SAM-Adapter-PyTorch

Introduction to the SAM-Adapter: Enhancing Segment Everything Performance in Challenging Scenarios

The SAM-Adapter project is a cutting-edge development aimed at improving the Segment Anything Model (SAM) in scenarios where it typically underperforms. This initiative is led by a collaboration of researchers from prestigious institutions such as Zhejiang University, the Singapore University of Technology and Design, and Beihang University, among others. The project has been featured prominently in the proceedings of the IEEE/CVF International Conference on Computer Vision.

Key Features and Updates

SAM2 Adapter Integration: The project has recently extended support to incorporate SAM2, a more robust backbone for the model. This advancement opens up a wide array of applications, especially in tasks like camouflage, shadow, and medical image segmentation. The latest results and technical discussion can be found in their new report linked within their updates.

Performance Across Diverse Datasets: SAM-Adapter demonstrates its versatility through applications spanning multiple datasets and challenges. These include camouflaged object detection on datasets like COD10K, CAMO, and CHAMELEON, as well as shadow detection using the ISTD dataset. The tool also has implications in the medical field, exemplified by its efficacy in medical polyp segmentation using the Kvasir dataset.

Quick Start Guide

To dive into utilizing the SAM-Adapter, follow these basic setup instructions:

1. Environment Setup: The project is coded in Python 3.8 using PyTorch 1.13.0. Essential dependencies can be installed via:

   pip install -r requirements.txt
   

2. Dataset and Model Preparation: Download your dataset and place it in the ./load directory. Acquire the pre-trained SAM model, which is available here, and store it in the ./pretrained directory.

3. Training and Evaluation: Execute the following commands for training and evaluation, ensuring that your system has sufficient memory to handle the process:

   # Training command
   CUDA_VISIBLE_DEVICES=0,1,2,3 python -m torch.distributed.launch --nnodes 1 --nproc_per_node 4 loadddptrain.py --config configs/demo.yaml
   
   # Evaluation command
   python test.py --config [CONFIG_PATH] --model [MODEL_PATH]
   

Acknowledgments and Citation

If the SAM-Adapter proves beneficial in your research or projects, the developers kindly ask for citations to their work. Citations not only acknowledge their contributions but also support further advancements in this area.

Additionally, parts of the code were developed using resources from the Explicit Visual Prompt project by collaborators from the University of Macau and Tencent AI Lab.

For more information on this groundbreaking project, researchers and developers are encouraged to explore their comprehensive documentation and support resources provided on their project page.

This dynamic project highlights a significant step toward refining image segmentation techniques across a spectrum of real-world applications, attesting to the collaborative spirit of the global research community.