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PyTorch implementation of the paper 'BBS-Net: RGB-D Salient Object Detection with a Bifurcated Backbone Strategy Network, ECCV2020'

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BBS-Net

BBS-Net: RGB-D Salient Object Detection with a Bifurcated Backbone Strategy Network


Figure 1: Pipeline of the BBS-Net.

1. Requirements

Python 3.7, Pytorch 0.4.0+, Cuda 10.0, TensorboardX 2.0, opencv-python

2. Data Preparation

  • Download the raw data from Baidu Pan [code: yiy1] or Google Drive and trained model (BBSNet.pth) from Here [code: dwcp]. Then put them under the following directory:

     -BBS_dataset\ 
       -RGBD_for_train\  
       -RGBD_for_test\
       -test_in_train\
     -BBSNet
       -models\
       -model_pths\
          -BBSNet.pth
       ...
    
  • Note that the depth maps of the raw data above are not normalized. If you train and test using the normalized depth maps, the performance will be improved.

3. Training & Testing

  • Train the BBSNet:

    python BBSNet_train.py --batchsize 10 --gpu_id 0

  • Test the BBSNet:

    python BBSNet_test.py --gpu_id 0

    The test maps will be saved to './test_maps/'.

  • Evaluate the result maps:

    You can evaluate the result maps using the tool in Python_GPU Version or Matlab Version.

  • If you need the codes using VGG16 and VGG19 backbones, please send to the email (zhaiyingjier@163.com). Please provide your Name & Institution. Please note the code can be only used for research purpose.

4. Results

4.1 Qualitative Comparison


Figure 2: Qualitative visual comparison of the proposed model versus 8 SOTA models.


Table 1: Quantitative comparison of models using S-measure max F-measure, max E-measureand MAE scores on 7 datasets.

4.2 Results of multiple backbones


Table 2: Performance comparison using different backbones.

4.3 Download

  • Test maps of the above datasets (ResNet50 backbone) can be download from here [code: qgai ].
  • Test maps of vgg16 and vgg19 backbones of our model can be download from here [code: zuds ].
  • Test maps of DUT-RGBD dataset (using the proposed training-test splits of DMRA) can be downloaded from here [code: 3nme ].

5. Citation

Please cite the following paper if you use this repository in your reseach.

@inproceedings{fan2020bbsnet,
title={BBS-Net: RGB-D Salient Object Detection with a Bifurcated Backbone Strategy Network},
author={Fan, Deng-Ping and Zhai, Yingjie and Borji, Ali and Yang, Jufeng and Shao, Ling},
booktitle={ECCV},
year={2020}
}
  • For more information about BBS-Net, please read the Manuscript (PDF) (Chinese version[code:0r4a]).
  • Note that there is a wrong in the Fig.3 (c) of the ECCV version. The second and third BConv3 in the first column of the figure should be BConv5 and BConv7 respectively.

6. Benchmark RGB-D SOD

The complete RGB-D SOD benchmark can be found in this page:

http://dpfan.net/d3netbenchmark/

7. Acknowledgement

We implement this project based on the code of ‘Cascaded Partial Decoder for Fast and Accurate Salient Object Detection, CVPR2019’ proposed by Wu et al.

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PyTorch implementation of the paper 'BBS-Net: RGB-D Salient Object Detection with a Bifurcated Backbone Strategy Network, ECCV2020'

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