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license: gpl-3.0
pipeline_tag: object-detection
tags:
- open-vocabulary-detection
- cytopathology
- medical-imaging
---
# CytoSPM
Model weights of **CytoSPM: Open-Vocabulary Cytopathology Detection with Structured Prompt Bank** (BIBM 2026).
- Paper: [arXiv:2609.31314](https://arxiv.org/abs/2609.31314)
- Code: [github.com/CVIU-CSU/CytoSPM](https://github.com/CVIU-CSU/CytoSPM)
## Files
| File | Backbone | Base AP / AP50 / AP75 | Novel AP / AP50 / AP75 | SHA256 |
|------|----------|-----------------------|------------------------|--------|
| `cytospm_convnext_t.pth` | ConvNeXt-T | 33.54 / 52.20 / 38.16 | 15.00 / 21.00 / 17.10 | `bc94215314319f712c731e8f5f24388df29d254cc2029695d32e815efb3aac4d` |
This is the model reported in the paper (Table III). The numbers above were
measured with the released code on an RTX 3090; the paper values
(33.55 / 52.20 / 38.17 and 15.00 / 21.00 / 17.20) were measured on an RTX 4090.
Base AP is the COCO AP over the 24 base categories of PentaCyto; novel AP is the
zero-shot COCO AP over the 9 held-out categories.
The file contains model weights only (`state_dict`) and loads with
`torch.load(..., weights_only=True)`.
## Usage
```bash
git clone https://github.com/CVIU-CSU/CytoSPM.git && cd CytoSPM
CKPT_FILES=cytospm_convnext_t.pth bash tools/fetch_checkpoints.sh # downloads and checks SHA256
python tools/test.py configs/cytospm.py checkpoints/cytospm_convnext_t.pth
python tools/test_novel.py configs/cytospm.py checkpoints/cytospm_convnext_t.pth
```
See the code repository for installation, data preparation and training.
The PentaCyto images and annotations are not included.
## Citation
```bibtex
@inproceedings{li2026cytospm,
title = {CytoSPM: Open-Vocabulary Cytopathology Detection with Structured Prompt Bank},
author = {Li, Wenjie and Xu, Zishan and Huang, Jinyang and Nie, Zhengxin and Kan, Shichao and Liang, Yixiong},
booktitle = {IEEE International Conference on Bioinformatics and Biomedicine (BIBM)},
year = {2026}
}
```
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