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TS-SAM: Fine-tuning Segment Anything Model For DownStream Tasks

Yang Yu, Chen Xu, Kai Wang*

NanKai University

In Proceedings of the IEEE/CVF International Conference on Multimedia and Expo

Paper link:

Update on 12 April: This paper is scheduled for oral presentation in ICME 2024

Update on 13 March: This paper is accepted by ICME 2024.

Directory Structure

TS-SAM/
│   └── .gitignore
│   └── LICENSE
│   └── README.md
│   └── requirements.txt
│   └── sod_metric.py
│   └── test.py
│   └── train.py
│   └── utils.py
│   └── __init__.py
└── configs/
│   │   └── cod-tssam-vit-b.yaml
│   │   └── cod-tssam-vit-h.yaml
│   │   └── sd-tssam-vit-b.yaml
│   │   └── sd-tssam-vit-h.yaml
│   │   └── sod-tssam-vit-b.yaml
│   │   └── sod-tssam-vit-h.yaml
└── datasets/
└── models/
│   │   └── block.py
│   │   └── bn_helper.py
│   │   └── iou_loss.py
│   │   └── models.py
│   │   └── sam.py
│   │   └── __init__.py
│   └── mmseg/
│   │   └── apis/
│   │   └── core/
│   │   └── datasets/
│   │   └── models/
│   │   │   │   └── builder.py
│   │   │   │   └── __init__.py
│   │   │   └── losses/
│   │   │   └── sam/
│   │   │   │   │   └── common.py
│   │   │   │   │   └── feature_fusion_decoder.py (TS-SAM Decoder)
│   │   │   │   │   └── image_encoder.py
│   │   │   │   │   └── image_encoder_ts.py (TS-SAM Encoder)
│   │   │   │   │   └── mask_decoder.py
│   │   │   │   │   └── prompt_encoder.py
│   │   │   │   │   └── sam.py
│   │   │   │   │   └── transformer.py
│   │   │   │   │   └── __init__.py
│   │   │   └── utils/
│   │   └── ops/
│   │   └── utils/
└── pretrained/
│   │   └── .gitignore

Environment Setup

This code was implemented with Python 3.8 and PyTorch 1.13.0. You can install all the requirements via:

pip install -r requirements.txt --extra-index-url https://download.pytorch.org/whl/cu116
# install mmcv
pip install -U openmim
mim install mmcv==1.7.0

Dataset Download

Camouflaged Object Detection

Shadow Detection

Salient Object Detection

Polyp Segmentation - Medical Applications

Quick Start

  1. Download the dataset.
  2. Download the original SAM(Segment Anything) checkpoint and put it in ./pretrained.
  3. Set the dataset path and pretrained model path in the yaml file in ./configs
  4. Training:
CUDA_VISIBLE_DEVICES=0,1,2,3 python -m torch.distributed.launch --nnodes 1 --nproc_per_node 4 train.py --config [CONFIG_PATH]

Config

  1. cod-tssam-vit-h.yaml: The configuration for Camouflaged Object Detection(COD), using SAM vit-h as the backbone for training.
  2. sd-tssam-vit-h.yaml: The configuration for Shadow Detection(SD), using SAM vit-h as the backbone for training.
  3. sod-tssam-vit-h.yaml: The configuration for Salient Object Detection(SOD), using SAM vit-h as the backbone for training.

Train

CUDA_VISIBLE_DEVICES=0,1,2,3 python -m torch.distributed.launch --nnodes 1 --nproc_per_node 4 train.py --config [CONFIG_PATH]

Test

CUDA_VISIBLE_DEVICES=0 python -m torch.distributed.launch --nnodes 1 --nproc_per_node 1 test.py --config [CONFIG_PATH] --model [CHECKPOINT_PATH] --save True

Weights

https://drive.google.com/drive/folders/1dQJiWONDSTrUKCkDLktxaabzgim0OfsQ?usp=drive_link

Citation

If you find our work useful in your research, please consider citing:

@article{yu2024ts,
  title={TS-SAM: Fine-Tuning Segment-Anything Model for Downstream Tasks},
  author={Yu, Yang and Xu, Chen and Wang, Kai},
  journal={arXiv preprint arXiv:2408.01835},
  year={2024}
}

Contact

f you have any questions, please feel free to contact us via email [email protected] or WeChat ChenXu2230744290.

Acknowledgements

The part of the code is derived from SAM-adapter .