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NASBench-101

The NASBench-101 dataset is a large-scale benchmark for neural architecture search. It contains nearly 423K unique convolutional neural network architectures with diverse structures and various number of convolutional operations.

Data and Resources

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Cite this as

Kiran Seshadri, Berkin Akin, James Laudon, Ravi Narayanaswami, Amir Yazdanbakhsh (2024). Dataset: NASBench-101. https://doi.org/10.57702/lre1njq3

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Additional Info

Field	Value
Created	December 17, 2024
Last update	December 17, 2024
Defined In	https://doi.org/10.48550/arXiv.2102.10423
Author	Kiran Seshadri
More Authors	Berkin Akin James Laudon Ravi Narayanaswami Amir Yazdanbakhsh
Homepage	https://arxiv.org/abs/1907.04452