EndoSAM

Fine-tune for endoscope clapster segmentation (adapted from SurgicalSAM but provided all scripts for fine-tune)

• EndoSAM
  • Installation tested on Ubuntu 20.04.6 LTS x86_64
  • Usage
  • Inference
  • Reference

Installation (tested on Ubuntu 20.04.6 LTS x86_64)

git clone https://huggingface.co./ChrisXiao/EndoSAM
cd EndoSAM
conda env create -f environment.yml
conda activate sam

If conda cannot install successfully, try

conda create -y -n sam python=3.10.11
conda activate sam
pip install -r requirements.txt

Usage

• Download (using wget or manual way) the SAM model checkpoint and place it into sam_weightsfolder, click the links below to download the checkpoint for the corresponding model type.

  • default or vit_h: ViT-H SAM model.
  • vit_l: ViT-L SAM model.
  • vit_b: ViT-B SAM model.

• Run the script (change the config file for play)

cd endoSAM
python train.py --cfg ../config/finetune.yaml

• GPU RAM Requirement
  Even though this is the fine-tune work, it requires a large GPU RAM. We tested on the EndoVis2017 [1] and EndoVis2018 [2] Dataset and image resolution is 1024 x 1024 with initial processing for the SAM. Use suitable batch size based on the VRAM you have
  • Batch Size 1 -> 6 GB RAM
  • Batch Size 2 -> 12 GB RAM
  • Batch Size 4 -> 21 GB RAM
  • Batch Size 8 -> 33 GB RAM

The training checkpoints, best model, loss plots and log files will be saved in thelog_folder, model_folder, ckpt_folder and plot_folderyou provide in the config file respectively.

Inference

python test.py --cfg ../config/finetune.yaml

The prediction results will be saved into the test_folder you provide in the config file.

Reference

[1] Allan, M.; Shvets, A.; Kurmann, T.; Zhang, Z.; Duggal, R.; Su, Y.-H.; Rieke, N.; Laina, I.; Kalavakonda, N.; Bodenstedt, S.; Herrera, L.; Li, W.; Iglovikov, V.; Luo, H.; Yang, J.; Stoyanov, D.; Maier-Hein, L.; Speidel, S.; and Azizian, M. 2019. 2017 Robotic Instrument Segmentation Challenge. arXiv:1902.06426.

[2] Allan, M.; Kondo, S.; Bodenstedt, S.; Leger, S.; Kadkhodamohammadi, R.; Luengo, I.; Fuentes, F.; Flouty, E.; Mohammed, A.; Pedersen, M.; Kori, A.; Alex, V.; Krishnamurthi, G.; Rauber, D.; Mendel, R.; Palm, C.; Bano, S.; Saibro, G.; Shih, C.-S.; Chiang, H.-A.; Zhuang, J.; Yang, J.; Iglovikov, V.; Dobrenkii, A.; Reddiboina, M.; Reddy, A.; Liu, X.; Gao, C.; Unberath, M.; Kim, M.; Kim, C.; Kim, C.; Kim, H.; Lee, G.; Ullah, I.; Luna, M.; Park, S. H.; Azizian, M.; Stoyanov, D.; Maier-Hein, L.; and Speidel, S. 2020. 2018 Robotic Scene Segmentation Challenge. arXiv:2001.11190.