/BGNet

Boundary-Guided Camouflaged Object Detection, IJCAI2022

Primary LanguagePython

BGNet: Boundary-Guided Camouflaged Object Detection (IJCAI 2022)

Authors: Yujia Sun, Shuo Wang, Chenglizhao Chen, and Tian-Zhu Xiang.

1. Preface

  • This repository provides code for "Boundary-Guided Camouflaged Object Detection" IJCAI-2022. Arxiv Page

2. Proposed Baseline

2.1. Training/Testing

The training and testing experiments are conducted using PyTorch with a single NVIDIA Tesla P40 GPU of 24 GB Memory.

  1. Configuring your environment (Prerequisites):

    • Creating a virtual environment in terminal: conda create -n BGNet python=3.6.

    • Installing necessary packages: pip install -r requirements.txt.

  2. Downloading necessary data:

  3. Training Configuration:

    • Assigning your costumed path, like --train_save and --train_path in etrain.py.

  4. Testing Configuration:

    • After you download all the pre-trained model and testing dataset, just run etest.py to generate the final prediction map: replace your trained model directory (--pth_path).

2.2 Evaluating your trained model:

One-key evaluation is written in MATLAB code (revised from link), please follow this the instructions in ./eval/main.m and just run it to generate the evaluation results in.

If you want to speed up the evaluation on GPU, you just need to use the efficient tool by pip install pysodmetrics.

Assigning your costumed path, like method, mask_root and pred_root in eval.py.

Just run eval.py to evaluate the trained model.

pre-computed maps of BGNet can be found in download link (Google Drive).

pre-computed maps of other comparison methods can be found in download link (Baidu Pan) with Code: yxy9.

3. Citation

Please cite our paper if you find the work useful:

@inproceedings{sun2022bgnet,
title={Boundary-Guided Camouflaged Object Detection},
author={Sun, Yujia and Wang, Shuo and Chen, Chenglizhao and Xiang, Tian-Zhu},
booktitle={IJCAI},
pages = "1335--1341",
year={2022}
}