Channelslast

Channels_last is a data layout convention for multi-dimensional tensors in which the channel dimension is placed last. In the common four-dimensional case for batches of images, this yields shapes such as (batch, height, width, channels). The alternative layout, channels_first, places the channel dimension immediately after the batch dimension, e.g., (batch, channels, height, width). Many frameworks provide options to choose between these formats, and data_format settings or memory formats determine how tensors are stored and processed.

In practice, channels_last is the default or common choice in TensorFlow and Keras, where image data are often represented as NHWC (batch, height, width, channels). PyTorch typically uses channels_first (NCHW) by default, but can operate with a channels_last memory format (torch.channels_last) for certain operations, offering flexibility to optimize performance on some hardware.

Choosing a data format affects model construction and data preprocessing. Layer configurations for convolution, pooling, normalization, and other operations may specify the expected data format, and input data or pretrained weights must conform to that format. Interoperability with datasets, libraries, or pre-trained models can require layout conversions, which may involve transposition or reordering steps.

Advantages of channels_last include alignment with common image file formats and potential memory access benefits on particular backends. Disadvantages can include the need for data format conversions when integrating components that assume a different layout. See also NCHW, NHWC, and data formats.