Awesome

Keras implementation of "Wide Residual Networks"

This repo contains the code to run Wide Residual Networks using Keras.

Paper (v1): http://arxiv.org/abs/1605.07146v1 (the authors have since published a v2 of the paper, which introduces slightly different preprocessing and improves the accuracy a little).
Original code: https://github.com/szagoruyko/wide-residual-networks

pip install -r requirements.txt
To plot the architecture of the model used (like the plot of the WRN-16-2 architecture plotted below), you need to install pydot and graphviz. I recommend installing with conda install -c conda-forge python-graphviz:

Run the default configuration (i.e. best configuration for CIFAR10 from original paper/code, WRN-28-10 without dropout) with:

$ python main.py

There are three configuration sections at the top of main.py:

DATA CONFIGURATION: Containing data details.
NETWORK/TRAINING CONFIGURATION: Includes the main parameters the authors experimented with.
OUTPUT CONFIGURATION: Defines paths regarding where to save model/checkpoint weights and plots.

WRN-28-10 no dropout:
- Using these values in main.py, I obtained a test loss = 0.31 and test accuracy = 0.93. This test error (i.e. 1 - 0.93 = 7%) is a little higher than the reported result (Table 4 states the same model obtains a test error of 4.97%); see the note below for a likely explanation.
- You can find the trained weights for this model at models/WRN-28-10.h5, whilst models/test.py provides an example of running these weights against the test set.

Note: I have not followed the exact same preprocessing and data augmentation steps used in the paper, in particular:

"global contrast normalization", and
"random crops from image padded by 4 pixels on each side, filling missing pixels with reflections of original image", which appears to be implemented in this file.

Ideally, we will add such methods directly to the Keras image preprocessing script.

WRN-16-2 Architecture