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📓RoDLA

Benchmarking the Robustness of Document Layout Analysis Models (CVPR'24)

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🏡 Project Homepage

This is the official repository for our CVPR 2024 paper RoDLA:Benchmarking the Robustness of Document Layout Analysis Models. For more result and benchmarking details, please visit our project homepage.

🔎 Introduction

We introduce RoDLA that aims to benchmark the robustness of Document Layout Analysis (DLA) models. RoDLA is a large-scale benchmark that contains 450,000+ documents with diverse layouts and contents. We also provide a set of evaluation metrics to facilitate the comparison of different DLA models. We hope that RoDLA can serve as a standard benchmark for the robustness evaluation of DLA models.

📝 Catalog

  • Perturbation Benchmark Dataset
    • PubLayNet-P
    • DocLayNet-P
    • M6Doc-P
  • Perturbation Generation and Evaluation Code
  • RoDLA Model Checkpoints
  • RoDLA Model Training Code
  • RoDLA Model Evaluation Code

📦 Installation

1. Clone the repository

git clone https://github.com/yufanchen96/RoDLA.git
cd RoDLA

2. Create a conda virtual environment

# create virtual environment
conda create -n RoDLA python=3.7 -y
conda activate RoDLA

3. Install benchmark dependencies

  • Install Basic Dependencies
pip install torch==1.10.2+cu113 torchvision==0.11.3+cu113 torchaudio==0.10.2+cu113 -f https://download.pytorch.org/whl/cu113/torch_stable.html
python -m pip install detectron2 -f https://dl.fbaipublicfiles.com/detectron2/wheels/cu113/torch1.10/index.html
pip install -U openmim
mim install mmcv-full==1.5.0
pip install timm==0.6.11 mmdet==2.28.1
pip install Pillow==9.5.0
pip install opencv-python termcolor yacs pyyaml scipy
  • Install ocrodeg Dependencies
git clone https://github.com/NVlabs/ocrodeg.git
cd ./ocrodeg
pip install -e .
  • Compile CUDA operators
cd ./model/ops_dcnv3
sh ./make.sh
python test.py

  • You can also install the operator using .whl files

    DCNv3-1.0-whl

📂 Dataset Preparation

RoDLA Benchmark Dataset Preparation

Download the RoDLA dataset from Google Driver to the desired root directory.

Self-generated Perturbation Dataset Preparation

Prepare the dataset as follows by yourself:

cd ./perturbation

python apply_perturbation.py \
      --dataset_dir ./publaynet/val \
      --json_dir ./publaynet/val.json \
      --dataset_name PubLayNet-P \
      --output_dir ./PubLayNet-P \
      --pert_method all \
      --background_folder ./background \
      --metric all

Dataset Structure

After dataset preparation, the perturbed dataset structure would be:

.desired_root
└── PubLayNet-P
    ├── Background
    │   ├── Background_1
    │   │   ├── psnr.json
    │   │   ├── ms_ssim.json
    │   │   ├── cw_ssim.json
    │   │   ├── val.json  
    │   │   ├── val
    │   │   │   ├── PMC538274_00004.jpg
    ...
    │   ├── Background_2
    ...
    ├── Rotation
    ...

🚀 Quick Start

Download the RoDLA model checkpoints

Evaluate the RoDLA model

cd ./model
python -u test.py configs/publaynet/rodla_internimage_xl_publaynet.py \
  checkpoint_dir/rodla_internimage_xl_publaynet.pth \
  --work-dir result/rodla_internimage_publaynet/Speckle_1 \
  --eval bbox \
  --cfg-options data.test.ann_file='PubLayNet-P/Speckle/Speckle_1/val.json' \
                data.test.img_prefix='PubLayNet-P/Speckle/Speckle_1/val/'

Training the RoDLA model

  • Modify the configuration file under configs/_base_/datasets to specify the dataset path
  • Run the following command to train the model with 4 GPUs
sh dist_train.sh configs/publaynet/rodla_internimage_xl_2x_publaynet.py 4

🌳 Citation

If you find this code useful for your research, please consider citing:

@inproceedings{chen2024rodla,
      title={RoDLA: Benchmarking the Robustness of Document Layout Analysis Models}, 
      author={Yufan Chen and Jiaming Zhang and Kunyu Peng and Junwei Zheng and Ruiping Liu and Philip Torr and Rainer Stiefelhagen},
      booktitle={CVPR},
      year={2024}
}