LEMUR Neural Network Dataset: Towards Seamless AutoML
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arXiv
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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866914619860189184 |
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| author | Goodarzi, Arash Torabi Kochnev, Roman Khalid, Waleed Goudarzi, Hojjat Torabi Qin, Furui Uzun, Tolgay Atinc Dhameliya, Yashkumar Sanjaybhai Kathiriya, Yash Kanubhai Bentyn, Zofia Antonina Ignatov, Dmitry Timofte, Radu |
| author_facet | Goodarzi, Arash Torabi Kochnev, Roman Khalid, Waleed Goudarzi, Hojjat Torabi Qin, Furui Uzun, Tolgay Atinc Dhameliya, Yashkumar Sanjaybhai Kathiriya, Yash Kanubhai Bentyn, Zofia Antonina Ignatov, Dmitry Timofte, Radu |
| contents | 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 |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2504_10552 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | LEMUR Neural Network Dataset: Towards Seamless AutoML Goodarzi, Arash Torabi Kochnev, Roman Khalid, Waleed Goudarzi, Hojjat Torabi Qin, Furui Uzun, Tolgay Atinc Dhameliya, Yashkumar Sanjaybhai Kathiriya, Yash Kanubhai Bentyn, Zofia Antonina Ignatov, Dmitry Timofte, Radu Machine Learning Artificial Intelligence Computer Vision and Pattern Recognition Digital Libraries 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 |
| title | LEMUR Neural Network Dataset: Towards Seamless AutoML |
| topic | Machine Learning Artificial Intelligence Computer Vision and Pattern Recognition Digital Libraries |
| url | https://arxiv.org/abs/2504.10552 |