In the development of CNN classifiers, the inception network is a very important milestone. Before the inception network came into the picture, CNNs used to just stack the convolutional layers to the utmost depths in order to achieve better performance. Inception networks use complex techniques and tricks to meet performance both in terms of speed and accuracy.
Inception networks are evolving constantly and have led to the birth of several new versions of the network. Some of the popular versions are—Inception-v1, v2, v3, v4, and Inception-ResNet. Since there can be huge variations in salient parts and the location of information in images, choosing the right kernel size for the convolution operation becomes tough. A larger ...
