---
license: cc-by-nc-4.0
library_name: cellpose
base_model: mouseland/cellpose-sam
pipeline_tag: image-segmentation
tags:
  - biology
  - microscopy
  - image-segmentation
  - cell-segmentation
  - cellpose
  - zebrafish
  - experimental
---

# Zebrafish Cellpose-SAM Fine-Tunes

Experimental Cellpose-SAM fine-tunes for zebrafish microscopy cell
segmentation.

## What Is Included

This model repository contains two Cellpose model files:

| File | Target cells | Training pairs | Validation pairs | Size | SHA-256 |
| --- | --- | ---: | ---: | ---: | --- |
| `zebrafish_macrophage_cpsam` | Macrophages | 28 | 7 | 1,218,639,667 bytes | `2c71c9ba9b6a41d39b02027721bd3edbb0e5a9fff969f58fa774aedd09760fcc` |
| `zebrafish_fibroblast_cpsam` | Fibroblasts | 46 | 12 | 1,218,639,667 bytes | `321056e7687254189529e8a424b59c8a84cd42560c03b3b677c9ee601bc368e3` |

The source microscopy data were 3D zebrafish stacks. The fine-tunes were
trained from extracted 2D z-slices with Cellpose-style instance masks.

## Intended Use

Use these weights for exploratory segmentation of similar zebrafish microscopy
data. Performance should be checked on representative images from your own
acquisition conditions before using the masks for quantitative biological
analysis.

## Training Data

- Base model: Cellpose-SAM `cpsam`.
- Training software: Cellpose `4.1.1`.
- Training hardware: CPU-only cluster nodes.
- Labels: initial Cellpose output followed by manual correction/segmentation.

## Data Scope

- Trained on extracted 2D z-slices from one 3D zebrafish time point/source
  context.
- Validation examples are held-out z-slices from the same imaging context.
- Labels were initialized with Cellpose and manually corrected/segmented.
- Best suited to similar zebrafish microscopy acquisitions; check a few
  representative images before quantitative use.

## Example Usage

Download a model file and pass its local path to Cellpose:

```bash
hf download SDu90/zebrafish-cellpose-finetunes \
  zebrafish_fibroblast_cpsam \
  --local-dir models
```

```python
from cellpose import models
import tifffile as tiff

image = tiff.imread("input_stack.tif")
model = models.CellposeModel(
    gpu=False,
    pretrained_model="models/zebrafish_fibroblast_cpsam",
)

masks, flows, styles = model.eval(
    image,
    do_3D=False,
    z_axis=0,
    stitch_threshold=0.4,
    diameter=15,
    cellprob_threshold=0.0,
    min_size=100,
)
```

For GPU inference, initialize Cellpose with `gpu=True` and an appropriate torch
device.

## License

The model weights are released under Creative Commons
Attribution-NonCommercial 4.0 International (`CC BY-NC 4.0`). The companion
GitHub code, notebooks, and documentation are licensed separately under BSD
3-Clause.

## Citation

If you use these weights, please cite this Hugging Face repository and the
relevant Cellpose papers:

> Pachitariu, M., Rariden, M., & Stringer, C. (2025). Cellpose-SAM:
> superhuman generalization for cellular segmentation. bioRxiv.

> Stringer, C., Wang, T., Michaelos, M., & Pachitariu, M. (2021). Cellpose: a
> generalist algorithm for cellular segmentation. Nature Methods, 18(1),
> 100-106.

> Pachitariu, M. & Stringer, C. (2022). Cellpose 2.0: how to train your own
> model. Nature Methods, 1-8.