- Most commonly used for image classification to sort images into different categories, e.g., keyword search on images.

University of Michigan - Ann Arbor

- AlexNet is trained on 227 x 227 RGB images.
- The first layer has 96, 11 x 11 filters with a stride of four. The output is passed to a 3 x 3 max pooling with a stride of two.
- The second layer has 256, 5 x 5 same convolution filters that pass the results to a 3 x 3 max pooling with a stride of two.
- The third layer has 384, 3 x 3 same convolution filters.
- The fourth layer has 384, 3 x 3 same convolution filters.
- The fifth layer has 256, 3 x 3 same convolution filters that pass the results to a 3 x 3 max pooling with a stride of two.
- Two fully connected layers get the output and pass it to a softmax output layer to detect one of 1,000 classes.

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