The reason that ChannelsLast is preferred for vision models is because XNNPACK (kernel acceleration library) used by PyTorch expects all inputs to be in Channels Last format, so if the input to the model isn’t channels last, then it must first be converted to channels last, which is an additional operation.
Additionally, most PyTorch operators preserve the input tensor’s memory format, so if the input is Channels First, then the operator needs to first convert to Channels Last, then perform the operation, and then convert back to Channels First.
When you combine it with the fact that accelerated operators work better with a channels last memory format, you’ll notice that having the operator return back a channels-last memory format is better for subsequent operator calls or you’ll end up having every operator convert to channels-last (should it be more efficient for that specific operator).
From the XNNPACK home page: