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.

qianrusun1015 / recam Goto Github PK

recam's Introduction

ReCAM

Citation

Prerequisite

Usage (PASCAL VOC)

Step 1. Prepare dataset.

Step 2. Train ReCAM and generate seeds.

Step 3. Train IRN and generate pseudo masks.

Step 4. Train semantic segmentation network.

Usage (MS COCO)

Step 1. Prepare dataset.

Step 2. Train ReCAM and generate seeds.

Step 3. Train IRN and generate pseudo masks.

Step 4. Train semantic segmentation network.

Acknowledgment

recam's People

Contributors

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