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README.md
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---
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license: gpl-2.0
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tags:
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library_name: pytorch
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pipeline_tag: image-segmentation
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datasets:
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metrics:
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#
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| Item | Value |
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| Backbone | HRNet-W32 |
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| Input | 256 × 8192 (freq × time) |
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| Size | 381.75 MB |
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| Formats | .fits (PSRFITS), .fil (Filterbank) |
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| Bit Depth | 1/2/4/8/32-bit |
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## QUICK START
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Or download directly from Files and versions.
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## TEST SAMPLES
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When using the test samples, please also cite the original observations:
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GPL-2.0 | Based on MMDetection & PRESTO
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license: gpl-2.0
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tags:
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- Radio Astronomy
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- Mask-RCNN
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- Pulsar
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- RRAT
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- FRB
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- Signal Processing
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library_name: pytorch
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pipeline_tag: image-segmentation
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datasets:
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- CRAFTS-FRT
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metrics:
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- recall
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- FPR
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# FRTSearch: Fast Radio Transient Search
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[](https://doi.org/10.57760/sciencedb.Fastro.00038) [](https://doi.org/10.57760/sciencedb.Fastro.00038) [](https://github.com/BinZhang109/FRTSearch)
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**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)**.
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## Model Info
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| Item | Value |
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|------|-------|
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| Backbone | HRNet-W32 |
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| Input | 256 × 8192 (freq × time) |
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| Size | 381.75 MB |
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| Formats | `.fits` (PSRFITS), `.fil` (Filterbank) |
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| Bit Depth | 1/2/4/8/32-bit |
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## QUICK START
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```
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Or download directly from [Files and versions](https://huggingface.co/waterfall109/FRTSearch/tree/main).
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## TEST SAMPLES
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```
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### Test Sample References
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When using the test samples, please also cite the original observations:
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- **FAST samples**: [Guo et al. (2025)](https://doi.org/10.3847/1538-4365/adf42d)
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- **SKA samples**: [Shannon et al. (2018)](https://doi.org/10.1038/s41586-018-0588-y)
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## License & Acknowledgments
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GPL-2.0 | Based on [MMDetection](https://github.com/open-mmlab/mmdetection) & [PRESTO](https://github.com/scottransom/presto)
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---
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<div align="center">
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<sub>🌌 Questions? <a href="https://github.com/BinZhang109/FRTSearch/issues">GitHub Issues</a></sub>
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</div>
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```
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---
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