AustNet-Inharmonious-Region-Localization

This is the official code of the paper:

Inharmonious Region Localization with Auxiliary Style Feature
Penghao Wu, Li Niu, Liqing Zhang
arXiv Paper, BMVC 2022

Install

Clone this repo and build the environment

git clone https://github.com/bcmi/AustNet-Inharmonious-Region-Localization.git
cd AustNet-Inharmonious-Region-Localization
conda env create -f environment.yml --name Austnet
conda activate Austnet

Download the semantic segmentation network model weight through link Google Drive or Baidu Yun with code pfpy. Put the model weight in the HRNet-Semantic-Segmentation-HRNet-OCR folder.

Datset

Please refer to DIRL to download the iHarmoney4 dataset.

Training

To train AustNet, run

python train_austnet.py --dataset_root PATH_OF_THE_DATASET --logdir austnet_training_log --gpus NUMBER_OF_GPUS

To train AustNet_S, run

python train_austnet_s.py --dataset_root PATH_OF_THE_DATASET --logdir austnet_s_training_log --gpus NUMBER_OF_GPUS

Pretrained Model

Model	Google Drive Link	Baidu Yun Link
Austnet	Google Drive	Baidu Yun code: m8ku
Austnet_s	Google Drive	Baidu Yun code: jrdi

Evaluation

To evaluate AustNet, run

python test_austnet.py --dataset_root PATH_OF_THE_DATASET --ckpt MODEL_WEIGHT_PATH

To evaluate AustNet_S, run

python test_austnet_s.py --dataset_root PATH_OF_THE_DATASET --ckpt MODEL_WEIGHT_PATH

Citation

If you find our work or code helpful, please cite:

@inproceedings{Wu2022Inharmonious,
  title={Inharmonious Region Localization with Auxiliary Style Feature},
  author={Penghao Wu and Li Niu and Liqing Zhang},
  booktitle={BMVC},
  year={2022}
}

Acknowledgement

Our code is based on repositories:

• DIRL
• HRNet-OCR