I am using a pre-trained network with imagenet data .Tthe mean and std of imagenet are
mean = [0.485, 0.456, 0.406]
std = [0.229, 0.224, 0.225]
whereas, I have 1000 images in my dataset.
Mean and std of my dataset are :
mean = [0.5589, 0.3216, 0.2356]
std =[ 0.3056, 0.21442, 0.1775]
I am confused if I should use imagenet MEAN AND STD or use my own mean and std to normalize my images?
Is there any way to find if fine-tuning is good or feature extraction i.e. updating the last layer in good for this case?