%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)
[](https://badge.fury.io/py/efficientnet) [](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.
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)
[](https://badge.fury.io/py/efficientnet) [](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.
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)
[](https://badge.fury.io/py/efficientnet) [](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.
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 - 1.1.1-1
- Package Spec generated
