A pioneering unified platform designed to systematize and accelerate deep learning research in spectroscopy.

🚀 Quick Start

Environment Setup

We recommend using conda and uv for environment management:

# Clone the repository
git clone https://github.com/little1d/SpectrumLab.git
cd SpectrumLab

# Create conda environment
conda create -n spectrumlab python=3.10
conda activate spectrumlab

pip install uv
uv pip install -e .

Data Setup

Download benchmark data from Hugging Face:

• SpectrumBench v1.0

Extract the data to the data directory in the project root.

API Keys Configuration

# Copy and edit environment configuration
cp .env.example .env
# Configure your API keys in the .env file

💻 Usage

Python API

from spectrumlab.benchmark import get_benchmark_group
from spectrumlab.models import GPT4o
from spectrumlab.evaluator import get_evaluator

# Load benchmark data
benchmark = get_benchmark_group("perception")
data = benchmark.get_data_by_subcategories("all")

# Initialize model
model = GPT4o()

# Get evaluator
evaluator = get_evaluator("perception")

# Run evaluation
results = evaluator.evaluate(
    data_items=data,
    model=model,
    save_path="./results"
)

print(f"Overall accuracy: {results['metrics']['overall']['accuracy']:.2f}%")

Command Line Interface

The CLI provides a simple way to run evaluations:

# Basic evaluation
spectrumlab eval --model gpt4o --level perception

# Specify data path and output directory
spectrumlab eval --model claude --level signal --data-path ./data --output ./my_results

# Evaluate specific subcategories
spectrumlab eval --model deepseek --level semantic --subcategories "IR_spectroscopy" "Raman_spectroscopy"

# Customize output length
spectrumlab eval --model internvl --level generation --max-length 1024

# Get help
spectrumlab eval --help

🤝 Contributing

We welcome community contributions! Please see CONTRIBUTING.md for detailed guidelines.

Citation

If you use SpectrumLab in your research, please cite our paper:

@article{yang2025spectrumworldartificialintelligencefoundation,
      title={SpectrumWorld: Artificial Intelligence Foundation for Spectroscopy}, 
      author={Zhuo Yang and Jiaqing Xie and Shuaike Shen and Daolang Wang and Yeyun Chen and Ben Gao and Shuzhou Sun and Biqing Qi and Dongzhan Zhou and Lei Bai and Linjiang Chen and Shufei Zhang and Qinying Gu and Jun Jiang and Tianfan Fu and Yuqiang Li},
      year={2025},
      eprint={2508.01188},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2508.01188}, 
}

Acknowledgments

• Experiment Tracking: SwanLab for experiment management and visualization
• Choice Evaluator Framework: Inspired by MMAR