robust-segment-anything 1.0.0

RobustSAM: Segment Anything Robustly on Degraded Images.

RobustSAM: Segment Anything Robustly on Degraded Images (CVPR 2024 Highlight)

Official repository for RobustSAM: Segment Anything Robustly on Degraded Images

Project Page | Paper | Video | Dataset

Updates

• June 2024: ✨ Inference code of RobustSAM was released!
• Feb 2024: ✨ RobustSAM was accepted into CVPR 2024!

Introduction

Segment Anything Model (SAM) has emerged as a transformative approach in image segmentation, acclaimed for its robust zero-shot segmentation capabilities and flexible prompting system. Nonetheless, its performance is challenged by images with degraded quality. Addressing this limitation, we propose the Robust Segment Anything Model (RobustSAM), which enhances SAM's performance on low-quality images while preserving its promptability and zero-shot generalization.

Our method leverages the pre-trained SAM model with only marginal parameter increments and computational requirements. The additional parameters of RobustSAM can be optimized within 30 hours on eight GPUs, demonstrating its feasibility and practicality for typical research laboratories. We also introduce the Robust-Seg dataset, a collection of 688K image-mask pairs with different degradations designed to train and evaluate our model optimally. Extensive experiments across various segmentation tasks and datasets confirm RobustSAM's superior performance, especially under zero-shot conditions, underscoring its potential for extensive real-world application. Additionally, our method has been shown to effectively improve the performance of SAM-based downstream tasks such as single image dehazing and deblurring.

Setup

1. Create a conda environment and activate it.

conda create --name robustsam python=3.10 -y
conda activate robustsam

2. Clone and enter into repo directory.

git clone https://github.com/robustsam/RobustSAM
cd RobustSAM

3. Use command below to check your CUDA version.

nvidia-smi

4. Replace the CUDA version with yours in command below.

pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu[$YOUR_CUDA_VERSION]
# For example: pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu117 # cu117 = CUDA_version_11.7

5. Install remaining dependencies

pip install -r requirements.txt

6. Download pretrained RobustSAM checkpoint and place into current directory.

Demo

We have prepared some images im demo_images folder for demo purpose. Besides, two prompting modes are available (box prompts and point prompts).

• For box prompt:

python eval.py --bbox

• For point prompt:

python eval.py

In default, demo results will be saved to demo_result/[$PROMPT_TYPE].

Comparison of computational requirements

Visual Comparison

Quantitative Comparison

Seen dataset with synthetic degradation

Unseen dataset with synthetic degradation

Unseen dataset with real degradation

Reference

If you find this work useful, please consider citing us!

@inproceedings{chen2024robustsam,
  title={RobustSAM: Segment Anything Robustly on Degraded Images},
  author={Chen, Wei-Ting and Vong, Yu-Jiet and Kuo, Sy-Yen and Ma, Sizhou and Wang, Jian},
  journal={CVPR},
  year={2024}
}

Acknowledgements

We thank the authors of SAM from which our repo is based off of.