The AlexNet model

AlexNet, a modification of LeNet, was designed by the group named SuperVision, which was composed of Alex Krizhevsky, Geoffrey Hinton, and Ilya Sutskever. AlexNet made history by achieving the top-5 error percentage of 15.3%, which was 10 points more than the runner-up, in the ImageNet Large Scale Visual Recognition Challenge in 2012.

The architecture uses five convolutional layers, three max pool layers, and three fully connected layers at the end, as shown in the following diagram. There were a total of 60 million parameters in the model trained on 1.2 million images, which took about five to six days on two NVIDIA GTX 580 3GB GPUs. The following image shows the AlexNet model:

Convolutional Layer 1 | Max Pool Layer 1 | Normalization Layer 1| Convolutional Layer 2 | Max Pool Layer 2 |Normalization Layer 2 |Convolutional Layer 3 |Convolutional layer 4 | Convolutional Layer 5 | Max Pool Layer 3 |Fully Connected 6 |Fully Connected 7 |Fully Connected 8 | Output