Should input images be normalized to -1 to 1 or 0 to 1 Many ML tutorials are normalizing input images to value of -1 to 1 before feeding them to ML model. The ML model is most likely a few conv 2d layers followed by a fully connected layers. Assuming activation function is ReLu. 

My question is, would normalizing images to [-1, 1] range be unfair to input pixels in negative range since through ReLu, output would be 0. Would normalizing images into [0, 1] range instead be a better idea?

Thank you.
As the other answers previously said, in practice it doesn't have much difference which of the two you choose. However, theoretically it's better to scale your input to $[-1, 1]$ than $[0, 1]$ and I'd argue that it's even better to standardize your input (i.e. $μ=0$, $σ=1$). 

Let me explain why:

Deep neural networks, especially in their early days, had much trouble with backpropagation, as they suffered from vanishing/exploding gradients. A popular way to combat this is by initializing the weights of the network in smarter ways. Two initialization techniques have proven to be the most popular: Glorot initialization (sometimes referred to as Xavier initialization) and He initialization, both of which are variations of the same idea. 

In order to derive their intialization they make a few assumptions, one of which is that the input features have a zero mean. You can read this post for simplified explanation, but I'd suggest reading the two papers mentioned above.