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BiconNet: An Edge-preserved Connectivity-based Approach for Salient Object Detection

Requirement: Pytorch 1.7.1

This code including three parts:

  1. Codes for customizing BiconNet wtih other backbones (/general)
  2. Codes for reproducing the paper results (/paper_result)
  3. Evaluation Code (/evaluation)
  1. Customize the BiconNet based on your own network. (/general) If you want to construct the BiconNet based on your own network, there are four simple steps:
  1. replace your network's one-channel output fully connected layers with 8-channel FC layers.

For training: 2) generate the ground truth connectivity masks using the function 'sal2conn' in utils_bicon.py 3) replace your own loss function with Bicon_loss: you can edit the connect_loss.py

For testing: 4) use the function 'bv_test' in utils_bicon.py after you get the 8-channel connectivity map output to get your final saliency prediction. 2. Customize the BiconNet based on your own network. (/general) If you want to construct the BiconNet based on your own network, there are four simple steps:

  1. replace your network's one-channel output fully connected layers with 8-channel FC layers.

For training: 2) generate the ground truth connectivity masks using the function 'sal2conn' in utils_bicon.py 3) replace your own loss function with Bicon_loss: you can edit the connect_loss.py

For testing: 4) use the function 'bv_test' in utils_bicon.py after you get the 8-channel connectivity map output to get your final saliency prediction.

  1. Reproduce the results in the paper (/paper_result)

(a) PoolNet Baseline code from: https://github.com/backseason/PoolNet

For traing: cd /PoolNet/bicon/train python train.py

For testing: cd /PoolNet/bicon/test python test.py

make sure the datapath is correct.

(b) CPD-R Baseline code from: https://github.com/wuzhe71/CPD

For training cd /CPD-R/bicon/train python train.py

For testing: cd /CPD-R/bicon/test python test.py

make sure the datapath is correct.

(c) EGNet Baseline code from: https://github.com/JXingZhao/EGNet

For training cd /EGNet/bicon/train python run.py

For testing: cd /EGNet/bicon/test python test.py

make sure the datapath is correct.

(d) GCPANet Baseline code from: https://github.com/JosephChenHub/GCPANet

For training cd /GCPANet/bicon/train python train.py

For testing: cd /GCPANet/bicon/test python test.py

make sure the datapath is correct.

(d) ITSD Baseline code from: https://github.com/moothes/ITSD-pytorch

For training cd /ITSD/bicon/train python train.py

For testing: cd /ITSD/bicon/test python test.py

make sure the datapath is correct.

(d) MINet Baseline code from: https://github.com/lartpang/MINet

For training cd /MINet/bicon/train python main.py

For testing: cd /MINet/bicon/test python main.py

make sure the datapath is correct.

  1. Results evaluation (/evaluation) We use Matlab to evaluate the output saliency maps as did in: https://github.com/JosephChenHub/GCPANet