构建标准化神经网络数据集，助力自动化机器学习研究
LEMUR Neural Network Dataset: Towards Seamless AutoML
- 统一模板存储PyTorch模型配置与结果，确保可复现
- 集成Optuna实现自动超参优化，支持多任务评估
- 开源框架适配新模型/数据集，降低实验门槛
神经网络是现代人工智能的核心，但其设计、评估与比较仍依赖大量人工。尽管训练数据集众多，但缺乏标准化的模型集合。本文提出LEMUR，一个基于PyTorch的开源神经网络数据集与框架，涵盖分类、分割、检测和自然语言处理等任务。每个模型遵循统一模板，配置与性能结果存入结构化数据库，保障一致性与可复现性。LEMUR集成Optuna实现自动化超参数优化，提供统计分析与可视化工具，并开放API便于性能数据调用。该框架可扩展，支持研究人员无缝添加新模型、数据集或评估指标。通过统一实现与评测标准，LEMUR旨在加速AutoML研究，推动公平基准测试，降低大规模神经网络实验的门槛。相关代码与插件以MIT许可证开源：https://github.com/ABrain-One/nn-dataset https://github.com/ABrain-One/nn-plots https://github.com/ABrain-One/nn-vr
原文摘要 · Abstract (English)
Neural networks are the backbone of modern artificial intelligence, but designing, evaluating, and comparing them remains labor-intensive. While numerous datasets exist for training, there are few standardized collections of the models themselves. We introduce LEMUR, an open-source dataset and framework that provides a large collection of PyTorch-based neural networks across tasks such as classification, segmentation, detection, and natural language processing. Each model follows a unified template, with configurations and results stored in a structured database to ensure consistency and reproducibility. LEMUR integrates automated hyperparameter optimization via Optuna, includes statistical analysis and visualization tools, and offers an API for seamless access to performance data. The framework is extensible, allowing researchers to add new models, datasets, or metrics without breaking compatibility. By standardizing implementations and unifying evaluation, LEMUR aims to accelerate AutoML research, enable fair benchmarking, and reduce barriers to large-scale neural network experimentation. To support adoption and collaboration, LEMUR and its plugins are released under the MIT license at: https://github.com/ABrain-One/nn-dataset https://github.com/ABrain-One/nn-plots https://github.com/ABrain-One/nn-vr
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