform like this:
input[channel] = (input[channel] - mean[channel]) / std[channel].
But what does it mean what pytorch does? Normalize?
Hoping anyone to show some his opinion.
Thanks very much.
I guess these numbers are the average of all images from imagenet
it’s doing something of reverse-transform or unnormalize which transform the data to range(-1,1).
It’s to transform inputs normalized one way into inputs normalized differently. The Inceptionv3 model uses the pre-trained weights from Google. They assume images are normalized by: (img - 0.5) / 0.5 so that inputs are between -1 and 1.
The PyTorch data loader transforms images using (roughly):
mean=[0.485, 0.456, 0.406]
std=[0.229, 0.224, 0.225]
img[c] = (img[c] - mean[c]) / std[c]
This makes the ImageNet training set have zero-mean and unit-variance.
The code in the inception model reverses the PyTorch transformation and applies the transform from Inceptionv3.