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file: # (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).
  
  ## How do I use this model on an image?
  To load a pretrained model:
  
  ```python
  import timm
  model = timm.create_model('tf_efficientnet_cc_b0_4e', pretrained=True)
  model.eval()
  ```
  
  To load and preprocess the image:
  ```python 
  import urllib
  from PIL import Image
  from timm.data import resolve_data_config
  from timm.data.transforms_factory import create_transform
  
  config = resolve_data_config({}, model=model)
  transform = create_transform(**config)
  
  url, filename = ("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg")
  urllib.request.urlretrieve(url, filename)
  img = Image.open(filename).convert('RGB')
  tensor = transform(img).unsqueeze(0) # transform and add batch dimension
  ```
  
  To get the model predictions:
  ```python
  import torch
  with torch.no_grad():
      out = model(tensor)
  probabilities = torch.nn.functional.softmax(out[0], dim=0)
  print(probabilities.shape)
  # prints: torch.Size([1000])
  ```
  
  To get the top-5 predictions class names:
  ```python
  # Get imagenet class mappings
  url, filename = ("https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt", "imagenet_classes.txt")
  urllib.request.urlretrieve(url, filename) 
  with open("imagenet_classes.txt", "r") as f:
      categories = [s.strip() for s in f.readlines()]
  
  # Print top categories per image
  top5_prob, top5_catid = torch.topk(probabilities, 5)
  for i in range(top5_prob.size(0)):
      print(categories[top5_catid[i]], top5_prob[i].item())
  # prints class names and probabilities like:
  # [('Samoyed', 0.6425196528434753), ('Pomeranian', 0.04062102362513542), ('keeshond', 0.03186424449086189), ('white wolf', 0.01739676296710968), ('Eskimo dog', 0.011717947199940681)]
  ```
  
  Replace the model name with the variant you want to use, e.g. `tf_efficientnet_cc_b0_4e`. You can find the IDs in the model summaries at the top of this page.
  
  To extract image features with this model, follow the [timm feature extraction examples](https://rwightman.github.io/pytorch-image-models/feature_extraction/), just change the name of the model you want to use.
  
  ## How do I finetune this model?
  You can finetune any of the pre-trained models just by changing the classifier (the last layer).
  ```python
  model = timm.create_model('tf_efficientnet_cc_b0_4e', pretrained=True, num_classes=NUM_FINETUNE_CLASSES)
  ```
  To finetune on your own dataset, you have to write a training loop or adapt [timm's training
  script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py) to use your dataset.
  
  ## 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}
  }
  ```
  
  <!--
  Type: model-index
  Collections:
  - Name: TF EfficientNet CondConv
    Paper:
      Title: 'CondConv: Conditionally Parameterized Convolutions for Efficient Inference'
      URL: https://paperswithcode.com/paper/soft-conditional-computation
  Models:
  - Name: tf_efficientnet_cc_b0_4e
    In Collection: TF EfficientNet CondConv
    Metadata:
      FLOPs: 224153788
      Parameters: 13310000
      File Size: 53490940
      Architecture:
      - 1x1 Convolution
      - Average Pooling
      - Batch Normalization
      - CondConv
      - Convolution
      - Dense Connections
      - Dropout
      - Inverted Residual Block
      - Squeeze-and-Excitation Block
      - Swish
      Tasks:
      - Image Classification
      Training Techniques:
      - AutoAugment
      - Label Smoothing
      - RMSProp
      - Stochastic Depth
      - Weight Decay
      Training Data:
      - ImageNet
      ID: tf_efficientnet_cc_b0_4e
      LR: 0.256
      Epochs: 350
      Crop Pct: '0.875'
      Momentum: 0.9
      Batch Size: 2048
      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
    Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_cc_b0_4e-4362b6b2.pth
    Results:
    - Task: Image Classification
      Dataset: ImageNet
      Metrics:
        Top 1 Accuracy: 77.32%
        Top 5 Accuracy: 93.32%
  - Name: tf_efficientnet_cc_b0_8e
    In Collection: TF EfficientNet CondConv
    Metadata:
      FLOPs: 224158524
      Parameters: 24010000
      File Size: 96287616
      Architecture:
      - 1x1 Convolution
      - Average Pooling
      - Batch Normalization
      - CondConv
      - Convolution
      - Dense Connections
      - Dropout
      - Inverted Residual Block
      - Squeeze-and-Excitation Block
      - Swish
      Tasks:
      - Image Classification
      Training Techniques:
      - AutoAugment
      - Label Smoothing
      - RMSProp
      - Stochastic Depth
      - Weight Decay
      Training Data:
      - ImageNet
      ID: tf_efficientnet_cc_b0_8e
      LR: 0.256
      Epochs: 350
      Crop Pct: '0.875'
      Momentum: 0.9
      Batch Size: 2048
      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
    Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_cc_b0_8e-66184a25.pth
    Results:
    - Task: Image Classification
      Dataset: ImageNet
      Metrics:
        Top 1 Accuracy: 77.91%
        Top 5 Accuracy: 93.65%
  - Name: tf_efficientnet_cc_b1_8e
    In Collection: TF EfficientNet CondConv
    Metadata:
      FLOPs: 370427824
      Parameters: 39720000
      File Size: 159206198
      Architecture:
      - 1x1 Convolution
      - Average Pooling
      - Batch Normalization
      - CondConv
      - Convolution
      - Dense Connections
      - Dropout
      - Inverted Residual Block
      - Squeeze-and-Excitation Block
      - Swish
      Tasks:
      - Image Classification
      Training Techniques:
      - AutoAugment
      - Label Smoothing
      - RMSProp
      - Stochastic Depth
      - Weight Decay
      Training Data:
      - ImageNet
      ID: tf_efficientnet_cc_b1_8e
      LR: 0.256
      Epochs: 350
      Crop Pct: '0.882'
      Momentum: 0.9
      Batch Size: 2048
      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
    Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_cc_b1_8e-f7c79ae1.pth
    Results:
    - Task: Image Classification
      Dataset: ImageNet
      Metrics:
        Top 1 Accuracy: 79.33%
        Top 5 Accuracy: 94.37%
  -->