MoADNet: Mobile Asymmetric Dual-Stream Networks for Real-Time and Lightweight RGB-D Salient Object Detection
This is the official implementation of "MoADNet: Mobile Asymmetric Dual-Stream Networks for Real-Time and Lightweight RGB-D Salient Object Detection" as well as the follow-ups. The paper has been published by IEEE Transactions on Circuits and Systems for Video Technology, 2022. The paper link is here.
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Train
runpython train.py
# set '--train-root' to your training dataset folder -
Test
runpython test.py
# set '--test-root' to your test dataset folder
# set '--ckpt' as the correct checkpoint
- The saliency maps can be approached in Baidu Cloud (fetach code is moad). Note that the results provided in paper are the average values after several training times.
- The evaluation tools, training and test datasets can be obtained in RGBD-SOD-tools.
@ARTICLE{jin2022moadnet,
author={Jin, Xiao and Yi, Kang and Xu, Jing},
journal={IEEE Transactions on Circuits and Systems for Video Technology},
title={MoADNet: Mobile Asymmetric Dual-Stream Networks for Real-Time and Lightweight RGB-D Salient Object Detection},
year={2022},
volume={32},
number={11},
pages={7632-7645}
}