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# EfficientNet

The EfficientNet model is based on the EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks paper.

## Model builders

The following model builders can be used to instantiate an EfficientNet model, with or without pre-trained weights. All the model builders internally rely on the `torchvision.models.efficientnet.EfficientNet` base class. Please refer to the source code for more details about this class.

`efficientnet_b0`(*[, weights, progress]) | EfficientNet B0 model architecture from the EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks paper.
`efficientnet_b1`(*[, weights, progress]) | EfficientNet B1 model architecture from the EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks paper.
`efficientnet_b2`(*[, weights, progress]) | EfficientNet B2 model architecture from the EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks paper.
`efficientnet_b3`(*[, weights, progress]) | EfficientNet B3 model architecture from the EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks paper.
`efficientnet_b4`(*[, weights, progress]) | EfficientNet B4 model architecture from the EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks paper.
`efficientnet_b5`(*[, weights, progress]) | EfficientNet B5 model architecture from the EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks paper.
`efficientnet_b6`(*[, weights, progress]) | EfficientNet B6 model architecture from the EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks paper.
`efficientnet_b7`(*[, weights, progress]) | EfficientNet B7 model architecture from the EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks paper.

## Docs

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## Tutorials

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## Resources

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