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65 lines
1.9 KiB
65 lines
1.9 KiB
4 years ago
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# Summary
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**Progressive Neural Architecture Search**, or **PNAS**, is a method for learning the structure of convolutional neural networks (CNNs). It uses a sequential model-based optimization (SMBO) strategy, where we search the space of cell structures, starting with simple (shallow) models and progressing to complex ones, pruning out unpromising structures as we go.
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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{liu2018progressive,
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title={Progressive Neural Architecture Search},
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author={Chenxi Liu and Barret Zoph and Maxim Neumann and Jonathon Shlens and Wei Hua and Li-Jia Li and Li Fei-Fei and Alan Yuille and Jonathan Huang and Kevin Murphy},
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year={2018},
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eprint={1712.00559},
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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: pnasnet5large
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Metadata:
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FLOPs: 31458865950
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Batch Size: 1600
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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: 100x NVIDIA P100 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: 345153926
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Tasks:
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- Image Classification
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ID: pnasnet5large
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LR: 0.015
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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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Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/pnasnet.py#L343
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In Collection: PNASNet
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Collections:
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- Name: PNASNet
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Paper:
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title: Progressive Neural Architecture Search
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url: https://papperswithcode.com//paper/progressive-neural-architecture-search
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type: model-index
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Type: model-index
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-->
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