Metadata-Version: 2.1
Name: efficientnet
Version: 0.0.3
Summary: EfficientNet model re-implementation. Keras.
Home-page: https://github.com/qubvel/efficientnet
Author: Pavel Yakubovskiy
Author-email: qubvel@gmail.com
License: MIT
Description: 
        # EfficientNet-Keras
        
        This repository contains Keras reimplementation of EfficientNet, the new convolutional neural network architecture from [EfficientNet](https://arxiv.org/abs/1905.11946) ([TensorFlow implementation](https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet)). 
        
        ### Table of content
         1. [About EfficientNets](#about)
         2. [Examples](#examples)
         3. [Models](#models) 
         4. [Installation](#installation)
        
        
        ### About EfficientNet Models <a name="about"></a>
        
        If you're new to EfficientNets, here is an explanation straight from the official TensorFlow implementation: 
        
        EfficientNets are a family of image classification models, which achieve state-of-the-art accuracy, yet being an order-of-magnitude smaller and faster than previous models. EfficientNets are based on AutoML and Compound Scaling. In particular, [AutoML Mobile framework](https://ai.googleblog.com/2018/08/mnasnet-towards-automating-design-of.html) have been used to develop a mobile-size baseline network, named as EfficientNet-B0; Then, the compound scaling method is used to scale up this baseline to obtain EfficientNet-B1 to B7.
        
        <table border="0">
        <tr>
            <td>
            <img src="https://raw.githubusercontent.com/tensorflow/tpu/master/models/official/efficientnet/g3doc/params.png" width="100%" />
            </td>
            <td>
            <img src="https://raw.githubusercontent.com/tensorflow/tpu/master/models/official/efficientnet/g3doc/flops.png", width="90%" />
            </td>
        </tr>
        </table>
        
        EfficientNets achieve state-of-the-art accuracy on ImageNet with an order of magnitude better efficiency:
        
        
        * In high-accuracy regime, EfficientNet-B7 achieves state-of-the-art 84.4% top-1 / 97.1% top-5 accuracy on ImageNet with 66M parameters and 37B FLOPS, being 8.4x smaller and 6.1x faster on CPU inference than previous best [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 with 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 constraint.
        
        ### Examples <a name="examples"></a>
        
         - Two lines to create model:
        
        ```python
        from efficientnet import EfficientNetB0
        
        model = EfficientNetB0(weights='imagenet')
        
        ```
        
         - Inference example:  
         [inference_example.ipynb](https://github.com/qubvel/efficientnet/blob/master/examples/inference_exmaple.ipynb)
        
         - Loading saved model:
         
        ```python
        from efficientnet import load_model
        
        model = load_model('path/to/model.h5')
        ```
        
        ### Models <a name="models"></a>
        
        Available architectures and pretrained weights (converted from original repo):
        
        | Architecture   | @top1*| @top5*| Weights |
        |----------------|:-----:|:-----:|:-------:|
        | EfficientNetB0 |0.7668 |0.9312 |    +    |
        | EfficientNetB1 |0.7863 |0.9418 |    +    |
        | EfficientNetB2 |0.7968 |0.9475 |    +    |
        | EfficientNetB3 |0.8083 |0.9531 |    +    |
        | EfficientNetB4 |   -   |  -    |    -    |
        | EfficientNetB5 |   -   |  -    |    -    |
        | EfficientNetB6 |   -   |  -    |    -    |
        | EfficientNetB7 |   -   |  -    |    -    |
        
        "*" - topK accuracy score for converted models (imagenet `val` set) 
         
        Weights for B4-B7 are not released yet ([issue](https://github.com/tensorflow/tpu/issues/377)).
        
        ### Installation <a name="installation"></a>
        
        Requirements:
         - keras >= 2.2.0 (tensorflow)
         - scikit-image
        
        Source:
        
        ```bash
        $ pip install -U git+https://github.com/qubvel/efficientnet
        ```
        
        PyPI:
        
        ```bash
        $ pip install -U efficientnet
        ```
        
Platform: UNKNOWN
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: Implementation :: CPython
Classifier: Programming Language :: Python :: Implementation :: PyPy
Requires-Python: >=3.0.0
Description-Content-Type: text/markdown
Provides-Extra: test
