Visual Question Answering

Based on the VQA challenge and paper, this is just a simple implementation of a pretrained model on a flask server.
online demo: Hasura ( can only run images below 1Mb due to server limits)
Note: This is a python 2 program

Installation

0. Install dependencies:

pip install -r requirements.txt

1. Download weights from here: G-Drive and add it to resources folder.
2. Download nltk punkt data (for tokenization) using python -c "import nltk; nltk.download('punkt')".
3. VGG19 model weights are needed which will start downloading automatically once the server.py runs, or can be downloaded using python -c "from keras.applications.vgg19 import VGG19;VGG19(weights='imagenet')". Note that this will happen only once
4. Run the demo.

FLASK_APP=server.py flask run -h 0.0.0.0 -p 8000

Credits

This code and weights are based on @anantzoid's VQA code.