channelsfirst

Channelsfirst is a data layout used for multi-dimensional arrays in neural networks, where the channel dimension comes before the spatial dimensions. For 2D data, the common shape is N, C, H, W (batch size N, channels C, height H, width W); for 3D data it is N, C, D, H, W. This arrangement is often referred to as NCHW and is contrasted with channels last layouts such as N, H, W, C (NHWC).

The term is used across different machine learning frameworks, with varying defaults. PyTorch commonly employs channels first for 2D and 3D convolutions, and many models are trained in NCHW. TensorFlow and Keras historically default to channels last (NHWC), but they support channels_first by configuring data_format='channels_first' in layers and models. The choice of layout affects input shapes, weight tensors, and how operations are implemented.

Performance considerations play a role in the choice of channelsfirst. Memory layout can influence cache efficiency and the performance of convolutional routines on certain hardware. Some GPUs and libraries have optimized paths for NCHW, while others may prefer NHWC. In practice, using channels_first can improve throughput when the model and data are consistently aligned with that format, but it may require careful handling of pretrained weights and data preprocessing to avoid costly format conversions.

Practical notes: ensure consistency of data format across the entire workflow, including data loading, model definition, training, and inference. When transitioning between formats, account for potential shape changes and checkpoint compatibility. In summary, channelsfirst is one of several data layouts used to organize tensors in deep learning, with specific implications for model architecture, performance, and interoperability.