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A lightweight end-to-end multi-modality deep learning model for Red Clump stars paramaters estimation.

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SPFusion: A Lightweight Multi-Modality Deep Learning Model for Red Clump Stars Parameters Estimation

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SPFusion Architecture

Environment Preparation

  1. Clone the source code from Github

    git clone https://github.com/qintianjian-lab/SPFusion.git
    cd SPFusion
  2. Create Python environment via Conda

    conda create -n spfusion python=3.9
    conda activate spfusion
  3. Install dependencies

    pip install -r requirements.txt

Configuration

See more configuration in config/config.py

Pretrained Weights Download

Dataset Directory Structure

├── DATASET
│   ├── fold 1
│   │   ├── train
│   │   │   ├── spectrum
│   │   │   │   ├── xxx.npy
│   │   │   │   ├── yyy.npy
│   │   │   │   └── ...
│   │   │   ├── photometric
│   │   │   │   ├── xxx.npy
│   │   │   │   ├── yyy.npy
│   │   │   │   └── ...
│   │   │   └── label
│   │   │       └── label.csv
│   │   ├── val
│   │   │   ├── spectrum
│   │   │   │   ├── xxx.npy
│   │   │   │   ├── yyy.npy
│   │   │   │   └── ...
│   │   │   ├── photometric
│   │   │   │   ├── xxx.npy
│   │   │   │   ├── yyy.npy
│   │   │   │   └── ...
│   │   │   └── label
│   │   │       └── label.csv
│   │   └── test
│   │       ├── spectrum
│   │       │   ├── xxx.npy
│   │       │   ├── yyy.npy
│   │       │   └── ...
│   │       ├── photometric
│   │       │   ├── xxx.npy
│   │       │   ├── yyy.npy
│   │       │   └── ...
│   │       └── label
│   │           └── label.csv
│   ├── fold 2
│   │   ├── ...
│   ├── fold 3
│   │   ├── ...
└── ...

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A lightweight end-to-end multi-modality deep learning model for Red Clump stars paramaters estimation.

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