Awesome
DSNet
Dynamic Selective Network for RGB-D Salient Object Detection
This repo is an official implementation of the DSNet, which has been accepted in the journal IEEE Transactions on Image Processing, 2021.
Prerequisites
- python=3.x
- pytorch=1.0.0+
- torchvision
- numpy
- opencv-python
Usage
1. Clone the repository
git clone https://github.com/Brook-Wen/DSNet.git
cd DSNet/
2. Training
python main.py --gpu '0' --lr 1e-5 --batch_size 4
- Training set: NJU2K (1,485), NLPR (700).
- Make sure that the GPU memory is enough.
3. Testing
python main.py --gpu '0' --batch_size 1 --mode='test' --model='[YOUR PATH]' --test_fold='[SAVE PATH]' --sal_mode='[DATASET]'
- We provide the pre-trained model (fetch code: ve9d).
- We evaluate our DSNet on eight commonly used datasets: NJU2K, NLPR, STERE, DES, LFSD, SSD, SIP and ReDWeb-S. These datasets can be downloaded from the links provided in http://dpfan.net/d3netbenchmark/.
4. Evaluation
- We provide saliency maps (fetch code: nd4m) of our DSNet on 8 datasets.
- You can use this toolbox for evaluation.
Citation
If you think this work is helpful, please cite
@inproceedings{wen2021dsnet,
title={Dynamic Selective Network for RGB-D Salient Object Detection},
author={Wen, Hongfa and Yan, Chenggang and Zhou, Xiaofei and Cong, Runmin and Sun, Yaoqi and Zheng, Bolun and Zhang, Jiyong and Bao, Yongjun and Ding, Guiguang},
booktitle={IEEE Transactions on Image Processing},
year={2021}
}
- If you have any questions, feel free to contact me via:
hf_wen(at)outlook.com
.