Add files using upload-large-folder tool

.gitattributes

@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+zebrafish_fibroblast_cpsam filter=lfs diff=lfs merge=lfs -text
+zebrafish_macrophage_cpsam filter=lfs diff=lfs merge=lfs -text

MODEL_LICENSE.md

@@ -0,0 +1,24 @@
+# Model Weight License
+
+The released fine-tuned model weights are licensed under Creative Commons
+Attribution-NonCommercial 4.0 International (`CC BY-NC 4.0`).
+
+In practical terms, this allows sharing and adaptation with attribution for
+non-commercial use. It does not grant commercial use rights for the model
+weights.
+
+## Rationale
+
+This project fine-tunes Cellpose-SAM (`cpsam`). The Cellpose software and the
+upstream `mouseland/cellpose-sam` Hugging Face repository are distributed under
+BSD 3-Clause, while the Cellpose README notes that Cellpose-SAM was trained on
+CC-BY-NC data and that the Cellpose annotated dataset is CC-BY-NC.
+
+Because this release is an experimental fine-tune intended for research use,
+the model weights are published under `CC BY-NC 4.0` to preserve a
+non-commercial use boundary. This is a license choice for these released
+fine-tuned weights; it is not a statement that the GitHub code has the same
+license.
+
+The GitHub code, notebooks, and documentation are separate from the model
+weights and are licensed under the BSD 3-Clause License.

README.md

@@ -0,0 +1,110 @@
+---
+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.

zebrafish_fibroblast_cpsam

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:321056e7687254189529e8a424b59c8a84cd42560c03b3b677c9ee601bc368e3
+size 1218639667

zebrafish_macrophage_cpsam

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2c71c9ba9b6a41d39b02027721bd3edbb0e5a9fff969f58fa774aedd09760fcc
+size 1218639667