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66 lines
1.7 KiB
66 lines
1.7 KiB
3 years ago
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# NASNet
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3 years ago
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**NASNet** is a type of convolutional neural network discovered through neural architecture search. The building blocks consist of normal and reduction cells.
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{% include 'code_snippets.md' %}
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## How do I train this model?
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You can follow the [timm recipe scripts](https://rwightman.github.io/pytorch-image-models/scripts/) for training a new model afresh.
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## Citation
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```BibTeX
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@misc{zoph2018learning,
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title={Learning Transferable Architectures for Scalable Image Recognition},
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author={Barret Zoph and Vijay Vasudevan and Jonathon Shlens and Quoc V. Le},
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year={2018},
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eprint={1707.07012},
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archivePrefix={arXiv},
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primaryClass={cs.CV}
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}
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```
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<!--
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Models:
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- Name: nasnetalarge
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Metadata:
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FLOPs: 30242402862
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Training Data:
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- ImageNet
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Training Techniques:
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- Label Smoothing
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- RMSProp
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- Weight Decay
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Training Resources: 50x Tesla K40 GPUs
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Architecture:
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- Average Pooling
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- Batch Normalization
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- Convolution
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- Depthwise Separable Convolution
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- Dropout
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- ReLU
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File Size: 356056626
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Tasks:
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- Image Classification
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Training Time: ''
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ID: nasnetalarge
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Dropout: 0.5
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Crop Pct: '0.911'
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Momentum: 0.9
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Image Size: '331'
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Interpolation: bicubic
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Label Smoothing: 0.1
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RMSProp $\epsilon$: 1.0
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Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/nasnet.py#L562
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Config: ''
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In Collection: NASNet
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Collections:
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- Name: NASNet
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Paper:
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title: Learning Transferable Architectures for Scalable Image Recognition
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3 years ago
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url: https://paperswithcode.com//paper/learning-transferable-architectures-for
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3 years ago
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type: model-index
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Type: model-index
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-->
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