---
license: other
license_name: dinov3-license
pipeline_tag: image-segmentation
library_name: cervicalseg
tags:
  - medical-imaging
  - cervical
  - semantic-segmentation
  - dinov3
  - dpt
  - wtconv
---

# CervicalSeg

CervicalSeg is a four-class semantic segmentation model for cervical colposcopy images. It combines
a DINOv3 ViT-S/16 backbone, a DPT decoder, and WTConv in the final two high-resolution decoder
fusion stages.

宫颈阴道镜图像四分类语义分割模型，采用 DINOv3 ViT-S/16、DPT 解码器，并在最后两个高分辨率
融合阶段使用 WTConv。

## Classes / 类别

| Index | Class | Colour |
|---:|---|---|
| 0 | background | black |
| 1 | blue | blue |
| 2 | green | green |
| 3 | red | red |

## Usage / 使用方法

```bash
pip install cervicalseg
```

```python
from cervicalseg import CervicalSeg

segmenter = CervicalSeg(device="auto")
result = segmenter.predict("image.jpg")
result.save_mask("mask.png")
result.save_color_mask("color_mask.png")
result.save_overlay("overlay.jpg")
```

The first call downloads `model.safetensors`; later calls reuse the Hugging Face cache. Output masks
are restored to the original image size.

首次调用会下载 `model.safetensors`，后续调用复用 Hugging Face 缓存。输出 mask 会恢复到原图尺寸。

## Evaluation / 评估结果

The data were split at the patient level to prevent patient leakage between train, validation, and
test sets.

数据按患者划分，避免同一患者同时出现在训练、验证或测试集合中。

| Split | Samples | Foreground mIoU | Foreground Dice | Pixel accuracy |
|---|---:|---:|---:|---:|
| Validation | 221 | 0.4075 | 0.5704 | 0.9121 |
| Test | 216 | 0.3633 | 0.5296 | 0.8891 |

Test-set per-class metrics:

| Class | Precision | Recall | Dice | IoU |
|---|---:|---:|---:|---:|
| blue | 0.5651 | 0.5697 | 0.5674 | 0.3961 |
| green | 0.3784 | 0.4950 | 0.4289 | 0.2730 |
| red | 0.6850 | 0.5219 | 0.5924 | 0.4209 |

## Intended use and limitations / 用途与限制

- Research use only.
- Not a medical device and not for clinical diagnosis or treatment decisions.
- Predictions require review by qualified professionals.
- Performance may not generalize to devices, institutions, populations, or acquisition conditions
  not represented in the training data.
- Green-region segmentation is the weakest class in the reported test results.

- 仅用于研究。
- 不是医疗器械，不得直接用于临床诊断或治疗决策。
- 预测结果需要由具备资质的专业人员复核。
- 对训练数据未覆盖的设备、机构、人群和采集条件，模型性能可能下降。

## License / 许可证

The CervicalSeg Python wrapper is released under the MIT License. The trained weights contain
DINOv3 materials and are distributed under the DINOv3 License included as `DINOV3_LICENSE.md`.
WTConv-derived code retains its MIT notice in `WTCONV_LICENSE`.

CervicalSeg Python 封装代码采用 MIT License。训练权重包含 DINOv3 材料，权重分发遵循仓库中的
`DINOV3_LICENSE.md`。WTConv 衍生代码保留其 MIT 许可声明。

## Author

Shi Minghai (`PlanetSMH`)