The official repo of the paper Democracy Does Matter: Comprehensive Feature Mining for Co-Salient Object Detection
.
create enviroment and intall as following:
pip install -r requirements.txt
trainset: CoCo-SEG
testset: CoCA, CoSOD3k, Cosal2015
Put the CoCo-SEG, CoCA, CoSOD3k and Cosal2015 datasets to DCFM/data
as the following structure:
DCFM
├── other codes
├── ...
│
└── data
├── CoCo-SEG (CoCo-SEG's image files)
├── CoCA (CoCA's image files)
├── CoSOD3k (CoSOD3k's image files)
└── Cosal2015 (Cosal2015's image files)
trained model can be downloaded from papermodel.
Run test.py
for inference.
The evaluation tool please follow: https://github.com/zzhanghub/eval-co-sod
Download pretrainde backbone model VGG.
Run train.py
for training.
The co-saliency maps of DCFM can be found at preds.
reproductions by myself on 2080Ti can be found at reproduction1 and reproduction2.
reprodution by myself on TITAN X can be found at reproduction3.
The code is based on GCoNet. I've added a validation part to help select the model for closer results. This validation part is based on GCoNet_plus. You can try different evaluation metrics to select the model.