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110 lines
3.3 KiB
110 lines
3.3 KiB
# MnasNet
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**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](https://paperswithcode.com/method/inverted-residual-block) (from [MobileNetV2](https://paperswithcode.com/method/mobilenetv2)).
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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{tan2019mnasnet,
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title={MnasNet: Platform-Aware Neural Architecture Search for Mobile},
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author={Mingxing Tan and Bo Chen and Ruoming Pang and Vijay Vasudevan and Mark Sandler and Andrew Howard and Quoc V. Le},
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year={2019},
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eprint={1807.11626},
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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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Type: model-index
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Collections:
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- Name: MNASNet
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Paper:
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Title: 'MnasNet: Platform-Aware Neural Architecture Search for Mobile'
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URL: https://paperswithcode.com/paper/mnasnet-platform-aware-neural-architecture
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Models:
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- Name: mnasnet_100
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In Collection: MNASNet
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Metadata:
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FLOPs: 416415488
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Parameters: 4380000
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File Size: 17731774
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Architecture:
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- 1x1 Convolution
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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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- Global Average Pooling
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- Inverted Residual Block
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- Max Pooling
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- ReLU
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- Residual Connection
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- Softmax
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Tasks:
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- Image Classification
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Training Techniques:
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- RMSProp
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- Weight Decay
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Training Data:
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- ImageNet
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ID: mnasnet_100
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Layers: 100
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Dropout: 0.2
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Crop Pct: '0.875'
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Momentum: 0.9
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Batch Size: 4000
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Image Size: '224'
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Interpolation: bicubic
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RMSProp Decay: 0.9
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Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L894
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Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mnasnet_b1-74cb7081.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: 74.67%
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Top 5 Accuracy: 92.1%
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- Name: semnasnet_100
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In Collection: MNASNet
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Metadata:
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FLOPs: 414570766
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Parameters: 3890000
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File Size: 15731489
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Architecture:
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- 1x1 Convolution
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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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- Global Average Pooling
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- Inverted Residual Block
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- Max Pooling
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- ReLU
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- Residual Connection
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- Softmax
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- Squeeze-and-Excitation Block
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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: semnasnet_100
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Crop Pct: '0.875'
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Image Size: '224'
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Interpolation: bicubic
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Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L928
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Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mnasnet_a1-d9418771.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: 75.45%
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Top 5 Accuracy: 92.61%
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-->
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