Deep Residual Networks for Image Classification with Python NumPy

A description of the main concepts that permitted the goals achieved in the last decade, an introduction of image classification and object localization problems, ILSVRC and the models that obtained best results from 2012 to 2015 in both the tasks. This chapter contains an explanation on how to implement both forward and backward steps for each one of the layers used by the residual model, the residual model's implementation and some method to test a network before training. After developed the model and a solver to train it, I conducted several experiments with the residual model on CIFAR-10, in this chapter I show how I tested the model and how the behavior of the network changes when one removes the residual paths, applies data-augmenting functions to reduce overfitting or increases the number of the layers, then I show how to foil a trained network using random generated images or images from the dataset. Here I describe other results obtained training the same model on MNIST and SFDDD (check below for more infos), an overview of the project and possible future works with it. Below I describe in brief how I got all of that, the sources I used, the structure of the residual model I trained and the results I obtained. Please keep in mind that my first objective was to develop and train the model so I didn't spent much time on the design aspect of the framework, but I'm working on it (and pull requests are welcome)! When I started to think I wanted to implement "Deep Residual Networks for Image Recognition", on GitHub there was only this project from gcr, based on Lua Torch, this code really helped me a lot when I had to implement the residual model. Neural Networks and Deep Learning by Michael Nielsen contains a really well organized exhaustive introduction to the subject and a lot of code to help the user understand what is going on on each part of the process.