pytorch-image-models/modelindex/.templates/models/efficientnet.md

# Summary

**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.

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.

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).

{% include 'code_snippets.md' %}

## How do I train this model?

You can follow the [timm recipe scripts](https://rwightman.github.io/pytorch-image-models/scripts/) for training a new model afresh.

## Citation

```BibTeX
@misc{tan2020efficientnet,
      title={EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks}, 
      author={Mingxing Tan and Quoc V. Le},
      year={2020},
      eprint={1905.11946},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}
```

<!--
Models:
- Name: efficientnet_b2a
  Metadata:
    FLOPs: 1452041554
    Training Data:
    - ImageNet
    Architecture:
    - 1x1 Convolution
    - Average Pooling
    - Batch Normalization
    - Convolution
    - Dense Connections
    - Dropout
    - Inverted Residual Block
    - Squeeze-and-Excitation Block
    - Swish
    File Size: 49369973
    Tasks:
    - Image Classification
    ID: efficientnet_b2a
    Crop Pct: '1.0'
    Image Size: '288'
    Interpolation: bicubic
  Code: https://github.com/rwightman/pytorch-image-models/blob/a7f95818e44b281137503bcf4b3e3e94d8ffa52f/timm/models/efficientnet.py#L1029
  In Collection: EfficientNet
- Name: efficientnet_b3a
  Metadata:
    FLOPs: 2600628304
    Training Data:
    - ImageNet
    Architecture:
    - 1x1 Convolution
    - Average Pooling
    - Batch Normalization
    - Convolution
    - Dense Connections
    - Dropout
    - Inverted Residual Block
    - Squeeze-and-Excitation Block
    - Swish
    File Size: 49369973
    Tasks:
    - Image Classification
    ID: efficientnet_b3a
    Crop Pct: '1.0'
    Image Size: '320'
    Interpolation: bicubic
  Code: https://github.com/rwightman/pytorch-image-models/blob/a7f95818e44b281137503bcf4b3e3e94d8ffa52f/timm/models/efficientnet.py#L1047
  In Collection: EfficientNet
- Name: efficientnet_em
  Metadata:
    FLOPs: 3935516480
    Training Data:
    - ImageNet
    Architecture:
    - 1x1 Convolution
    - Average Pooling
    - Batch Normalization
    - Convolution
    - Dense Connections
    - Dropout
    - Inverted Residual Block
    - Squeeze-and-Excitation Block
    - Swish
    File Size: 27927309
    Tasks:
    - Image Classification
    ID: efficientnet_em
    Crop Pct: '0.882'
    Image Size: '240'
    Interpolation: bicubic
  Code: https://github.com/rwightman/pytorch-image-models/blob/a7f95818e44b281137503bcf4b3e3e94d8ffa52f/timm/models/efficientnet.py#L1118
  In Collection: EfficientNet
- Name: efficientnet_lite0
  Metadata:
    FLOPs: 510605024
    Training Data:
    - ImageNet
    Architecture:
    - 1x1 Convolution
    - Average Pooling
    - Batch Normalization
    - Convolution
    - Dense Connections
    - Dropout
    - Inverted Residual Block
    - Squeeze-and-Excitation Block
    - Swish
    File Size: 18820005
    Tasks:
    - Image Classification
    ID: efficientnet_lite0
    Crop Pct: '0.875'
    Image Size: '224'
    Interpolation: bicubic
  Code: https://github.com/rwightman/pytorch-image-models/blob/a7f95818e44b281137503bcf4b3e3e94d8ffa52f/timm/models/efficientnet.py#L1163
  In Collection: EfficientNet
- Name: efficientnet_es
  Metadata:
    FLOPs: 2317181824
    Training Data:
    - ImageNet
    Architecture:
    - 1x1 Convolution
    - Average Pooling
    - Batch Normalization
    - Convolution
    - Dense Connections
    - Dropout
    - Inverted Residual Block
    - Squeeze-and-Excitation Block
    - Swish
    File Size: 22003339
    Tasks:
    - Image Classification
    ID: efficientnet_es
    Crop Pct: '0.875'
    Image Size: '224'
    Interpolation: bicubic
  Code: https://github.com/rwightman/pytorch-image-models/blob/a7f95818e44b281137503bcf4b3e3e94d8ffa52f/timm/models/efficientnet.py#L1110
  In Collection: EfficientNet
- Name: efficientnet_b3
  Metadata:
    FLOPs: 2327905920
    Training Data:
    - ImageNet
    Architecture:
    - 1x1 Convolution
    - Average Pooling
    - Batch Normalization
    - Convolution
    - Dense Connections
    - Dropout
    - Inverted Residual Block
    - Squeeze-and-Excitation Block
    - Swish
    File Size: 49369973
    Tasks:
    - Image Classification
    ID: efficientnet_b3
    Crop Pct: '0.904'
    Image Size: '300'
    Interpolation: bicubic
  Code: https://github.com/rwightman/pytorch-image-models/blob/a7f95818e44b281137503bcf4b3e3e94d8ffa52f/timm/models/efficientnet.py#L1038
  In Collection: EfficientNet
- Name: efficientnet_b0
  Metadata:
    FLOPs: 511241564
    Training Data:
    - ImageNet
    Architecture:
    - 1x1 Convolution
    - Average Pooling
    - Batch Normalization
    - Convolution
    - Dense Connections
    - Dropout
    - Inverted Residual Block
    - Squeeze-and-Excitation Block
    - Swish
    File Size: 21376743
    Tasks:
    - Image Classification
    ID: efficientnet_b0
    Layers: 18
    Crop Pct: '0.875'
    Image Size: '224'
    Interpolation: bicubic
  Code: https://github.com/rwightman/pytorch-image-models/blob/a7f95818e44b281137503bcf4b3e3e94d8ffa52f/timm/models/efficientnet.py#L1002
  In Collection: EfficientNet
- Name: efficientnet_b1
  Metadata:
    FLOPs: 909691920
    Training Data:
    - ImageNet
    Architecture:
    - 1x1 Convolution
    - Average Pooling
    - Batch Normalization
    - Convolution
    - Dense Connections
    - Dropout
    - Inverted Residual Block
    - Squeeze-and-Excitation Block
    - Swish
    File Size: 31502706
    Tasks:
    - Image Classification
    ID: efficientnet_b1
    Crop Pct: '0.875'
    Image Size: '240'
    Interpolation: bicubic
  Code: https://github.com/rwightman/pytorch-image-models/blob/a7f95818e44b281137503bcf4b3e3e94d8ffa52f/timm/models/efficientnet.py#L1011
  In Collection: EfficientNet
- Name: efficientnet_b2
  Metadata:
    FLOPs: 1265324514
    Training Data:
    - ImageNet
    Architecture:
    - 1x1 Convolution
    - Average Pooling
    - Batch Normalization
    - Convolution
    - Dense Connections
    - Dropout
    - Inverted Residual Block
    - Squeeze-and-Excitation Block
    - Swish
    File Size: 36788104
    Tasks:
    - Image Classification
    ID: efficientnet_b2
    Crop Pct: '0.875'
    Image Size: '260'
    Interpolation: bicubic
  Code: https://github.com/rwightman/pytorch-image-models/blob/a7f95818e44b281137503bcf4b3e3e94d8ffa52f/timm/models/efficientnet.py#L1020
  In Collection: EfficientNet
Collections:
- Name: EfficientNet
  Paper:
    title: 'EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks'
    url: https://papperswithcode.com//paper/efficientnet-rethinking-model-scaling-for
  type: model-index
Type: model-index
-->