Using ResNet18 To Classify Dogs
Image classified using a ResNet18 architecture. Image Source: Stanford-Dogs Dataset What is a ResNet? A ResNet, short form of “Residual Network,” created to solve the vanishing gradient (modifying the parameters does not result in an increase in performance) and exploding gradient (modifying the parameters results in large updates to the parameters and fails in the learning process) problems. This allows us to create larger, and more deep networks. They solved this problem by using a skip connection, where they skip some layers to find as potentially more layers are unable to learn the identity well, and allows it to try and find the difference between the output and the input. Then, you add the transformed input (output of the network) to the initial input to pass back into the network again. Implementation *This code is a modification from the PyTorch website ( https://pytorch.org/hub/pytorch_vision_resnet ). You can get my modified code on GitHub ( https://github.com/smrt...