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272 lines
7.2 KiB
272 lines
7.2 KiB
# Summary
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**MobileNetV3** is a convolutional neural network that is designed for mobile phone CPUs. The network design includes the use of a [hard swish activation](https://paperswithcode.com/method/hard-swish) and [squeeze-and-excitation](https://paperswithcode.com/method/squeeze-and-excitation-block) modules in the [MBConv blocks](https://paperswithcode.com/method/inverted-residual-block).
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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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@article{DBLP:journals/corr/abs-1905-02244,
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author = {Andrew Howard and
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Mark Sandler and
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Grace Chu and
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Liang{-}Chieh Chen and
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Bo Chen and
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Mingxing Tan and
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Weijun Wang and
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Yukun Zhu and
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Ruoming Pang and
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Vijay Vasudevan and
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Quoc V. Le and
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Hartwig Adam},
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title = {Searching for MobileNetV3},
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journal = {CoRR},
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volume = {abs/1905.02244},
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year = {2019},
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url = {http://arxiv.org/abs/1905.02244},
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archivePrefix = {arXiv},
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eprint = {1905.02244},
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timestamp = {Tue, 12 Jan 2021 15:30:06 +0100},
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biburl = {https://dblp.org/rec/journals/corr/abs-1905-02244.bib},
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bibsource = {dblp computer science bibliography, https://dblp.org}
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}
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```
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<!--
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Models:
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- Name: tf_mobilenetv3_large_075
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Metadata:
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FLOPs: 194323712
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Batch Size: 4096
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Training Data:
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- ImageNet
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Training Techniques:
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- RMSProp
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- Weight Decay
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Training Resources: 4x4 TPU Pod
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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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- Dense Connections
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- Depthwise Separable Convolution
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- Dropout
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- Global Average Pooling
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- Hard Swish
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- Inverted Residual Block
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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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File Size: 16097377
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Tasks:
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- Image Classification
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ID: tf_mobilenetv3_large_075
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LR: 0.1
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Dropout: 0.8
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Crop Pct: '0.875'
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Momentum: 0.9
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Image Size: '224'
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Weight Decay: 1.0e-05
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Interpolation: bilinear
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Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/mobilenetv3.py#L394
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In Collection: TF MobileNet V3
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- Name: tf_mobilenetv3_large_100
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Metadata:
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FLOPs: 274535288
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Batch Size: 4096
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Training Data:
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- ImageNet
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Training Techniques:
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- RMSProp
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- Weight Decay
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Training Resources: 4x4 TPU Pod
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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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- Dense Connections
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- Depthwise Separable Convolution
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- Dropout
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- Global Average Pooling
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- Hard Swish
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- Inverted Residual Block
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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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File Size: 22076649
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Tasks:
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- Image Classification
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ID: tf_mobilenetv3_large_100
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LR: 0.1
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Dropout: 0.8
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Crop Pct: '0.875'
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Momentum: 0.9
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Image Size: '224'
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Weight Decay: 1.0e-05
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Interpolation: bilinear
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Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/mobilenetv3.py#L403
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In Collection: TF MobileNet V3
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- Name: tf_mobilenetv3_large_minimal_100
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Metadata:
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FLOPs: 267216928
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Batch Size: 4096
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Training Data:
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- ImageNet
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Training Techniques:
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- RMSProp
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- Weight Decay
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Training Resources: 4x4 TPU Pod
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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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- Dense Connections
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- Depthwise Separable Convolution
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- Dropout
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- Global Average Pooling
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- Hard Swish
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- Inverted Residual Block
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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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File Size: 15836368
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Tasks:
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- Image Classification
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ID: tf_mobilenetv3_large_minimal_100
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LR: 0.1
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Dropout: 0.8
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Crop Pct: '0.875'
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Momentum: 0.9
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Image Size: '224'
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Weight Decay: 1.0e-05
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Interpolation: bilinear
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Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/mobilenetv3.py#L412
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In Collection: TF MobileNet V3
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- Name: tf_mobilenetv3_small_075
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Metadata:
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FLOPs: 48457664
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Batch Size: 4096
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Training Data:
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- ImageNet
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Training Techniques:
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- RMSProp
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- Weight Decay
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Training Resources: 16x GPUs
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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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- Dense Connections
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- Depthwise Separable Convolution
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- Dropout
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- Global Average Pooling
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- Hard Swish
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- Inverted Residual Block
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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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File Size: 8242701
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Tasks:
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- Image Classification
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ID: tf_mobilenetv3_small_075
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LR: 0.045
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Crop Pct: '0.875'
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Momentum: 0.9
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Image Size: '224'
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Weight Decay: 4.0e-05
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Interpolation: bilinear
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RMSProp Decay: 0.9
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Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/mobilenetv3.py#L421
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In Collection: TF MobileNet V3
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- Name: tf_mobilenetv3_small_100
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Metadata:
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FLOPs: 65450600
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Batch Size: 4096
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Training Data:
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- ImageNet
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Training Techniques:
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- RMSProp
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- Weight Decay
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Training Resources: 16x GPUs
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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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- Dense Connections
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- Depthwise Separable Convolution
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- Dropout
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- Global Average Pooling
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- Hard Swish
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- Inverted Residual Block
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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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File Size: 10256398
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Tasks:
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- Image Classification
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ID: tf_mobilenetv3_small_100
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LR: 0.045
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Crop Pct: '0.875'
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Momentum: 0.9
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Image Size: '224'
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Weight Decay: 4.0e-05
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Interpolation: bilinear
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RMSProp Decay: 0.9
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Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/mobilenetv3.py#L430
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In Collection: TF MobileNet V3
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- Name: tf_mobilenetv3_small_minimal_100
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Metadata:
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FLOPs: 60827936
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Batch Size: 4096
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Training Data:
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- ImageNet
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Training Techniques:
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- RMSProp
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- Weight Decay
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Training Resources: 16x GPUs
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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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- Dense Connections
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- Depthwise Separable Convolution
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- Dropout
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- Global Average Pooling
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- Hard Swish
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- Inverted Residual Block
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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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File Size: 8258083
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Tasks:
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- Image Classification
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ID: tf_mobilenetv3_small_minimal_100
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LR: 0.045
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Crop Pct: '0.875'
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Momentum: 0.9
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Image Size: '224'
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Weight Decay: 4.0e-05
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Interpolation: bilinear
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RMSProp Decay: 0.9
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Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/mobilenetv3.py#L439
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In Collection: TF MobileNet V3
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Collections:
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- Name: TF MobileNet V3
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Paper:
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title: Searching for MobileNetV3
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url: https://papperswithcode.com//paper/searching-for-mobilenetv3
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type: model-index
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Type: model-index
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-->
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