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cellpose-sam-wquant-w8a16
capicu-ai/cellpose-sam-wquant-w8a16
cellpose-sam-wquant-w8a16 is a image segmentation model from capicu-ai. Use it for the image segmentation task on the model card, and read the license before you ship it in a product. It is set up for cellpose. The card lists the license as bsd-3-clause.
<div align="center" <img src="https://cdn-uploads.huggingface.co/production/uploads/68ed488e386d9944bc2ea3a5/z6lWF8anMTDnO-E3zrFTU.png" alt="Capicú Technologies" / </div
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310 MB on disk
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.safetensors310 MB · 100%
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How the weights are stored.
U8303M · 99%
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Model card
From the Hugging Face model README
<div align="center">
<img src="https://cdn-uploads.huggingface.co/production/uploads/68ed488e386d9944bc2ea3a5/z6lWF8anMTDnO-E3zrFTU.png" alt="Capicú Technologies" />
</div>
Cellpose-SAM WQuant — W8A16
Ready-to-load W8A16 quantized variant of
Cellpose-SAM cpsam_v2.
Load
import runpy
from huggingface_hub import hf_hub_download
loader = runpy.run_path(
hf_hub_download("capicu-ai/cellpose-sam-wquant-w8a16", "runtime_loader.py")
)
model = loader["load_model"]()
Pass device="cuda" to load_model for CUDA.
Result
- Weight size: 295.8 MiB
- Compression versus FP32: 3.93x
- Evaluated paired samples: 20
- Complete bounded-panel criterion met: Yes
- Reload verification normalized RMSE: 0.0
results.json and model_registry.json contain the compact machine-readable
record.
Citation
If you use this model or code, please cite our paper:
@article{cruzromero2026retention,
title={Retention-Constrained Post-Training Quantization of Cellpose-SAM for Stem Cell Microscopy},
author={Cruz Romero, Sebasti{\'a}n A.},
journal={arXiv preprint arXiv:2609.21038},
note={Accepted at NeurIPS 2026 LXAI Workshop},
year={2026}
}
- Task
- Image Segmentation
- Library
- cellpose
- License
- bsd-3-clause
- Created
- Jul 31, 2026
- Updated
- Sep 21, 2026