You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
2.8 KiB
2.8 KiB
MnasNet
MnasNet is a type of convolutional neural network optimized for mobile devices that is discovered through mobile neural architecture search, which explicitly incorporates model latency into the main objective so that the search can identify a model that achieves a good trade-off between accuracy and latency. The main building block is an inverted residual block (from MobileNetV2).
{% include 'code_snippets.md' %}
How do I train this model?
You can follow the timm recipe scripts for training a new model afresh.
Citation
@misc{tan2019mnasnet,
title={MnasNet: Platform-Aware Neural Architecture Search for Mobile},
author={Mingxing Tan and Bo Chen and Ruoming Pang and Vijay Vasudevan and Mark Sandler and Andrew Howard and Quoc V. Le},
year={2019},
eprint={1807.11626},
archivePrefix={arXiv},
primaryClass={cs.CV}
}