28x28x3

28x28x3 refers to a set of dimensions, commonly encountered in contexts related to image processing, particularly in deep learning models. The numbers represent the height, width, and number of channels of a data structure, most often a tensor or a multi-dimensional array. In this specific case, it would denote an image or a feature map with a height of 28 pixels, a width of 28 pixels, and 3 color channels.

The 28x28 aspect is frequently seen in datasets like the MNIST (Modified National Institute of Standards and Technology) dataset, which features handwritten digits. While the original MNIST dataset typically uses grayscale images (1 channel), variations or subsequent processing steps might result in 28x28 images with 3 channels. The 3 channels typically correspond to the Red, Green, and Blue (RGB) color model, even if the original image was grayscale and the channels contain identical or derived information.

In machine learning, particularly convolutional neural networks (CNNs), these dimensions are important for defining the input layer and the subsequent convolutional filters. The depth of 3 channels dictates the number of input planes a convolutional layer must expect. Understanding these dimensions is crucial for correctly setting up model architectures, ensuring compatibility between different layers, and processing input data effectively.