Visual_Question_Answering

This is a simple "updated" Demo of Visual Question Answering by VQA_Demo which uses pretrained models (see VGG16 and models/VQA) to answer a given question about the given image.

Dependency

1. Keras version 2.0.4

   • Modular deep learning library based on python

2. Tensorflow

   • For the development of this project, I used Tensorflow 1.1.0

3. scikit-learn

   • Quintessential machine library for python

4. Spacy version 1.8.2

   • Used to load Glove vectors (word2vec)
   • You may have to upgrade your Spacy to use Glove vectors (default is Goldberg Word2Vec)
   • To upgrade & install Glove Vectors
     • pip install spacy
     • python -m spacy download en

5. OpenCV

   • OpenCV is used only to resize the image and change the color channels,
   • You may use other libraries as long as you can pass a 224x224 BGR Image (NOTE: BGR and not RGB)

6. VGG 16 Pretrained Weights

   • Please download the weights file vgg16_weights.h5

Usage

python demo.py

Put your test images in the dataset/ directory

Expected Output: 095.2 % train 00.67 % subway 00.54 % mcdonald's 00.38 % bus 00.33 % train station