# VQA_Demo

**Repository Path**: skyarn/VQA_Demo

## Basic Information

- **Project Name**: VQA_Demo
- **Description**: Visual Question Answering Demo on pretrained model
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 1
- **Forks**: 0
- **Created**: 2020-03-28
- **Last Updated**: 2020-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# VQA Demo

**Updated** to work with Keras 2.0 and TF 1.2 and Spacy 2.0
This code is meant for education thus focus is on simplicity and not speed.

This is a simple Demo of Visual Question answering which uses pretrained models (see models/CNN and models/VQA) to answer a given question about the given image.

## Dependency

1. Keras version 2.0+
   * Modular deep learning library based on python

2. Tensorflow 1.2+
    (Might also work with Theano. I have not tested Theano 
    after the recent commit, use commit 0f89007 for Theano)

3. scikit-learn
   * Quintessential machine library for python

4. Spacy version 2.0+
    * Used to load Glove vectors (word2vec)
    * To upgrade & install Glove Vectors
       * python -m spacy download en_vectors_web_lg

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](https://drive.google.com/file/d/0Bz7KyqmuGsilT0J5dmRCM0ROVHc/view)

## Usage

> python demo.py -image_file_name `path_to_file` -question "Question to be asked"

e.g 

> python demo.py -image_file_name test.jpg -question "Is there a man in the picture?"


if you have prefer to use Theano backend and if you have GPU you may want to run like this

> THEANO_FLAGS='floatX=float32,device=gpu0,lib.cnmem=1,mode=FAST_RUN' python demo.py -image_file_name test.jpg -question "What vechile is in the picture?"


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


## Runtime

  * GPU (Titan X) Theano optimizer=fast_run       : 51.3 seconds
  * GPU (Titan X) Theano optimizer=fast_compile   : 47.5 seconds
  * CPU (i7-5820K CPU @ 3.30GHz                   : 35.9 seconds (Is this strange or not ?)

## iPython Notebook

Jupyter/iPython Notebook has been provided with more examples and interactive tutorial.
<https://github.com/iamaaditya/VQA_Demo/blob/master/Visual_Question_Answering_Demo_in_python_notebook.ipynb>

NOTE:
See the comments on demo.py for more information on the model and methods

# VQA Training

* See the repo https://github.com/iamaaditya/VQA_Keras to learn how to train new models