[IEEE TCyb] KepSalinst: Using Peripheral Points to Delineate Salient Instances

This is an official implementation of the paper "KepSalinst: Using Peripheral Points to Delineate Salient Instances", accepted by IEEE Transactions on Cybernetics. [paper]

Installation

This repository has been tested with the following environment:

• CUDA (11.3)
• Python (3.9.12)
• Pytorch (1.10.1)
• Detectron2 (0.6+cu113)
• Pandas (1.5.2)
• Scipy (1.10.0rc1)

Example conda environment setup

conda create -n kepsalinst python=3.9.12
conda activate kepsalinst
conda install pytorch==1.10.1 torchvision==0.11.2 torchaudio==0.10.1 cudatoolkit=11.3 -c pytorch -c conda-forge
python -m pip install detectron2 -f https://dl.fbaipublicfiles.com/detectron2/wheels/cu113/torch1.10/index.html

# under your working directory
git clone https://github.com/MonkeyKing0528/KepSalinst.git
cd KepSalinst
python setup.py build develop
pip install -r requirements.txt

Getting Started

Datasets

To meet Detectron2's specifications, our project employs COCO-style annotated datasets. There are two ways to acquire these: Firstly, download the raw datasets from official sources and manually create COCO-style annotations. Alternatively, for ease, we offer pre-annotated datasets in COCO format, available for download from Google Drive or Baidu Netdisk (code: wub4). Notably, the ILSO-1K dataset annotations are sourced from RDPNet, while we generated annotations for the remaining datasets ourselves.

After acquiring the datasets, place them in the ./SIS_datasets/ directory. Alternatively, you can create a symbolic link from the directory containing the datasets to "./SIS_datasets/".

Training

To train our model, run:

python train.py --config-file configs/KepSalinst/{dataset}.yaml

Replace {dataset} with the dataset name, choosing from ILSO1K, ILSO2K, SOC, COME15K, or SIS10K.

Evaluation

To evaluate after training, run:

python test.py --config-file configs/KepSalinst/{dataset}.yaml

Or, download our trained models from Google Drive or Baidu Netdisk (code: swbr). Then, use the following command for evaluation:

python test.py --config-file configs/KepSalinst/{dataset}.yaml MODEL.WEIGHTS {path to model}

Scripts

To facilitate ease of use, you can directly run the .sh files located in the ./scripts/ directory. These files offer complete commands for training and evaluating on different datasets.

Citation

@article{chen2023kepsalinst,
  title={{KepSalinst}: Using Peripheral Points to Delineate Salient Instances},
  author={Chen, Jinpeng and Cong, Runmin and Ip, Horace Ho Shing and Kwong, Sam},
  journal={IEEE Transactions on Cybernetics},
  year={2023},
  publisher={IEEE}
}

Acknowledgements

• This code is based on the CondInst ([2020-ECCV] Conditional convolutions for instance segmentation) implementation from AdelaiDet.
• The ILSO-1K dataset annotations in our work are sourced from RDPNet