In this https://pytorch.org/vision/stable/models.html tutorial it clearly states:
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].
Does that mean that for example if I want my model to have input size 128x128 it is or if I calculate mean and std which is unique to my dataset that it is gonna perform worse or won't work at all? I know that with tensorflow if you are loading pretrained models there is a specific argument input_shape which you can set according to your needs just like here:
tf.keras.applications.ResNet101( include_top=True, weights='imagenet', input_tensor=None, input_shape=None, pooling=None, classes=1000, **kwargs)
I know that I can pass any shape to those (pytorch) pretrained models and it works. What I wanna understand is can I change input shape of those models so that I don't decrease my models training performance?