Hi,
I would like to apply pre-trained model (Ex. VGG16 or Resnet50) on my dataset. My dataset doesn’t consist of normal images (3 channels RBG (0, 255)). They are remote sensing data with size (2, 75, 75) and the values range from (-25, 0).
Quoted from http://pytorch.org/docs/master/torchvision/models.html: 3
All pre-trained models expect input images normalized in the same way, i.e. mini-batches of 3-channel RGB images of shape (3 x H x W), where H and W are expected to be at least 224. The images have to be loaded in to a range of [0, 1] and then normalized using mean = [0.485, 0.456, 0.406] and std = [0.229, 0.224, 0.225].
However, my dataset doesn’t fit the size and range. What is the best practical way to transfer learning on such a data set? Thanks very much!