https://github.com/sangrockEG/S2C

https://github.com/sangrockEG/S2C

Last synced: 3 months ago
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• Host: GitHub
• URL: https://github.com/sangrockEG/S2C
• Owner: sangrockEG
• Created: 2024-03-18T03:05:01.000Z (10 months ago)
• Default Branch: main
• Last Pushed: 2024-07-10T07:45:24.000Z (7 months ago)
• Last Synced: 2024-07-17T00:32:27.785Z (6 months ago)
• Language: Python
• Size: 771 KB
• Stars: 17
• Watchers: 3
• Forks: 2
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

• awesome-segmentation-and-graph-method -
• Awesome-Segment-Anything - [code

README

        
# S2C

Official repository for CVPR 2024 Oral paper: "**From SAM to CAMs: Exploring Segment Anything Model for Weakly Supervised Semantic Segmentation**" by [Hyeokjun Kweon](https://scholar.google.com/citations?user=em3aymgAAAAJ&hl=en&oi=ao).

## Prerequisite

* Tested on Ubuntu 18.04, with Python 3.8, PyTorch 1.8.2, CUDA 11.4, 4 GPUs.

* [The PASCAL VOC 2012 development kit](http://host.robots.ox.ac.uk/pascal/VOC/voc2012/):

You need to specify place VOC2012 under ./data folder.

* ImageNet-pretrained weights for resnet38d are from [[resnet_38d.params]](https://drive.google.com/file/d/1fpb4vah3e-Ynx4cv5upUcqnpJFY_FTja/view?usp=sharing). You need to place the weights as ./pretrained/resnet_38d.params.

# Prerequisite on SAM 

* Please install [SAM](https://github.com/facebookresearch/segment-anything) and download vit_h version as ./pretrained/sam_vit_h.pth

* Note that I slightly modified the original code of SAM for fast batch-wise inference during the training of CAMs.

* After installing SAM properly, you should substitute the files 'mask_decoder.py' and 'sam.py' in the segment_anything/modeling directory with the files in 'modeling' of this repository.

* Additionally, you need to run the Segment-Everything option using SAM as preprocessing. Please refer to get_se_map.py for further details.

## Usage

* This repository generates CAMs (seeds) to train the segmentation network.

* For further refinement, refer [RIB](https://github.com/jbeomlee93/RIB) and [SAM_WSSS](https://github.com/cskyl/SAM_WSSS).

### Training

* Please specify the name of your experiment.

* Training results are saved at ./experiment/[exp_name]

```

python train.py --name [exp_name] --model s2c

```

### Evaluation for CAM

```

python evaluation.py --name [exp_name] --task cam --dict_dir dict

```

## Citation

If our code be useful for you, please consider citing our CVPR 2024 paper using the following BibTeX entry.

```

@inproceedings{kweon2024sam,

  title={From SAM to CAMs: Exploring Segment Anything Model for Weakly Supervised Semantic Segmentation},

  author={Kweon, Hyeokjun and Yoon, Kuk-Jin},

  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},

  pages={19499--19509},

  year={2024}

}

```