---
datasets:
- HC18
license: cc-by-nc-4.0
metrics:
- f1
- auroc
pipeline_tag: zero-shot-image-classification
tags:
- medical
- ultrasound
- vision
- knowledge-distillation
---

# MobileFetalCLIP

**Selective Repulsive Knowledge Distillation for Mobile Fetal Ultrasound Analysis**

[Project Website](https://numansaeed.com/MobileFetalCLIP/) | [Paper](https://huggingface.co/papers/2603.05421) | [GitHub Repository](https://github.com/numanai/MobileFetalCLIP)

MobileFetalCLIP is a highly efficient foundation model designed specifically for fetal ultrasound analysis on point-of-care, low-resource devices (like smartphones). It achieves this by distilling knowledge from a massive 427M parameter teacher model into a tiny 11.4M parameter student model using a novel technique called **Selective Repulsive Knowledge Distillation**.

Despite being **26× smaller** and **24× faster**, MobileFetalCLIP *surpasses* its massive teacher on standard validity benchmarks (HC18) and retains 97-98% of linear probing performance across tasks.

## Model Details

- **Architecture:** FastViT (Student) distilled from ViT-L/14 (Teacher)
- **Parameters:** 11.4M Visual Parameters (75M Total)
- **Modality:** Ultrasound Image / Text
- **License:** CC BY-NC 4.0 (Non-Commercial Research Use Only)

## Key Contributions

1. **Selective Repulsive KD:** A novel methodology that explicitly pushes apart non-matching image-text embeddings during distillation, improving representation geometry.
2. **Mobile Deployment:** Native efficiency, capable of running inference at 1.6ms on an iPhone 16 Pro (compared to the teacher which entirely OOMs).
3. **SOTA Performance:** Establishes a new efficiency-accuracy Pareto frontier for prenatal ultrasound AI.

## Usage

Please refer to the official GitHub repository for installation instructions, dataset preparation, and inference scripts:
🔗 **[GitHub: numanai/MobileFetalCLIP](https://github.com/numanai/MobileFetalCLIP)**

## Citation

If you find this model or codebase useful for your research, please cite the paper:

```bibtex
@article{saeed2026mobilefetalclip,
  title     = {MobileFetalCLIP: Selective Repulsive Knowledge Distillation
               for Mobile Fetal Ultrasound Analysis},
  author    = {Saeed, Numan and Maani, Fadillah Adamsyah and Yaqub, Mohammad},
  journal   = {arXiv preprint arXiv:2603.05421},
  year      = {2026}
}
```