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506 lines
14 KiB
506 lines
14 KiB
3 years ago
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# (Tensorflow) EfficientNet
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3 years ago
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**EfficientNet** is a convolutional neural network architecture and scaling method that uniformly scales all dimensions of depth/width/resolution using a *compound coefficient*. Unlike conventional practice that arbitrary scales these factors, the EfficientNet scaling method uniformly scales network width, depth, and resolution with a set of fixed scaling coefficients. For example, if we want to use $2^N$ times more computational resources, then we can simply increase the network depth by $\alpha ^ N$, width by $\beta ^ N$, and image size by $\gamma ^ N$, where $\alpha, \beta, \gamma$ are constant coefficients determined by a small grid search on the original small model. EfficientNet uses a compound coefficient $\phi$ to uniformly scales network width, depth, and resolution in a principled way.
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The compound scaling method is justified by the intuition that if the input image is bigger, then the network needs more layers to increase the receptive field and more channels to capture more fine-grained patterns on the bigger image.
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The base EfficientNet-B0 network is based on the inverted bottleneck residual blocks of [MobileNetV2](https://paperswithcode.com/method/mobilenetv2), in addition to [squeeze-and-excitation blocks](https://paperswithcode.com/method/squeeze-and-excitation-block).
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3 years ago
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The weights from this model were ported from [Tensorflow/TPU](https://github.com/tensorflow/tpu).
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3 years ago
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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{tan2020efficientnet,
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title={EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks},
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author={Mingxing Tan and Quoc V. Le},
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year={2020},
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eprint={1905.11946},
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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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Models:
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- Name: tf_efficientnet_b1
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Metadata:
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FLOPs: 883633200
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Epochs: 350
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Batch Size: 2048
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Training Data:
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- ImageNet
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Training Techniques:
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- AutoAugment
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- Label Smoothing
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- RMSProp
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- Stochastic Depth
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- Weight Decay
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Architecture:
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- 1x1 Convolution
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- Average Pooling
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- Batch Normalization
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- Convolution
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- Dense Connections
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- Dropout
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- Inverted Residual Block
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- Squeeze-and-Excitation Block
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- Swish
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File Size: 31512534
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Tasks:
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- Image Classification
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ID: tf_efficientnet_b1
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LR: 0.256
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Crop Pct: '0.882'
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Momentum: 0.9
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Image Size: '240'
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Weight Decay: 1.0e-05
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Interpolation: bicubic
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RMSProp Decay: 0.9
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Label Smoothing: 0.1
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BatchNorm Momentum: 0.99
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Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1251
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In Collection: TF EfficientNet
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- Name: tf_efficientnet_b4
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Metadata:
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FLOPs: 5749638672
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Epochs: 350
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Batch Size: 2048
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Training Data:
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- ImageNet
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Training Techniques:
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- AutoAugment
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- Label Smoothing
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- RMSProp
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- Stochastic Depth
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- Weight Decay
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Training Resources: TPUv3 Cloud TPU
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Architecture:
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- 1x1 Convolution
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- Average Pooling
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- Batch Normalization
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- Convolution
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- Dense Connections
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- Dropout
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- Inverted Residual Block
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- Squeeze-and-Excitation Block
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- Swish
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File Size: 77989689
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Tasks:
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- Image Classification
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Training Time: ''
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ID: tf_efficientnet_b4
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LR: 0.256
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Crop Pct: '0.922'
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Momentum: 0.9
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Image Size: '380'
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Weight Decay: 1.0e-05
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Interpolation: bicubic
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RMSProp Decay: 0.9
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Label Smoothing: 0.1
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BatchNorm Momentum: 0.99
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Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1281
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Config: ''
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In Collection: TF EfficientNet
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- Name: tf_efficientnet_b2
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Metadata:
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FLOPs: 1234321170
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Epochs: 350
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Batch Size: 2048
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Training Data:
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- ImageNet
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Training Techniques:
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- AutoAugment
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- Label Smoothing
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- RMSProp
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- Stochastic Depth
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- Weight Decay
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Architecture:
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- 1x1 Convolution
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- Average Pooling
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- Batch Normalization
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- Convolution
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- Dense Connections
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- Dropout
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- Inverted Residual Block
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- Squeeze-and-Excitation Block
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- Swish
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File Size: 36797929
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Tasks:
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- Image Classification
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ID: tf_efficientnet_b2
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LR: 0.256
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Crop Pct: '0.89'
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Momentum: 0.9
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Image Size: '260'
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Weight Decay: 1.0e-05
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Interpolation: bicubic
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RMSProp Decay: 0.9
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Label Smoothing: 0.1
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BatchNorm Momentum: 0.99
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Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1261
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In Collection: TF EfficientNet
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- Name: tf_efficientnet_b3
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Metadata:
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FLOPs: 2275247568
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Epochs: 350
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Batch Size: 2048
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Training Data:
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- ImageNet
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Training Techniques:
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- AutoAugment
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- Label Smoothing
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- RMSProp
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- Stochastic Depth
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- Weight Decay
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Architecture:
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- 1x1 Convolution
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- Average Pooling
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- Batch Normalization
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- Convolution
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- Dense Connections
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- Dropout
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- Inverted Residual Block
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- Squeeze-and-Excitation Block
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- Swish
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File Size: 49381362
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Tasks:
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- Image Classification
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ID: tf_efficientnet_b3
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LR: 0.256
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Crop Pct: '0.904'
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Momentum: 0.9
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Image Size: '300'
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Weight Decay: 1.0e-05
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Interpolation: bicubic
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RMSProp Decay: 0.9
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Label Smoothing: 0.1
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BatchNorm Momentum: 0.99
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Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1271
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In Collection: TF EfficientNet
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- Name: tf_efficientnet_b0
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Metadata:
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FLOPs: 488688572
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Epochs: 350
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Batch Size: 2048
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|
Training Data:
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||
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- ImageNet
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|
Training Techniques:
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- AutoAugment
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- Label Smoothing
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||
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- RMSProp
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- Stochastic Depth
|
||
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- Weight Decay
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||
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Training Resources: TPUv3 Cloud TPU
|
||
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|
Architecture:
|
||
|
|
- 1x1 Convolution
|
||
|
|
- Average Pooling
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||
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- Batch Normalization
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- Convolution
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- Dense Connections
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- Dropout
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- Inverted Residual Block
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||
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- Squeeze-and-Excitation Block
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||
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- Swish
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||
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|
File Size: 21383997
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||
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|
Tasks:
|
||
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- Image Classification
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|
Training Time: ''
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|
|
ID: tf_efficientnet_b0
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LR: 0.256
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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: bicubic
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|
RMSProp Decay: 0.9
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|
|
Label Smoothing: 0.1
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||
|
|
BatchNorm Momentum: 0.99
|
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|
|
Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1241
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Config: ''
|
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In Collection: TF EfficientNet
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|
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- Name: tf_efficientnet_b5
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||
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|
Metadata:
|
||
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|
FLOPs: 13176501888
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|
Epochs: 350
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||
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|
Batch Size: 2048
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||
|
|
Training Data:
|
||
|
|
- ImageNet
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|
|
Training Techniques:
|
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|
|
- AutoAugment
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|
|
- Label Smoothing
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||
|
|
- RMSProp
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- Stochastic Depth
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||
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- Weight Decay
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||
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|
Architecture:
|
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|
- 1x1 Convolution
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- Average Pooling
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|
- Batch Normalization
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- Convolution
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- Dense Connections
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- Dropout
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- Inverted Residual Block
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- Squeeze-and-Excitation Block
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|
|
- Swish
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|
File Size: 122403150
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Tasks:
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- Image Classification
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ID: tf_efficientnet_b5
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LR: 0.256
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Crop Pct: '0.934'
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Momentum: 0.9
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Image Size: '456'
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Weight Decay: 1.0e-05
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Interpolation: bicubic
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||
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|
RMSProp Decay: 0.9
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|
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Label Smoothing: 0.1
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BatchNorm Momentum: 0.99
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Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1291
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In Collection: TF EfficientNet
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- Name: tf_efficientnet_b6
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Metadata:
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FLOPs: 24180518488
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Epochs: 350
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Batch Size: 2048
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||
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|
Training Data:
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||
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|
- ImageNet
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|
Training Techniques:
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||
|
|
- AutoAugment
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||
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|
- Label Smoothing
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||
|
|
- RMSProp
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||
|
|
- Stochastic Depth
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||
|
|
- Weight Decay
|
||
|
|
Architecture:
|
||
|
|
- 1x1 Convolution
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||
|
|
- Average Pooling
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||
|
|
- Batch Normalization
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||
|
|
- Convolution
|
||
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|
- Dense Connections
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||
|
|
- Dropout
|
||
|
|
- Inverted Residual Block
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||
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|
- Squeeze-and-Excitation Block
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||
|
|
- Swish
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||
|
|
File Size: 173232007
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||
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Tasks:
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|
- Image Classification
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|
ID: tf_efficientnet_b6
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LR: 0.256
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Crop Pct: '0.942'
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Momentum: 0.9
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||
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Image Size: '528'
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|
Weight Decay: 1.0e-05
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|
Interpolation: bicubic
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||
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|
RMSProp Decay: 0.9
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||
|
|
Label Smoothing: 0.1
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||
|
|
BatchNorm Momentum: 0.99
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||
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|
Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1301
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|
In Collection: TF EfficientNet
|
||
|
|
- Name: tf_efficientnet_b7
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||
|
|
Metadata:
|
||
|
|
FLOPs: 48205304880
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||
|
|
Epochs: 350
|
||
|
|
Batch Size: 2048
|
||
|
|
Training Data:
|
||
|
|
- ImageNet
|
||
|
|
Training Techniques:
|
||
|
|
- AutoAugment
|
||
|
|
- Label Smoothing
|
||
|
|
- RMSProp
|
||
|
|
- Stochastic Depth
|
||
|
|
- Weight Decay
|
||
|
|
Architecture:
|
||
|
|
- 1x1 Convolution
|
||
|
|
- Average Pooling
|
||
|
|
- Batch Normalization
|
||
|
|
- Convolution
|
||
|
|
- Dense Connections
|
||
|
|
- Dropout
|
||
|
|
- Inverted Residual Block
|
||
|
|
- Squeeze-and-Excitation Block
|
||
|
|
- Swish
|
||
|
|
File Size: 266850607
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||
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|
Tasks:
|
||
|
|
- Image Classification
|
||
|
|
ID: tf_efficientnet_b7
|
||
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|
LR: 0.256
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||
|
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Crop Pct: '0.949'
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||
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Momentum: 0.9
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||
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|
Image Size: '600'
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||
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|
Weight Decay: 1.0e-05
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||
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|
Interpolation: bicubic
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||
|
|
RMSProp Decay: 0.9
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||
|
|
Label Smoothing: 0.1
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||
|
|
BatchNorm Momentum: 0.99
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||
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|
Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1312
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In Collection: TF EfficientNet
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||
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- Name: tf_efficientnet_b8
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Metadata:
|
||
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|
FLOPs: 80962956270
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||
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Epochs: 350
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||
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|
Batch Size: 2048
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||
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|
Training Data:
|
||
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- ImageNet
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||
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|
Training Techniques:
|
||
|
|
- AutoAugment
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||
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|
- Label Smoothing
|
||
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|
- RMSProp
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||
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- Stochastic Depth
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||
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- Weight Decay
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||
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|
Architecture:
|
||
|
|
- 1x1 Convolution
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||
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- Average Pooling
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||
|
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- Batch Normalization
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||
|
|
- Convolution
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||
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- Dense Connections
|
||
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|
- Dropout
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||
|
|
- Inverted Residual Block
|
||
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- Squeeze-and-Excitation Block
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||
|
|
- Swish
|
||
|
|
File Size: 351379853
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||
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|
Tasks:
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||
|
|
- Image Classification
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||
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|
ID: tf_efficientnet_b8
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||
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LR: 0.256
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||
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Crop Pct: '0.954'
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Momentum: 0.9
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Image Size: '672'
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||
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Weight Decay: 1.0e-05
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||
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Interpolation: bicubic
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||
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RMSProp Decay: 0.9
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||
|
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Label Smoothing: 0.1
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||
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BatchNorm Momentum: 0.99
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||
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Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1323
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In Collection: TF EfficientNet
|
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- Name: tf_efficientnet_el
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||
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Metadata:
|
||
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FLOPs: 9356616096
|
||
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|
Training Data:
|
||
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- ImageNet
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||
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Architecture:
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- 1x1 Convolution
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- Average Pooling
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||
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- Batch Normalization
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||
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- Convolution
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- Dense Connections
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||
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- Dropout
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- Inverted Residual Block
|
||
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- Squeeze-and-Excitation Block
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||
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- Swish
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||
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File Size: 42800271
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Tasks:
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- Image Classification
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ID: tf_efficientnet_el
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||
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Crop Pct: '0.904'
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Image Size: '300'
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Interpolation: bicubic
|
||
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Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1551
|
||
|
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In Collection: TF EfficientNet
|
||
|
|
- Name: tf_efficientnet_em
|
||
|
|
Metadata:
|
||
|
|
FLOPs: 3636607040
|
||
|
|
Training Data:
|
||
|
|
- ImageNet
|
||
|
|
Architecture:
|
||
|
|
- 1x1 Convolution
|
||
|
|
- Average Pooling
|
||
|
|
- Batch Normalization
|
||
|
|
- Convolution
|
||
|
|
- Dense Connections
|
||
|
|
- Dropout
|
||
|
|
- Inverted Residual Block
|
||
|
|
- Squeeze-and-Excitation Block
|
||
|
|
- Swish
|
||
|
|
File Size: 27933644
|
||
|
|
Tasks:
|
||
|
|
- Image Classification
|
||
|
|
ID: tf_efficientnet_em
|
||
|
|
Crop Pct: '0.882'
|
||
|
|
Image Size: '240'
|
||
|
|
Interpolation: bicubic
|
||
|
|
Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1541
|
||
|
|
In Collection: TF EfficientNet
|
||
|
|
- Name: tf_efficientnet_es
|
||
|
|
Metadata:
|
||
|
|
FLOPs: 2057577472
|
||
|
|
Training Data:
|
||
|
|
- ImageNet
|
||
|
|
Architecture:
|
||
|
|
- 1x1 Convolution
|
||
|
|
- Average Pooling
|
||
|
|
- Batch Normalization
|
||
|
|
- Convolution
|
||
|
|
- Dense Connections
|
||
|
|
- Dropout
|
||
|
|
- Inverted Residual Block
|
||
|
|
- Squeeze-and-Excitation Block
|
||
|
|
- Swish
|
||
|
|
File Size: 22008479
|
||
|
|
Tasks:
|
||
|
|
- Image Classification
|
||
|
|
ID: tf_efficientnet_es
|
||
|
|
Crop Pct: '0.875'
|
||
|
|
Image Size: '224'
|
||
|
|
Interpolation: bicubic
|
||
|
|
Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1531
|
||
|
|
In Collection: TF EfficientNet
|
||
|
|
- Name: tf_efficientnet_l2_ns_475
|
||
|
|
Metadata:
|
||
|
|
FLOPs: 217795669644
|
||
|
|
Epochs: 350
|
||
|
|
Batch Size: 2048
|
||
|
|
Training Data:
|
||
|
|
- ImageNet
|
||
|
|
- JFT-300M
|
||
|
|
Training Techniques:
|
||
|
|
- AutoAugment
|
||
|
|
- FixRes
|
||
|
|
- Label Smoothing
|
||
|
|
- Noisy Student
|
||
|
|
- RMSProp
|
||
|
|
- RandAugment
|
||
|
|
- Weight Decay
|
||
|
|
Training Resources: TPUv3 Cloud TPU
|
||
|
|
Architecture:
|
||
|
|
- 1x1 Convolution
|
||
|
|
- Average Pooling
|
||
|
|
- Batch Normalization
|
||
|
|
- Convolution
|
||
|
|
- Dense Connections
|
||
|
|
- Dropout
|
||
|
|
- Inverted Residual Block
|
||
|
|
- Squeeze-and-Excitation Block
|
||
|
|
- Swish
|
||
|
|
File Size: 1925950424
|
||
|
|
Tasks:
|
||
|
|
- Image Classification
|
||
|
|
Training Time: ''
|
||
|
|
ID: tf_efficientnet_l2_ns_475
|
||
|
|
LR: 0.128
|
||
|
|
Dropout: 0.5
|
||
|
|
Crop Pct: '0.936'
|
||
|
|
Momentum: 0.9
|
||
|
|
Image Size: '475'
|
||
|
|
Weight Decay: 1.0e-05
|
||
|
|
Interpolation: bicubic
|
||
|
|
RMSProp Decay: 0.9
|
||
|
|
Label Smoothing: 0.1
|
||
|
|
BatchNorm Momentum: 0.99
|
||
|
|
Stochastic Depth Survival: 0.8
|
||
|
|
Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1509
|
||
|
|
Config: ''
|
||
|
|
In Collection: TF EfficientNet
|
||
|
|
Collections:
|
||
|
|
- Name: TF EfficientNet
|
||
|
|
Paper:
|
||
|
|
title: 'EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks'
|
||
|
3 years ago
|
url: https://paperswithcode.com//paper/efficientnet-rethinking-model-scaling-for
|
||
|
3 years ago
|
type: model-index
|
||
|
|
Type: model-index
|
||
|
|
-->
|