Visual Question Answering project for ETHZ's 2016/17 Deep Learning class.

TODO(name): Signals the author of the TODO, who will probably know a lot about it, but it's not necessarily a commitment---anyone can tackle any TODO. It's actually encouraged!

This project is designed to be run on AWS/Azure GPU instances. The format I'm currently using expects the following folder structure for the data on the remote instance:

/data
└── vqa
    ├── Annotations
    ├── Images
    │   └── mscoco
    │       ├── test2015
    │       ├── train2014
    │       └── val2014
    ├── Preprocessed
    └── Questions

This folder structure uses the format expected by the official VQA evaluation framework from https://github.com/VT-vision-lab/VQA.

1. Provision an AWS/Azure GPU instance with Ubuntu.
2. Prepare the data, as indicated in the 'Layout' section. Run fab setup_conda to install some dependencies of the evaluation code.
3. Run fab preprocess to preprocess the data for training. Run preprocess.py -h for more information and available options.
4. Run fab train to train the default model (i.e., the baseline at the moment). This will kick off in a screen on the host, and the script will detach. You can SSH into the remote host and attach to that script to check the progress!
5. After the training completes, run fab eval to generate the predictions on the validation set, and crunch them through the VQA evaluation tools, producing many cool stats, such as an accuracy breakdown based on question and answer types. This task is a little finniky and may need some manual tweaking in fabfile.py at the moment in order to work. Also, bear in mind that the VQA subproject requires Python 2.7.

   TODO(andrei): Update this info once tooling improves.