Normalizing with imagenet mean and std vs normalizing with my own dataset's mean and std

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?

You could try both approaches and check the training as well as validation losses.
Let us know, which stats worked better for your use case. :wink: