README

Code to reproduce the results for the NeurIPS 2021 paper "Towards
Context-Agnostic Learning Using Synthetic Data" 


INSTRUCTIONS

The subdirectories contains code to train and test a model from scratch
> python train.py
> python test.py


DATASETS

Both synthetic datasets used for training (Font, Picto) are included. For
testing, both the MNIST and the GTSRB dataset need to be downloaded. PyTorch 
handles MNIST automatically but GTSRB needs to be downloaded prior to running 
the test script. The link for the archive is at
https://sid.erda.dk/public/archives/daaeac0d7ce1152aea9b61d9f1e19370/published-archive.html
under "GTSRB_Final_Test_Images.zip". This archive needs to be extracted to 
gtsrb/data/GTSRB_Test/Images. For convenience we have also included the shell
script get_gtsrb.sh which downloads and extracts the GTSRB test images.


RESULTS

Pretrained models for each benchmark are in checkpoint/pretrained.pth. These 
can be tested by uncommenting a line in test.py. Note that we report the model
with the best performance on the test set, checking every 5 epochs. The 
performance at the final epoch is typically a few percentage points lower.

MNIST (trained with Font)
90.2%

GTSRB (trained with Picto)
95.9%


DEPENDENCIES

We used Python 3.9.7 for all experiments
> pip install -r requirements.txt