---
license: mit
---
# Gatekeeper Cervix Detector

This model serves as a **binary gatekeeper** for cervical cancer screening systems. It quickly determines whether an input image is a valid cervical image before passing it to a downstream diagnostic model (MedSigLip).

## Model Details

- **Model Name**: Gatekeeper Cervix Detector
- **Base Architecture**: MobileNetV3-Small
- **Task**: Binary classification (cervix vs not-cervix)
- **Input Size**: 224×224×3 (RGB)
- **Input Normalization**: [-1, 1] (mean = [0.5, 0.5, 0.5], std = [0.5, 0.5, 0.5])
- **Output**: Probability that the image contains a cervix (sigmoid output)
- **Threshold**: 0.70 (images below this confidence are rejected)
- **License**: CC BY 4.0
- **Repository**: [Link to this repo]

## Intended Use

This model is designed to be used as the **first stage** in a two-stage pipeline:

1. **Gatekeeper** (this model): Filters out non-cervix images (random photos, other medical images, poor quality, etc.).
2. **MedSigLip** (downstream model): Only runs if the gatekeeper accepts the image. It performs cancer stage classification or similarity scoring.

**Use Case**: Maternal health screening in low-resource settings where non-experts may capture images using blind sweeps.

## Performance

**Test Set Results (threshold = 0.70)**

- Accuracy: 99.94%
- Precision: 100.00%
- Recall/Sensitivity: 99.88%
- Specificity: 100.00%
- AUC: 1.0000
- Rejection Rate: ~52.0%

The model shows excellent generalization and very strong rejection of non-cervix images while maintaining high sensitivity on true cervix images.

**Class Distribution**
- Cervix: 48.0%
- Not-cervix: 52.0%
- The split is stratified across train/validation/test sets.

## How to Use

### Loading the Model

```python
from transformers import AutoModel
model = AutoModel.from_pretrained("your-username/gatekeeper-cervix-detector")