Update README.md

README.md

@@ -3,66 +3,42 @@ language:
 - en
 license: gpl-2.0
 tags:
-- radio-astronomy
-- fast-radio-bursts
-- pulsar
-- instance-segmentation
-- mask-rcnn
-- signal-processing
+- Radio Astronomy
+- Pulsar
+- Rotating Radio Transients
+- Fast Radio Bursts
+- Mask-RCNN
+- Signal Processing
 library_name: pytorch
 pipeline_tag: image-segmentation
 datasets:
 - CRAFTS-FRT
 metrics:
 - recall
-- precision
+- FPR
 ---
-# FRTSearch: Fast Radio Transient Detection Model
 
-[![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)
-[![Python](https://img.shields.io/badge/Python-3.10+-green.svg)](https://www.python.org/) 
-[![License](https://img.shields.io/badge/License-GPL--2.0-grey.svg)](https://github.com/BinZhang109/FRTSearch/blob/main/LICENSE)
+# FRTSearch: Fast Radio Transient Detection
 
----
-
-## Model Description
-
-**FRTSearch** is an end-to-end deep learning framework for detecting and characterizing Fast Radio Transients (FRTs), including:
-- **Fast Radio Bursts (FRBs)**
-- **Pulsars**
-- **Rotating Radio Transients (RRATs)**
-
-### Architecture
-
-- **Backbone**: HRNet-W32 (High-Resolution Network)
-- **Detection Head**: Mask R-CNN
-- **Training Epoch**: 36
-- **Input Dimensions**: 256 (frequency channels) × 8192 (time samples)
-- **Model Size**: 381.75 MB
----
-
-## Supported Data Formats
+[![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)
 
-- **PSRFITS** (`.fits`) - Standard format for pulsar data
-- **Sigproc Filterbank** (`.fil`) - Legacy filterbank format
+**FRTSearch** is an end-to-end deep learning framework for detecting and characterizing Fast Radio Transients (FRTs), including: **Pulsars**, and **Rotating Radio Transients (RRATs)** **Fast Radio Bursts (FRBs)**.
 
-### Supported Bit Depths
+## Model Info
 
-- 1-bit quantization
-- 2-bit quantization
-- 4-bit quantization
-- 8-bit quantization
-- 32-bit quantization
----
+| Item | Value |
+|------|-------|
+| Backbone | HRNet-W32 |
+| Input | 256 × 8192 (freq × time) |
+| Size | 381.75 MB |
+| Formats | `.fits` (PSRFITS), `.fil` (Filterbank) |
+| Bit Depth | 1/2/4/8/32-bit |
 
-## Download Model Weights
+## Quick Start
 
 ```python
 from huggingface_hub import hf_hub_download
 
-# Download model weights
 model_path = hf_hub_download(
     repo_id="waterfall109/FRTSearch",
     filename="models/hrnet_epoch_36.pth"
@@ -71,39 +47,23 @@ model_path = hf_hub_download(
 
 Or download directly from [Files and versions](https://huggingface.co/waterfall109/FRTSearch/tree/main).
 
----
 
 ## Test Samples
 
 This repository includes 7 test samples from 3 different telescopes to demonstrate cross-facility performance:
 
-### FAST Telescope (3 samples)
-- FRB 20121102 (DM=565.0 pc cm⁻³)
-- FRB 20180301 (DM=420.0 pc cm⁻³)
-- FRB 20201124 (DM=525.0 pc cm⁻³)
-
-### SKA/ASKAP (3 samples)
-- FRB 20180119 (DM=400.0 pc cm⁻³)
-- FRB 20180212 (DM=167.7 pc cm⁻³)
-- FRB 20220610A (DM=1457.6 pc cm⁻³)
-
-### Parkes Telescope (1 sample)
-- FRB 110220 (DM=944.0 pc cm⁻³)
-
-See [DATA_DESCRIPTION.md](DATA_DESCRIPTION.md) for detailed information about each test sample.
-
----
-
+| Telescope | FRB | DM (pc cm⁻³) |
+|-----------|-----|--------------|
+| FAST | 20121102, 20180301, 20201124 | 565, 420, 525 |
+| ASKAP | 20180119, 20180212, 20220610A | 400, 168, 1458 |
+| Parkes | 110220 | 944 |
 
 ## Citation
 
-If you use FRTSearch or the CRAFTS-FRT dataset in your research, please cite:
-
 ```bibtex
 @article{zhang2025frtsearch,
-  title={FRTSearch: Unified Detection and Parameter Inference of Fast Radio Transients using Instance Segmentation},
+  title={FRTSearch: Unified Detection and Parameter Inference of Fast Radio Transients},
   author={Zhang, Bin and Wang, Yabiao and Xie, Xiaoyao and others},
-  journal={Draft version (AASTeX631)},
   year={2025}
 }
 ```
@@ -116,26 +76,14 @@ When using the test samples, please also cite the original observations:
 - **Parkes sample**: [Keane et al. (2015)](https://doi.org/10.1093/mnras/stu2650)
 - **SKA samples**: [Shannon et al. (2018)](https://doi.org/10.1038/s41586-018-0588-y), [Ryder et al. (2022)](https://doi.org/10.1126/science.adf2678)
 
----
-
-## License
+## License & Acknowledgments
 
-This project is based on [PRESTO](https://github.com/scottransom/presto) and modified components are licensed under GNU General Public License v2.0.
-
-See [LICENSE](https://github.com/BinZhang109/FRTSearch/blob/main/LICENSE) for details.
+GPL-2.0 | Based on [MMDetection](https://github.com/open-mmlab/mmdetection) & [PRESTO](https://github.com/scottransom/presto)
 
 ---
-
-## Acknowledgments
-
-- **Dataset**: CRAFTS-FRT from FAST telescope observations
-- **Framework**: Built on [MMDetection](https://github.com/open-mmlab/mmdetection)
-- **Base Code**: Modified from [PRESTO](https://github.com/scottransom/presto)
-
----
-
 <div align="center">
-  <sub>Exploring the dynamic universe with AI 🌌📡</sub>
-  <br>
-  <sub>For questions and issues: <a href="https://github.com/BinZhang109/FRTSearch/issues">GitHub Issues</a></sub>
+  <sub>🌌 Questions? <a href="https://github.com/BinZhang109/FRTSearch/issues">GitHub Issues</a></sub>
 </div>
+```
+
+---