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https://github.com/cviu-csu/des-sam

DES-SAM: Distillation-Enhanced Semantic SAM

https://github.com/cviu-csu/des-sam

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Repository

DES-SAM: Distillation-Enhanced Semantic SAM

Basic Info
  • Host: GitHub
  • Owner: CVIU-CSU
  • Language: Python
  • Default Branch: main
  • Size: 7.43 MB
Statistics
  • Stars: 0
  • Watchers: 2
  • Forks: 0
  • Open Issues: 0
  • Releases: 0
Created about 2 years ago · Last pushed almost 2 years ago
Metadata Files
Readme

README.md

DES-SAM

Distillation-Enhanced Semantic SAM for Cervical Nuclear Segmentation with Box Annotation

[Model] [Paper] [BibTeX]

Lina Huang, Yixiong Liang, JianFeng Liu

Model Overview


*Overview of DES-SAM model architecture*

Install

On an NVIDIA 3090 Tensor Core GPU machine, with CUDA toolkit enabled.

  1. Download our repository and open the DES-SAM git clone git@github.com:CVIU-CSU/DES-SAM.git cd DES-SAM

  2. Install MMDetection 🛠️Installation and its dependencies

```Shell

Step 1. Create a conda environment and activate it

conda create --name dessam python=3.8 -y conda activate dessam

Step 2. Install PyTorch following official instructions, e.g.

pip install torch==1.13.1+cu116 torchvision==0.14.1+cu116 torchaudio==0.13.1 --extra-index-url https://download.pytorch.org/whl/cu116

Step 3. MMDetection Installation

pip install -U openmim mim install mmengine mim install "mmcv==2.0.1" cd mmdetection pip install -v -e .

Step 4. Package Installation

pip install -r requirements.txt

Step 5. SAM Installation

pip install segment-anything ```

Results and Models

  • Visual Result


  • Model Download

The MMDetection based models can be accessed from Baiduyun.

Train & Test

Download the pretrained model to train DES-SAM.

Our code is based on coco datasets, datasets need to be converted to coco first.

```shell

Train

CUDAVISIBLEDEVICES=0,1,2,3,4,5,6,7 bash ./tools/disttrain.sh {path}/mmdetection/configs/dessam/PatchSeg/des-sam-patch.py 8 ```

```shell

Test

CUDAVISIBLEDEVICES=0,1,2,3,4,5,6,7 bash ./tools/disttest.sh {path}/mmdetection/configs/dessam/PatchSeg/des-sam-patch.py {model_path} 8 ```

Acknowledgements

We would like to express our gratitude to the authors and developers of the exceptional repositories that this project is built upon: - MMDETECTION - CNSeg - SAM - VitDet - Faster R-CNN - BoxSnake - BoxLevelSet

Their contributions have been invaluable to our work.

Citation

If you find it useful for your your research and applications, please cite using this BibTeX:

bibtex @inproceedings{huang2024des-sam, title={DES-SAM: Distillation-Enhanced Semantic SAM for Cervical Nuclear Segmentation with Box Annotation}, author={Lina Huang, Yixiong Liang and Jianfeng Liu}, booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI)}, year={2024}, publisher={Springer} }

Owner

  • Name: CVIU-CSU
  • Login: CVIU-CSU
  • Kind: organization

GitHub Events

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Dependencies

mmdetection/requirements/albu.txt pypi
  • albumentations >=0.3.2
mmdetection/requirements/build.txt pypi
  • cython *
  • numpy *
mmdetection/requirements/docs.txt pypi
  • docutils ==0.16.0
  • myst-parser *
  • sphinx ==4.0.2
  • sphinx-copybutton *
  • sphinx_markdown_tables *
  • sphinx_rtd_theme ==0.5.2
  • urllib3 <2.0.0
mmdetection/requirements/mminstall.txt pypi
  • mmcv >=2.0.0rc4,<2.2.0
  • mmengine >=0.7.1,<1.0.0
mmdetection/requirements/multimodal.txt pypi
  • fairscale *
  • nltk *
  • pycocoevalcap *
  • transformers *
mmdetection/requirements/optional.txt pypi
  • cityscapesscripts *
  • fairscale *
  • imagecorruptions *
  • scikit-learn *
mmdetection/requirements/readthedocs.txt pypi
  • mmcv >=2.0.0rc4,<2.2.0
  • mmengine >=0.7.1,<1.0.0
  • scipy *
  • torch *
  • torchvision *
  • urllib3 <2.0.0
mmdetection/requirements/runtime.txt pypi
  • matplotlib *
  • numpy *
  • pycocotools *
  • scipy *
  • shapely *
  • six *
  • terminaltables *
  • tqdm *
mmdetection/requirements/tests.txt pypi
  • asynctest * test
  • cityscapesscripts * test
  • codecov * test
  • flake8 * test
  • imagecorruptions * test
  • instaboostfast * test
  • interrogate * test
  • isort ==4.3.21 test
  • kwarray * test
  • memory_profiler * test
  • nltk * test
  • onnx ==1.7.0 test
  • onnxruntime >=1.8.0 test
  • parameterized * test
  • prettytable * test
  • protobuf <=3.20.1 test
  • psutil * test
  • pytest * test
  • transformers * test
  • ubelt * test
  • xdoctest >=0.10.0 test
  • yapf * test
mmdetection/requirements/tracking.txt pypi
  • mmpretrain *
  • motmetrics *
  • numpy <1.24.0
  • scikit-learn *
  • seaborn *
mmdetection/requirements.txt pypi
mmdetection/setup.py pypi