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LeNet-5

LeNet-5 is a pioneering convolutional neural network (CNN) architecture developed by Yann LeCun and his colleagues in 1998. It was designed for handwritten digit recognition and played a significant role in the development of deep learning.

Architecture:

  • Input: 32x32 grayscale image
  • C1: Convolutional layer with 6 filters of size 5x5, followed by a subsampling layer (average pooling) with a 2x2 filter and stride of 2.
  • C3: Convolutional layer with 16 filters of size 5x5, followed by a subsampling layer (average pooling) with a 2x2 filter and stride of 2.
  • F5: Fully connected layer with 120 neurons.
  • F6: Fully connected layer with 84 neurons.
  • Output: 10 neurons corresponding to the 10 digit classes (0-9).