diff options
| -rw-r--r-- | .gitignore | 1 | ||||
| -rw-r--r-- | python-efficientnet.spec | 489 | ||||
| -rw-r--r-- | sources | 1 |
3 files changed, 491 insertions, 0 deletions
@@ -0,0 +1 @@ +/efficientnet-1.1.1.tar.gz diff --git a/python-efficientnet.spec b/python-efficientnet.spec new file mode 100644 index 0000000..173224c --- /dev/null +++ b/python-efficientnet.spec @@ -0,0 +1,489 @@ +%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. + +<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) + +[](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. + +<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) + +[](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. + +<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 +* Mon Apr 10 2023 Python_Bot <Python_Bot@openeuler.org> - 1.1.1-1 +- Package Spec generated @@ -0,0 +1 @@ +dbab59272ed84a46ab608e15bb897a97 efficientnet-1.1.1.tar.gz |