README.md

---
license: gpl-2.0
tags:
- Radio Astronomy
- Pulsar
- RRAT
- FRB
- Signal Processing
library_name: pytorch
pipeline_tag: image-segmentation
datasets:
- CRAFTS-FRT
metrics:
- recall
- FPR
---

# FRTSearch: Fast Radio Transient Search

[![Paper](https://img.shields.io/badge/Paper-AASTeX-blue.svg)](https://doi.org/10.57760/sciencedb.Fastro.00038) [![Dataset](https://img.shields.io/badge/Dataset-CRAFTS--FRT-yellow.svg)](https://doi.org/10.57760/sciencedb.Fastro.00038) [![GitHub](https://img.shields.io/badge/GitHub-FRTSearch-black.svg)](https://github.com/BinZhang109/FRTSearch)

**FRTSearch** is an end-to-end deep learning framework for detecting and characterizing Fast Radio Transients (FRTs), including: **Pulsars**, **Rotating Radio Transients (RRATs)** and **Fast Radio Bursts (FRBs)**.

## Model Info

| Item | Value |
|------|-------|
| Backbone | HRNet-W32 |
| Input | 256 × 8192 (freq × time) |
| Size | 400 MB |
| Formats | `.fits` (PSRFITS), `.fil` (Filterbank) |
| Bit Depth | 1/2/4/8/32-bit |

## QUICK START

```python
from huggingface_hub import hf_hub_download

model_path = hf_hub_download(
    repo_id="waterfall109/FRTSearch",
    filename="models/hrnet_epoch_36.pth"
)
```

Or download directly from [Files and versions](https://huggingface.co/waterfall109/FRTSearch/tree/main).

## TEST SAMPLES

This repository includes 5 test samples from 2 different telescopes to demonstrate cross-facility performance:

| Telescope | FRB | DM (pc cm⁻³) |
| :--- | :--- | :--- |
| FAST | 20121102, 20180301, 20201124 | 565, 420, 525 |
| ASKAP | 20180119, 20180212 | 400, 168 |

## CITATION

```bibtex
@article{zhang2026frtsearch,
  title={FRTSearch: Unified Detection and Parameter Inference of Fast Radio Transients using Instance Segmentation},
  author={Zhang, Bin and Wang, Yabiao and Xie, Xiaoyao et al.},
  year={2026}
}
```


### Test Sample References

When using the test samples, please also cite the original observations:

- **FAST samples**: [Guo et al. (2025)](https://doi.org/10.3847/1538-4365/adf42d)
- **SKA samples**: [Shannon et al. (2018)](https://doi.org/10.1038/s41586-018-0588-y)

## License & Acknowledgments

GPL-2.0 | Based on [MMDetection](https://github.com/open-mmlab/mmdetection) & [PRESTO](https://github.com/scottransom/presto)

<div align="center">
  <sub>Exploring the dynamic universe with AI 🌌📡 | <a href="https://github.com/BinZhang109/FRTSearch/issues">Issues</a></sub>
</div>