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63 lines
1.8 KiB
63 lines
1.8 KiB
# SPNASNet
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**Single-Path NAS** is a novel differentiable NAS method for designing hardware-efficient ConvNets in less than 4 hours.
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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{stamoulis2019singlepath,
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title={Single-Path NAS: Designing Hardware-Efficient ConvNets in less than 4 Hours},
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author={Dimitrios Stamoulis and Ruizhou Ding and Di Wang and Dimitrios Lymberopoulos and Bodhi Priyantha and Jie Liu and Diana Marculescu},
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year={2019},
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eprint={1904.02877},
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archivePrefix={arXiv},
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primaryClass={cs.LG}
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}
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```
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<!--
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Type: model-index
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Collections:
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- Name: SPNASNet
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Paper:
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Title: 'Single-Path NAS: Designing Hardware-Efficient ConvNets in less than 4
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Hours'
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URL: https://paperswithcode.com/paper/single-path-nas-designing-hardware-efficient
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Models:
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- Name: spnasnet_100
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In Collection: SPNASNet
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Metadata:
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FLOPs: 442385600
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Parameters: 4420000
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File Size: 17902337
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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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Tasks:
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- Image Classification
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Training Data:
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- ImageNet
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ID: spnasnet_100
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Crop Pct: '0.875'
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Image Size: '224'
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Interpolation: bilinear
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Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L995
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Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/spnasnet_100-048bc3f4.pth
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Results:
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- Task: Image Classification
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Dataset: ImageNet
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Metrics:
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Top 1 Accuracy: 74.08%
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Top 5 Accuracy: 91.82%
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-->
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