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https://github.com/qubvel/efficientnet

Implementation of EfficientNet model. Keras and TensorFlow Keras.
https://github.com/qubvel/efficientnet

classification deep-learning efficient efficientnet image-classification imagenet mobilenet nasnetmobile pretrained-models

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Implementation of EfficientNet model. Keras and TensorFlow Keras.

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# EfficientNet Keras (and TensorFlow Keras)



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This repository contains a Keras (and TensorFlow Keras) reimplementation of **EfficientNet**, a lightweight convolutional neural network architecture achieving the [state-of-the-art accuracy with an order of magnitude fewer parameters and FLOPS](https://arxiv.org/abs/1905.11946), on both ImageNet and

five other commonly used transfer learning datasets.



The codebase is heavily inspired by the [TensorFlow implementation](https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet).



## Important!

There was a huge library update on **24th of July 2019**. Now efficientnet works with both frameworks: `keras` and `tensorflow.keras`.

If you have models trained before that date, please use efficientnet of version 0.0.4 to load them. You can roll back using `pip install -U efficientnet==0.0.4` or `pip install -U git+https://github.com/qubvel/efficientnet/tree/v0.0.4`.



## Table of Contents



 1. [About EfficientNet Models](#about-efficientnet-models)

 2. [Examples](#examples)

 3. [Models](#models)

 4. [Installation](#installation)

 5. [Frequently Asked Questions](#frequently-asked-questions)

 6. [Acknowledgements](#acknowledgements)



## About EfficientNet Models



EfficientNets rely on AutoML and compound scaling to achieve superior performance without compromising resource efficiency. The [AutoML Mobile framework](https://ai.googleblog.com/2018/08/mnasnet-towards-automating-design-of.html) has helped develop a mobile-size baseline network, **EfficientNet-B0**, which is then improved by the compound scaling method  to obtain EfficientNet-B1 to B7.



    

    

    

    

    

    



EfficientNets achieve state-of-the-art accuracy on ImageNet with an order of magnitude better efficiency:



* In high-accuracy regime, EfficientNet-B7 achieves the state-of-the-art 84.4% top-1 / 97.1% top-5 accuracy on ImageNet with 66M parameters and 37B FLOPS. At the same time, the model is 8.4x smaller and 6.1x faster on CPU inference than the former leader, [Gpipe](https://arxiv.org/abs/1811.06965).



* In middle-accuracy regime, EfficientNet-B1 is 7.6x smaller and 5.7x faster on CPU inference than [ResNet-152](https://arxiv.org/abs/1512.03385), with similar ImageNet accuracy.



* Compared to the widely used [ResNet-50](https://arxiv.org/abs/1512.03385), EfficientNet-B4 improves the top-1 accuracy from 76.3% of ResNet-50 to 82.6% (+6.3%), under similar FLOPS constraints.



## Examples



* *Initializing the model*:



```python

# models can be build with Keras or Tensorflow frameworks

# use keras and tfkeras modules respectively

# efficientnet.keras / efficientnet.tfkeras

import efficientnet.keras as efn 



model = efn.EfficientNetB0(weights='imagenet')  # or weights='noisy-student'



```



* *Loading the pre-trained weights*:



```python

# model use some custom objects, so before loading saved model

# import module your network was build with

# e.g. import efficientnet.keras / import efficientnet.tfkeras

import efficientnet.tfkeras

from tensorflow.keras.models import load_model



model = load_model('path/to/model.h5')

```



See the complete example of loading the model and making an inference in the Jupyter notebook [here](https://github.com/qubvel/efficientnet/blob/master/examples/inference_example.ipynb).



## Models



The performance of each model variant using the pre-trained weights converted from checkpoints provided by the authors is as follows:



| Architecture   | @top1* Imagenet| @top1* Noisy-Student| 

| -------------- | :----: |:---:|

| EfficientNetB0 | 0.772  |0.788|

| EfficientNetB1 | 0.791  |0.815|

| EfficientNetB2 | 0.802  |0.824|

| EfficientNetB3 | 0.816  |0.841|

| EfficientNetB4 | 0.830  |0.853|

| EfficientNetB5 | 0.837  |0.861|

| EfficientNetB6 | 0.841  |0.864|

| EfficientNetB7 | 0.844  |0.869|



**\*** - topK accuracy score for converted models (imagenet `val` set)



## Installation



### Requirements



* `Keras >= 2.2.0` / `TensorFlow >= 1.12.0`

* `keras_applications >= 1.0.7`

* `scikit-image`



### Installing from the source



```bash

$ pip install -U git+https://github.com/qubvel/efficientnet

```



### Installing from PyPI



PyPI stable release



```bash

$ pip install -U efficientnet

```



PyPI latest release (with keras and tf.keras support)



```bash

$ pip install -U --pre efficientnet

```



## Frequently Asked Questions



* **How can I convert the original TensorFlow checkpoints to Keras HDF5?**



Pick the target directory (like `dist`) and run the [converter script](./scripts) from the repo directory as follows:



```bash

$ ./scripts/convert_efficientnet.sh --target_dir dist

```



You can also optionally create the virtual environment with all the dependencies installed by adding `--make_venv=true` and operate in a self-destructing temporary location instead of the target directory by setting `--tmp_working_dir=true`.



## Acknowledgements

I would like to thanks community members who actively contribute to this repository:



1) Sasha Illarionov ([@sdll](https://github.com/sdll)) for preparing automated script for weights conversion

2) Björn Barz ([@Callidior](https://github.com/Callidior)) for model code adaptation for keras and tensorflow.keras frameworks