Cellsam Onnx

Grant of Copyright License: ... each Contributor hereby grants to You a non-commercial, academic perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable copyright license...

See the LICENSE file for full terms. Commercial use requires contacting the original authors ([email protected]).

Usage with cellsam-rs

This model is designed for high-performance inference using cellsam-rs.

# Install cellsam-rs (example)
cargo install cellsam-rs

# Run inference
cellsam-rs infer \
  --input image.tif \
  --model models/cellsam-onnx \
  --output mask.tif

Model Details

• Original Repository: vanvalenlab/cellSAM
• Architecture: SAM-based (Segment Anything Model) specialized for cell segmentation.
• Input: Phase contrast + Fluorescence images (2-channel).
• Format: ONNX (Opset 17).

Citation

If you use this model, please cite the original CellSAM paper:

@article{Israel2024,
  title={CellSAM: A Foundation Model for Cell Segmentation},
  author={Israel, Uri and others},
  journal={Nature Methods},
  year={2024}
}

mask_decoder.onnx

onnx · 569 KB · SHA-256 067905fefe3f…3cc0 · Hugging Face

--- license: other license_name: modified-apache-2.0 license_link: LICENSE pipeline_tag: image-segmentation tags: - biology - microscopy - segmentation - cell-segmentation - onnx --- # CellSAM (ONNX) This repository contains the **ONNX export** of the [CellSAM](https://github.com/vanvalenlab/cellSAM) model for cell segmentation. **⚠️ LICENSE WARNING: NON-COMMERCIAL / ACADEMIC USE ONLY ⚠️** This model is derived from the original [CellSAM](https://github.com/vanvalenlab/cellSAM) work and is subject to a **Modified Apache License 2.0**. > **Grant of Copyright License:** ... each Contributor hereby grants to You a **non-commercial, academic** perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable copyright license... See the [LICENSE](LICENSE) file for full terms. Commercial use requires contacting the original authors (`[email protected]`). ## Usage with cellsam-rs This model is designed for high-performance inference using [cellsam-rs](https://github.com/keejkrej/cellsam-rs). ```bash # Install cellsam-rs (example) cargo install cellsam-rs # Run inference cellsam-rs infer \ --input image.tif \ --model models/cellsam-onnx \ --output mask.tif ``` ## Model Details - **Original Repository:** [vanvalenlab/cellSAM](https://github.com/vanvalenlab/cellSAM) - **Architecture:** SAM-based (Segment Anything Model) specialized for cell segmentation. - **Input:** Phase contrast + Fluorescence images (2-channel). - **Format:** ONNX (Opset 17). ## Citation If you use this model, please cite the original CellSAM paper: ```bibtex @article{Israel2024, title={CellSAM: A Foundation Model for Cell Segmentation}, author={Israel, Uri and others}, journal={Nature Methods}, year={2024} } ```