Here is the utility of the various files:

0. demo_batch.py: You need access to pretrained models (included in the repo to run this example)

1. get_started.sh: Downloads data, VQAtools, pre-computed features, and trains a model. Run this script when you are done with the dependencies.

2. dumpText.py: Dumps the questions and answers from the VQA json files to some text files for later ease of use. Run python dumpText.py -h for more info.

3. trainMLP.py: Trains Multi-Layer perceptrons. Run python trainMLP.py -h for more info.

4. trainLSTM_1.py: Trains LSTM-based model. Run python trainLSTM_1.py -h for more info.

5. trainLSTM_language.py: Trains LSTM-based language-only model. Run python trainLSTM_language.py -h for more info.

6. evaluateMLP.py: Evaluates models trained by trainMLP.py. Needs model json file, hdf5 weights file, and output txt file destinations to run.

7. evaluateLSTM.py: Evaluates models trained by trainLSTM_1.py and trainLSTM_language.py. Needs model json file, hdf5 weights file, and output txt file destinations to run.

8. features.py: Contains functions that are used to convert images and words to vectors (or sequences of vectors).

9. utils.py: Exactly what you think.

10. own_image.py: Use your own image. Caffe installation required

11. extract_features.py: Extract 4096D VGG features from a VGG Caffe Model

12. vgg_features.prototxt: VGG Caffe Model Definition