Document Visual Question Answering (DocVQA)

This repo hosts the basic functional code for our approach entitled HyperDQA in the Document Visual Question Answering competition hosted as a part of Workshop on Text and Documents in Deep Learning Era at CVPR2020. Our approach stands at position 4 on the Leaderboard.

Read more about our approach in this blogpost!

Installation

Virtual Environment Python 3 (Recommended)

1. Clone the repository

git clone https://github.com/anisha2102/docvqa.git

2. Install libraries

pip install -r requirements.txt

Downloads

1. Download the dataset The dataset for Task 1 can be downloaded from the Competition Website from the Downloads Section. The dataset consists of document images and their corresponding OCR transcriptions.

2. Download the pretrained model Download the pretrained model for LayoutLM-Base, Uncased from here

Prepare dataset

python create_dataset.py \
         <data-ocr-folder> \
         <data-documents-folder> \
         <path-to-train_v1.0.json> \
         <train-output-json-path> \
         <validation-output-json-path>

Train the model

CUDA_VISIBLE_DEVICES=0 python run_docvqa.py \
    --data_dir <data-folder> \
    --model_type layoutlm \
    --model_name_or_path <pretrained-model-path> \ #example ./models/layoutlm-base-uncased
    --do_lower_case \
    --max_seq_length 512 \
    --do_train \
    --num_train_epochs 15 \
    --logging_steps 500 \
    --evaluate_during_training \
    --save_steps 500 \
    --do_eval \
    --output_dir  <data-folder>/<exp-folder> \
    --per_gpu_train_batch_size 8 \
    --overwrite_output_dir \
    --cache_dir <data-folder>/models \
    --skip_match_answers \
    --val_json <train-output-json-path> \
    --train_json <train-output-json-path> \

Model Checkpoints

Download the pytorch_model.bin file from the link below and copy it to the models folder. Google Drive Link

Demo

Try out the demo on a sample datapoint with demo.ipynb

Acknowledgements

The code and pretrained models are based on LayoutLM and HuggingFace Transformers. Many thanks for their amazing open source contributions.

docvqa