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ReCAM

The official code of CVPR 2022 paper (Class Re-Activation Maps for Weakly-Supervised Semantic Segmentation). arXiv

Citation

@inproceedings{recam,
  title={Class Re-Activation Maps for Weakly-Supervised Semantic Segmentation},
  author={Chen, Zhaozheng and Wang, Tan and Wu, Xiongwei and Hua, Xian-Sheng and Zhang, Hanwang and Sun, Qianru},
  booktitle={The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2022}
}

Prerequisite

• Python 3.6, PyTorch 1.9, and others in environment.yml
• You can create the environment from environment.yml file

conda env create -f environment.yml

Usage (PASCAL VOC)

Step 1. Prepare dataset.

• Download PASCAL VOC 2012 devkit from official website. Download.
• You need to specify the path ('voc12_root') of your downloaded devkit in the following steps.

Step 2. Train ReCAM and generate seeds.

• Please specify a workspace to save the model and logs.

CUDA_VISIBLE_DEVICES=0 python run_sample.py --voc12_root ./VOCdevkit/VOC2012/ --work_space YOUR_WORK_SPACE --train_cam_pass True --train_recam_pass True --make_recam_pass True --eval_cam_pass True 

Step 3. Train IRN and generate pseudo masks.

CUDA_VISIBLE_DEVICES=0 python run_sample.py --voc12_root ./VOCdevkit/VOC2012/ --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 

Step 4. Train semantic segmentation network.

To train DeepLab-v2, we refer to deeplab-pytorch. We use the ImageNet pre-trained model for DeepLabV2 provided by AdvCAM. Please replace the groundtruth masks with generated pseudo masks.

Usage (MS COCO)

Step 1. Prepare dataset.

• Download MS COCO images from the official COCO website.
• Generate mask from annotations (annToMask.py file in ./mscoco/).
• Download MS COCO image-level labels from here and put them in ./mscoco/

Step 2. Train ReCAM and generate seeds.

• Please specify a workspace to save the model and logs.

CUDA_VISIBLE_DEVICES=0 python run_sample_coco.py --mscoco_root ../MSCOCO/ --work_space YOUR_WORK_SPACE --train_cam_pass True --train_recam_pass True --make_recam_pass True --eval_cam_pass True 

Step 3. Train IRN and generate pseudo masks.

CUDA_VISIBLE_DEVICES=0 python run_sample_coco.py --mscoco_root ../MSCOCO/ --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 

Step 4. Train semantic segmentation network.

• The same as PASCAL VOC.

Acknowledgment

This code is borrowed from IRN and AdvCAM, thanks Jiwoon and Jungbeom.