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MnasNet: Platform-Aware Neural Architecture Search for Mobile

Designing convolutional neural networks (CNN) for mobile devices is challenging because mobile models need to be small and fast, yet still accurate. Although significant efforts have been dedicated to design and improve mobile CNNs on all dimensions, it is very difficult to manually balance these trade-offs when there are so many architectural possibilities to consider.

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Cite this as

Mingxing Tan, Bo Chen, Ruoming Pang, Vijay Vasudevan, Mark Sandler, Andrew Howard, Quoc V. Le (2024). Dataset: MnasNet: Platform-Aware Neural Architecture Search for Mobile. https://doi.org/10.57702/mhfa6deb

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Additional Info

Field	Value
Created	December 3, 2024
Last update	December 3, 2024
Author	Mingxing Tan
More Authors	Bo Chen Ruoming Pang Vijay Vasudevan Mark Sandler Andrew Howard Quoc V. Le
Homepage	https://github.com/tensorflow/tpu/tree/master/models/official/mnasnet