SpaceToDepth
SpaceToDepth is a data rearrangement operation used in computer vision and deep learning. It converts spatial information into the channel dimension by moving blocks of pixels from the height and width into the depth (channel) dimension, thereby reducing spatial resolution while increasing the number of channels.
Formally, for an input tensor with shape (N, H, W, C) and a block size r, where
SpaceToDepth is the inverse operation of DepthToSpace. It is used to reformat feature maps to enable efficient
Advantages of SpaceToDepth include the ability to downsample spatially while preserving information in the depth dimension,