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README.md

WSSS-BED

For more meaningful and rigorous research for Weakly-Supervised Semantic Segmentation (WSSS).

How to run?

DATASET=voc

SAL_DIR=saliency_maps/VOC2012_MOVE
ATM_DIR=activation_maps/CAM
SAVE_DIR=pseudo_labels/VOC2012_CAM_MOVE
ATM_THRESH=0.1
IGNORE_LABEL=True
WO_SAL=False
WORKERS=8

python3 run.py --sal_dir ${SAL_DIR} --atm_dir ${SAL_DIR --save_dir ${SAVE_DIR} --dataset ${DATASET} --atm_thresh ${ATM_THRESH} --ignore_label ${IGNORE_LABEL} --wo_sal ${WO_SAL} --workers ${WORKERS}

or

we provide the recommended setting for each WSSS method in scripts/.

bash scripts/cam_voc12.sh
bash scripts/eps_voc12.sh

bash scripts/cam_coco14.sh
bash scripts/l2g_coco14.sh

Available WSSS Methods

We provide the download link for the activation map from below WSSS method.

Available Saliency Maps

We provide the download link for the below saliency map.

  • VOC2012

    • DSS_MSRA
    • DSS_MSRA_HKU
    • DSS_DUTS
    • DSS_COCO
    • DSS_COCO20
    • PFAN_MSRA
    • PFAN_MSRA_HKU
    • PFAN_DUTS
    • PFAN_COCO
    • PFAN_COCO20
    • PoolNet_MSRA
    • PoolNet_MSRA_HKU
    • PoolNet_DUTS
    • PoolNet_COCO
    • PoolNet_COCO20
    • VST_MSRA
    • VST_MSRA_HKU
    • VST_DUTS
    • VST_COCO
    • VST_COCO20
    • DeepUSPS
    • MOVE

  • COCO2014

    • DSS_MSRA
    • DSS_MSRA_HKU
    • DSS_DUTS
    • PFAN_MSRA
    • PFAN_MSRA_HKU
    • PFAN_DUTS
    • PoolNet_MSRA
    • PoolNet_MSRA_HKU
    • PoolNet_DUTS
    • VST_MSRA
    • VST_MSRA_HKU
    • VST_DUTS
    • DeepUSPS
    • MOVE

Source Contribution

Please make an issue first with your downloadable link of activation maps or saliency maps which is compatible with our framework.