Pytorch implementation of winner from VQA Chllange Workshop in CVPR'17

Overview

2017 VQA Challenge Winner (CVPR'17 Workshop)

pytorch implementation of Tips and Tricks for Visual Question Answering: Learnings from the 2017 Challenge by Teney et al.

Model architecture

Prerequisites

Data

Preparation

  • To download and extract vqav2, glove, and pretrained visual features:
    bash scripts/download_extract.sh
  • To prepare data for training:
    python scripts/preproc.py
  • The structure of data/ directory should look like this:
    - data/
      - zips/
        - v2_XXX...zip
        - ...
        - glove...zip
        - trainval_36.zip
      - glove/
        - glove...txt
        - ...
      - v2_XXX.json
      - ...
      - trainval_resnet...tsv
      (The above are files created after executing scripts/download_extract.sh)
      - tokenizers/
        - ...
      - dict_ans.pkl
      - dict_q.pkl
      - glove_pretrained_300.npy
      - train_qa.pkl
      - val_qa.pkl
      - train_vfeats.pkl
      - val_vfeats.pkl
      (The above are files created after executing scripts/preproc.py)
    

Train

Use default parameters:

bash scripts/train.sh

Notes

  • Huge re-factor (especially data preprocessing), tested based on pytorch 0.4.1 and python 3.6
  • Training for 20 epochs reach around 50% training accuracy. (model seems buggy in my implementation)
  • After all the preprocessing, data/ directory may be up to 38G+
  • Some of preproc.py and utils.py are based on this repo

Resources

Owner
Mark Dong
CyLab PhD student
Mark Dong
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