The Inception model

Inception was created by the team at Google in 2014. The main idea was to create deeper and wider networks while limiting the number of parameters and avoiding overfitting. The following image shows the full Inception module:

It applies multiple convolutional layers for a single input and outputs the stacked output of each convolution. The size of convolutions used are mainly 1x1, 3x3, and 5x5. This kind of architecture allows you to extract multi-level features from the same-sized input. An earlier version was also called GoogLeNet, which won the ImageNet challenge in 2014.