LeNet
LeNet is a family of convolutional neural networks developed by Yann LeCun and collaborators at Bell Labs for handwritten digit recognition. The most influential variant, LeNet-5, was introduced in the late 1990s and demonstrated end-to-end learning from raw pixel data to digit labels. LeNet helped establish convolutional neural networks as a practical tool for pattern recognition and remains foundational in the history of deep learning.
LeNet-5 processes greyscale images, typically 32 by 32 pixels. Its architecture combines convolutional layers, subsampling (pooling)
LeNet-5 incorporated local receptive fields and weight sharing, and used a relatively small parameter count by