saliencydetection/bbs-net

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

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 Here [code: yiy1] 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 (can be downloaded from here), 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 here.

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]).

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.