Wave-like Class Activation Map with Representation Fusion for Weakly-Supervised Semantic Segmentation
Pytorch implementation of "Wave-like Class Activation Map with Representation Fusion for Weakly-Supervised Semantic Segmentation" (Under Review).
Install dependencies:
conda env create -f environment.yml
Python 3.6, PyTorch 1.9, and others in environment.yml
Download PASCAL VOC 2012 dataset:
wget http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar
tar –xvf VOCtrainval_11-May-2012.tar
Download MS COCO 2014 dataset:
wget http://images.cocodataset.org/zips/train2014.zip
wget http://images.cocodataset.org/zips/val2014.zip
1. Train WaveCAM and generate Initialize pseudo labels.
python run_wavecam_voc.py --voc12_root your_path --work_space your_work_space --train_cam_pass True --train_wavecam_pass True --make_wavecam_pass True --eval_cam_pass True
2. Train IRN and generate pseudo labels.
python run_wavecam_voc.py --voc12_root your_path --work_space your_work_space --cam_to_ir_label_pass True --train_irn_pass True --make_sem_seg_pass True --eval_sem_seg_pass True
3. Train the fully supervised semantic segmentation network.
Please download ImageNet pre-trained model .
We refer to deeplab-pytorch to train DeepLab-v2. Please replace the groundtruth mask with the generated pseudo mask when training.
1. Train WaveCAM and generate Initialize pseudo labels.
python run_wavecam_coco.py --mscoco_root your_path --work_space your_work_space --train_cam_pass True --train_wavecam_pass True --make_wavecam_pass True --eval_cam_pass True
2. Train IRN and generate pseudo labels.
python run_wavecam_coco.py --mscoco_root your_path --work_space your_work_space --cam_to_ir_label_pass True --train_irn_pass True --make_sem_seg_pass True --eval_sem_seg_pass True
3. Train the fully supervised semantic segmentation network.
The same as PASCAL VOC Dataset.
This code is only for reviewers to understand implementation details.
This code is available only for non-commercial use.
Some implementation code comes from：
ReCAM：https://github.com/zhaozhengChen/ReCAM