InceptionV1—GoogleNet (2014)

General structure

InceptionV1 is the image classification architecture that won the LSVRC competition in 2014.

• It has a 22-layer architecture that uses the network-in-network approach for some layers that they call Inception modules.

• It’s training strategies are similar to other architectures. It has an SGD with a momentum of 0.9, fixed learning rate decreasing by 4% every 8 epochs, drop out at the fully connected layers with a rate of 0.4, ReLU activation function in Inception modules, and softmax at the end.

• Average pooling is applied between the final convolution layer and fully connected ones.

• Instead of having one fully connected head, they have three. They call two additional fully connected extensions auxiliary classifiers. The exciting part is they use these three classifier heads during training and take the average of the final weights of these different classifier heads to obtain the final and unique head to use alone in inference.