Saliency Guided Inter and Intra-Class Relation Constraints for Weakly Supervised Semantic Segmentation

Introduction

This is the source code for our paper Saliency Guided Inter and Intra-Class Relation Constraints for Weakly Supervised Semantic Segmentation

Network Architecture

The architecture of our proposed approach is as follows

Installation

• Install PyTorch 1.7.0 with Python 3 and CUDA 11.0

• Clone this repo

git clone https://github.com/chentao2016/I2CRC.git

Download PASCAL VOC 2012

• Download PASCAL VOC 2012

Testing

• Download our trained model checkpoint_69.3.pth and put it in the segmentation/data/models folder

cd segmentation

python main.py test --config-path configs/voc12.yaml --model-path data/models/checkpoint_69.3.pth

python main.py crf --config-path configs/voc12.yaml

Training

cd classification

• Download the pre-computed saliency map. (You need to go to utils/LoadData.py and set the path Line 100.) Then train the classification model and obtain CAM.

./train.sh 
./test.sh 

• Generate the pseudo labels

python gen_gt.py

• Then train the segmentation model with pseudo labels . (You need to set the path for pseudo labels.)

cd segmentation
python main.py train --config-path configs/voc12.yaml

• Get the initial prediction for trainaug images. (You need to go to configs/voc12.yaml and set DATASET.SPLIT.VAL = train_aug. Set it back to val when testing)

python trainaug_pred.py test --config-path configs/voc12.yaml \
    --model-path data/models/voc12/deeplabv2_resnet101_msc/train_aug/checkpoint_final.pth

python trainaug_pred.py crf --config-path configs/voc12.yaml -j 10

• Generate the refined pseudo labels

cd classification
python label_refinement.py

• Train the segmentation model with the refined pseudo labels. (You need to set the path for refined pseudo labels.)

cd segmentation
python main.py train --config-path configs/voc12.yaml