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easy-VQA-keras

A Keras implementation of a simple Visual Question Answering (VQA) architecture, using the easy-VQA dataset.

Methodology described in the official blog post. See easy-VQA featured on the official VQA site!

Usage

Setup and Basic Usage

First, clone the repo and install the dependencies:

git clone https://github.com/vzhou842/easy-VQA-keras.git
cd easy-VQA-keras
pip install -r requirements.txt

To run the model,

python train.py

A typical run with should have results that look like this:

Epoch 1/8
loss: 0.8887 - accuracy: 0.6480 - val_loss: 0.7504 - val_accuracy: 0.6838
Epoch 2/8
loss: 0.7443 - accuracy: 0.6864 - val_loss: 0.7118 - val_accuracy: 0.7095
Epoch 3/8
loss: 0.6419 - accuracy: 0.7468 - val_loss: 0.5659 - val_accuracy: 0.7780
Epoch 4/8
loss: 0.5140 - accuracy: 0.7981 - val_loss: 0.4720 - val_accuracy: 0.8138
Epoch 5/8
loss: 0.4155 - accuracy: 0.8320 - val_loss: 0.3938 - val_accuracy: 0.8392
Epoch 6/8
loss: 0.3078 - accuracy: 0.8775 - val_loss: 0.3139 - val_accuracy: 0.8762
Epoch 7/8
loss: 0.1982 - accuracy: 0.9286 - val_loss: 0.2202 - val_accuracy: 0.9212
Epoch 8/8
loss: 0.1157 - accuracy: 0.9627 - val_loss: 0.1883 - val_accuracy: 0.9378

Read the "Training" section for how you might improve the accuracy of the model--we were able to get it ot 99.5% validation accuracy!.

Training

The training script train.py has two optional arguments:

python train.py [--big-model] [--use-data-dir]

Optional arguments:
  --big-model     Use the bigger model with more conv layers
  --use-data-dir  Use custom data directory, at /data

The --big-model flag trains a slightly larger model, that we used to train a 99.5% accuracy model used in the following live demo.

Furthermore, instead of using the official easy-vqa package, you generate your own dataset using the easy-VQA repo and use that instead. After following the instructions in that repo, just copy the /data folder into the root directory of this repository, so that your files look like this:

easy-VQA-keras/
├── data/
  ├── answers.txt
  ├── test/
  ├── train/
├── analyze.py
├── model.py
├── prepare_data.py
└── train.py

For the 99.5% accuracy model, we used a custom dataset generated with double the images/questions as the official dataset (set NUM_TRAIN and NUM_TEST to 8000 and 2000, respectively, for the easy-VQA repo).

Other Files

In addition to the training script, we have three other files:

  • analyze.py, a script we used to debug our models. Run using a model weights file, and produce statistics about model outputs and confusion matrices to analyze model errors.
  • model.py, where the model architecture is specified
  • prepare_data.py, which reads and processes the data, either using the easy-vqa package or a custom data directory