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README.md

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+---
+license: bsd-3-clause
+license_name: bsd-3-clause-code-cc-by-nc-data
+library_name: onnx
+tags:
+  - cellpose
+  - cell-segmentation
+  - microscopy
+  - onnx
+  - biology
+pipeline_tag: image-segmentation
+---
+
+# Cellpose cpsam — ONNX
+
+ONNX export of the [Cellpose](https://github.com/MouseLand/cellpose) **cpsam** (Cellpose-SAM) model for cell segmentation in microscopy images.
+
+## Model Details
+
+- **Architecture**: ViT-L based Transformer (SAM backbone + Cellpose readout)
+- **Parameters**: 304M
+- **Input**: `pixel_values` — float32 tensor `(1, 3, 256, 256)` (one tile)
+- **Output**: `output` — float32 tensor `(1, 3, 256, 256)` — channels are `(dY, dX, cellprob)`
+- **Opset**: 14
+
+The model weights are stored as external data (`model.onnx.data`) due to the 2GB protobuf limit.
+Both files must be in the same directory for inference.
+
+## Usage
+
+### Python (onnxruntime)
+```python
+import onnxruntime as ort
+import numpy as np
+
+sess = ort.InferenceSession("model.onnx", providers=["CUDAExecutionProvider"])
+tile = np.zeros((1, 3, 256, 256), dtype=np.float32)  # your preprocessed tile
+output = sess.run(None, {"pixel_values": tile})[0]    # (1, 3, 256, 256)
+dY, dX, cellprob = output[0, 0], output[0, 1], output[0, 2]
+```
+
+### Rust (cellpose-rs)
+```rust
+use cellpose_rs::{CellposeSession, SegmentParams, preprocess};
+
+let mut session = CellposeSession::new(Path::new("model.onnx"), false)?;
+let chw = preprocess::build_chw_image(phase, fluo, h, w);
+let masks = session.segment(&chw, h, w, SegmentParams::default())?;
+```
+
+See [cellpose-rs](https://github.com/keejkrej/cellpose-rs) for the full Rust inference library.
+
+## Post-processing
+
+The raw output requires Cellpose flow dynamics post-processing:
+1. Threshold `cellprob > 0` to get foreground
+2. Scale flows: `dP = dP * foreground / 5.0`
+3. 200-step Euler integration with bilinear interpolation
+4. Histogram-based seed detection (max-pool kernel=5, threshold=10)
+5. Connected-component labelling of seeds
+6. Assign each foreground pixel to its nearest seed
+7. Remove masks >40% of image area, fill holes, remove masks <15px
+
+## License
+
+The Cellpose **code** is © 2020 Howard Hughes Medical Institute, licensed under the
+[BSD 3-Clause License](https://github.com/MouseLand/cellpose/blob/main/LICENSE).
+
+⚠️ The Cellpose-SAM model is trained on data licensed under **CC-BY-NC**.
+See the [upstream notice](https://github.com/MouseLand/cellpose#readme).
+
+## Citation
+
+If you use this model, please cite the Cellpose-SAM paper:
+
+> Pachitariu, M., Rariden, M., & Stringer, C. (2025).
+> Cellpose-SAM: superhuman generalization for cellular segmentation.
+> *bioRxiv*. https://doi.org/10.1101/2025.04.28.651001
+
+```bibtex
+@article{pachitariu2025cellposesam,
+  title={Cellpose-SAM: superhuman generalization for cellular segmentation},
+  author={Pachitariu, Marius and Rariden, Maia and Stringer, Carsen},
+  journal={bioRxiv},
+  year={2025},
+  doi={10.1101/2025.04.28.651001}
+}
+```

model.onnx

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model.onnx.data

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