path: root/python-efficientnet.spec

%global _empty_manifest_terminate_build 0
Name:		python-efficientnet
Version:	1.1.1
Release:	1
Summary:	EfficientNet model re-implementation. Keras and TensorFlow Keras.
License:	Apache License 2.0
URL:		https://github.com/qubvel/efficientnet
Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/bc/0f/811c73e9e579361b202b1e8205fff114ee7f9a738489247207c9141266f3/efficientnet-1.1.1.tar.gz
BuildArch:	noarch

Requires:	python3-keras-applications
Requires:	python3-scikit-image
Requires:	python3-pytest

%description

# EfficientNet Keras (and TensorFlow Keras)

[![PyPI version](https://badge.fury.io/py/efficientnet.svg)](https://badge.fury.io/py/efficientnet) [![Downloads](https://pepy.tech/badge/efficientnet/month)](https://pepy.tech/project/efficientnet/month)

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 **24 of July 2019**. Now efficintnet works with both frameworks: `keras` and `tensorflow.keras`.
If you have models, trained before that date, to load them, please, use efficientnet of 0.0.4 version (PyPI). You can roll back using `pip install -U efficientnet==0.0.4`.

## 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.

<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 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 




%package -n python3-efficientnet
Summary:	EfficientNet model re-implementation. Keras and TensorFlow Keras.
Provides:	python-efficientnet
BuildRequires:	python3-devel
BuildRequires:	python3-setuptools
BuildRequires:	python3-pip
%description -n python3-efficientnet

# EfficientNet Keras (and TensorFlow Keras)

[![PyPI version](https://badge.fury.io/py/efficientnet.svg)](https://badge.fury.io/py/efficientnet) [![Downloads](https://pepy.tech/badge/efficientnet/month)](https://pepy.tech/project/efficientnet/month)

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 **24 of July 2019**. Now efficintnet works with both frameworks: `keras` and `tensorflow.keras`.
If you have models, trained before that date, to load them, please, use efficientnet of 0.0.4 version (PyPI). You can roll back using `pip install -U efficientnet==0.0.4`.

## 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.

<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 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 




%package help
Summary:	Development documents and examples for efficientnet
Provides:	python3-efficientnet-doc
%description help

# EfficientNet Keras (and TensorFlow Keras)

[![PyPI version](https://badge.fury.io/py/efficientnet.svg)](https://badge.fury.io/py/efficientnet) [![Downloads](https://pepy.tech/badge/efficientnet/month)](https://pepy.tech/project/efficientnet/month)

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 **24 of July 2019**. Now efficintnet works with both frameworks: `keras` and `tensorflow.keras`.
If you have models, trained before that date, to load them, please, use efficientnet of 0.0.4 version (PyPI). You can roll back using `pip install -U efficientnet==0.0.4`.

## 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.

<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 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 




%prep
%autosetup -n efficientnet-1.1.1

%build
%py3_build

%install
%py3_install
install -d -m755 %{buildroot}/%{_pkgdocdir}
if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
pushd %{buildroot}
if [ -d usr/lib ]; then
	find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/lib64 ]; then
	find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/bin ]; then
	find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/sbin ]; then
	find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
fi
touch doclist.lst
if [ -d usr/share/man ]; then
	find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
fi
popd
mv %{buildroot}/filelist.lst .
mv %{buildroot}/doclist.lst .

%files -n python3-efficientnet -f filelist.lst
%dir %{python3_sitelib}/*

%files help -f doclist.lst
%{_docdir}/*

%changelog
* Sun Apr 23 2023 Python_Bot <Python_Bot@openeuler.org> - 1.1.1-1
- Package Spec generated