dataformatchannelslast

Data format channels last, often referred to as channels_last, is a data layout convention used for multi-dimensional data, especially images and feature maps in neural networks. In this layout, the channel dimension is the final axis. For a four-dimensional tensor representing a batch of images, the common shape is (batch, height, width, channels), abbreviated as NHWC. This contrasts with channels first layouts such as NCHW, where the channel dimension comes immediately after the batch size.

In practice, many deep learning frameworks expose a data_format or memory_format option to specify channels last. For example, TensorFlow and Keras typically use channels_last by default in image processing, resulting in shapes like (batch, height, width, channels). PyTorch, by contrast, commonly uses channels first (NCHW) for its convolutional operators, though support for alternative formats exists with appropriate data transformations and flags.

Advantages of channels last include closer alignment with common image representations (height and width preceding channels) and often improved compatibility with certain image processing libraries and some hardware accelerators. It can also simplify handling variable numbers of channels, such as grayscale (1 channel) versus color (3 channels). However, switching between formats can require data transposition, which introduces overhead, and some libraries or layers may be optimized for a particular layout, potentially affecting performance.

In summary, data format channels last is a widely used convention that stores the channel dimension as the last axis, with NHWC as a typical shape in four-dimensional tensors. It is one of the principal alternatives to channels first (NCHW) in neural network data pipelines.