Size-Invariant Salient Object Detection

This is the official code for the paper "Size-invariance Matters: Rethinking Metrics and Losses for Imbalanced Multi-object Salient Object Detection" accepted by International Conference on Machine Learning (ICML2024). The paper is available here.

A simple version of code, which is specially designed to calculate the Size-Invariant Metrics, can be found here!

Paper Title: Size-invariance Matters: Rethinking Metrics and Losses for Imbalanced Multi-object Salient Object Detection

Authors: Feiran Li, Qianqian Xu*, Shilong Bao, Zhiyong Yang, Runmin Cong, Xiaochun Cao, Qingming Huang*

Installation

Clone this repository:

git clone https://github.com/Ferry-Li/SI-SOD.git

Install the required libraries:

pip install -r requirements.txt

Datasets

In our paper, eight benchmark datasets are included in the experiments. The statics and download links of each dataset are displayed in the table below.

Dataset	Link	Description
DUTS	DUTS	DUTS-TR: 10,553 images DUTS-TE: 5,019 images
ECSSD	ECSSD	1,000 images
DUT-OMRON	DUT-OMRON	5,168 images
HKU-IS	HKU-IS	4,447 images
MSOD	MSOD	300 images, all multiple objects
PASCAL-S	PASCAL-S	850 images
SOD	SOD	300 images
XPIE	XPIE	10,000 images

Preprocessing

Before training and evaluation, there are a few steps of data pre-processing.

• Step1: Dataset preparation

The dataset directory should be organized as following:

dataset
├── DUT-OMRON
│   ├── image
│   │   └── im005.jpg
|   |		...
│   ├── list.txt
│   └── mask
│       └── im005.png
|			...
├── DUTS
│   ├── DUTS-TE
│   │   ├── image
|	|	|	└── ...	
│   │   ├── list.txt
│   │   └── mask
|	|	|	└── ...
│   └── DUTS-TR
|		└── ...
├── ......

• Step 2: Dataset preprocessing

cd src
python datasets/preprocess.py --config configs/datasets.yaml --dataset DUTS-TR
# change the '--dataset DUTS-TR' to other datasets you may use

You can edit the specific path of the dataset in configs/datasets.yaml, and other parameters in datastes/preprocess.py.

Specifically, --epsilon in the datasets/preprocess.py is designed to remove noise (whose scale is smaller than epsilon) in the mask. When the value of --epsilon is large (over 100 for instance), there may be some masks with all pixels equal to 0. In this case, you can follow the error massage and directly delete the corresponding sample in the list.txt.

Training

The training config file is stored at configs/train.yaml, where you can modify the settings of dataset, model, loss, optimizer, and training process. Detailed description of each parameter is commented in the config file. Please carefully read and check the config file before training.

To begin training, you can run the following command:

python run.py --config configs/train.yaml

Specifically, we provide a well-trained checkpoint of (Size-Invariant) EDN and PoolNet here.

Also, the pre-trained backbone used during training can be downloaded here.

Evaluation

The testing config file is stored at configs/test.yaml, where you can modify the settings of dataset, model, evaluation metrics, and testing process. Detailed description of each parameter is commented in the config file. Please carefully read and check the config file before testing.

To begin testing, you can run the following command:

python run.py --config configs/test.yaml --test

Citation

If you find this work or repository useful, please cite the following:

@inproceedings{li2024sizeinvariance,
title={Size-invariance Matters: Rethinking Metrics and Losses for Imbalanced Multi-object Salient Object Detection}, 
author={Feiran Li and Qianqian Xu and Shilong Bao and Zhiyong Yang and Runmin Cong and Xiaochun Cao and Qingming Huang},booktitle={The Forty-first International Conference on Machine Learning},
year={2024}
}

Contact us

If you have any detailed questions or suggestions, feel free to email us: lifeiran@iie.ac.cn! Thanks for your interest in our work!