pytorch-image-models/docs/models/.templates/models/tf-efficientnet-condconv.md

# (Tensorflow) EfficientNet CondConv

**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.

This collection of models amends EfficientNet by adding [CondConv](https://paperswithcode.com/method/condconv) convolutions.

The weights from this model were ported from [Tensorflow/TPU](https://github.com/tensorflow/tpu).

{% 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
@article{DBLP:journals/corr/abs-1904-04971,
  author    = {Brandon Yang and
               Gabriel Bender and
               Quoc V. Le and
               Jiquan Ngiam},
  title     = {Soft Conditional Computation},
  journal   = {CoRR},
  volume    = {abs/1904.04971},
  year      = {2019},
  url       = {http://arxiv.org/abs/1904.04971},
  archivePrefix = {arXiv},
  eprint    = {1904.04971},
  timestamp = {Thu, 25 Apr 2019 13:55:01 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/abs-1904-04971.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}
```

<!--
Models:
- Name: tf_efficientnet_cc_b1_8e
  Metadata:
    FLOPs: 370427824
    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
    - CondConv
    - Convolution
    - Dense Connections
    - Dropout
    - Inverted Residual Block
    - Squeeze-and-Excitation Block
    - Swish
    File Size: 159206198
    Tasks:
    - Image Classification
    ID: tf_efficientnet_cc_b1_8e
    LR: 0.256
    Crop Pct: '0.882'
    Momentum: 0.9
    Image Size: '240'
    Weight Decay: 1.0e-05
    Interpolation: bicubic
    RMSProp Decay: 0.9
    Label Smoothing: 0.1
    BatchNorm Momentum: 0.99
  Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1584
  In Collection: TF EfficientNet CondConv
- Name: tf_efficientnet_cc_b0_4e
  Metadata:
    FLOPs: 224153788
    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
    - CondConv
    - Convolution
    - Dense Connections
    - Dropout
    - Inverted Residual Block
    - Squeeze-and-Excitation Block
    - Swish
    File Size: 53490940
    Tasks:
    - Image Classification
    ID: tf_efficientnet_cc_b0_4e
    LR: 0.256
    Crop Pct: '0.875'
    Momentum: 0.9
    Image Size: '224'
    Weight Decay: 1.0e-05
    Interpolation: bicubic
    RMSProp Decay: 0.9
    Label Smoothing: 0.1
    BatchNorm Momentum: 0.99
  Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1561
  In Collection: TF EfficientNet CondConv
- Name: tf_efficientnet_cc_b0_8e
  Metadata:
    FLOPs: 224158524
    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
    - CondConv
    - Convolution
    - Dense Connections
    - Dropout
    - Inverted Residual Block
    - Squeeze-and-Excitation Block
    - Swish
    File Size: 96287616
    Tasks:
    - Image Classification
    ID: tf_efficientnet_cc_b0_8e
    LR: 0.256
    Crop Pct: '0.875'
    Momentum: 0.9
    Image Size: '224'
    Weight Decay: 1.0e-05
    Interpolation: bicubic
    RMSProp Decay: 0.9
    Label Smoothing: 0.1
    BatchNorm Momentum: 0.99
  Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1572
  In Collection: TF EfficientNet CondConv
Collections:
- Name: TF EfficientNet CondConv
  Paper:
    title: 'CondConv: Conditionally Parameterized Convolutions for Efficient Inference'
    url: https://paperswithcode.com//paper/soft-conditional-computation
  type: model-index
Type: model-index
-->