automatic import of python-image-classifiers

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+%global _empty_manifest_terminate_build 0
+Name:		python-image-classifiers
+Version:	1.0.0
+Release:	1
+Summary:	Image classification models. Keras.
+License:	MIT
+URL:		https://github.com/qubvel/classification_models
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/83/89/cf76a884d63477fc0e964d3494e65095272af60c48ee72b2c74b96da92c7/image_classifiers-1.0.0.tar.gz
+BuildArch:	noarch
+
+Requires:	python3-keras-applications
+Requires:	python3-scikit-image
+Requires:	python3-pytest
+
+%description
+
+[![PyPI version](https://badge.fury.io/py/image-classifiers.svg)](https://badge.fury.io/py/image-classifiers) [![Build Status](https://travis-ci.com/qubvel/classification_models.svg?branch=master)](https://travis-ci.com/qubvel/classification_models) 
+# Classification models Zoo - Keras (and TensorFlow Keras)
+Trained on [ImageNet](http://www.image-net.org/) classification models. 
+The library is designed to work both with [Keras](https://keras.io/) and [TensorFlow Keras](https://www.tensorflow.org/guide/keras). See example below.
+
+## Important!
+There was a huge library update **05 of August**. Now classification-models works with both frameworks: `keras` and `tensorflow.keras`.
+If you have models, trained before that date, to load them, please, use `image-classifiers` (PyPI package name) of 0.2.2 version. You can roll back using `pip install -U image-classifiers==0.2.2`.
+
+### Architectures: 
+- [VGG](https://arxiv.org/abs/1409.1556) [16, 19]
+- [ResNet](https://arxiv.org/abs/1512.03385) [18, 34, 50, 101, 152]
+- [ResNeXt](https://arxiv.org/abs/1611.05431) [50, 101]
+- [SE-ResNet](https://arxiv.org/abs/1709.01507) [18, 34, 50, 101, 152]
+- [SE-ResNeXt](https://arxiv.org/abs/1709.01507) [50, 101]
+- [SE-Net](https://arxiv.org/abs/1709.01507) [154]
+- [DenseNet](https://arxiv.org/abs/1608.06993) [121, 169, 201]
+- [Inception ResNet V2](https://arxiv.org/abs/1602.07261)
+- [Inception V3](http://arxiv.org/abs/1512.00567)
+- [Xception](https://arxiv.org/abs/1610.02357)
+- [NASNet](https://arxiv.org/abs/1707.07012) [large, mobile]
+- [MobileNet](https://arxiv.org/pdf/1704.04861.pdf)
+- [MobileNet v2](https://arxiv.org/abs/1801.04381)
+
+### Specification 
+The top-k accuracy were obtained using center single crop on the 
+2012 ILSVRC ImageNet validation set and may differ from the original ones. 
+The input size used was 224x224 (min size 256) for all models except:
+ - NASNetLarge 331x331 (352)
+ - InceptionV3 299x299 (324)
+ - InceptionResNetV2 299x299 (324)
+ - Xception 299x299 (324)  
+
+The inference \*Time was evaluated on 500 batches of size 16. 
+All models have been tested using same hardware and software. 
+Time is listed just for comparison of performance.
+
+| Model           |Acc@1|Acc@5|Time*|Source|
+|-----------------|:---:|:---:|:---:|------|
+|vgg16            |70.79|89.74|24.95|[keras](https://github.com/keras-team/keras-applications)|
+|vgg19            |70.89|89.69|24.95|[keras](https://github.com/keras-team/keras-applications)|
+|resnet18         |68.24|88.49|16.07|[mxnet](https://github.com/Microsoft/MMdnn)|
+|resnet34         |72.17|90.74|17.37|[mxnet](https://github.com/Microsoft/MMdnn)|
+|resnet50         |74.81|92.38|22.62|[mxnet](https://github.com/Microsoft/MMdnn)|
+|resnet101        |76.58|93.10|33.03|[mxnet](https://github.com/Microsoft/MMdnn)|
+|resnet152        |76.66|93.08|42.37|[mxnet](https://github.com/Microsoft/MMdnn)|
+|resnet50v2       |69.73|89.31|19.56|[keras](https://github.com/keras-team/keras-applications)|
+|resnet101v2      |71.93|90.41|28.80|[keras](https://github.com/keras-team/keras-applications)|
+|resnet152v2      |72.29|90.61|41.09|[keras](https://github.com/keras-team/keras-applications)|
+|resnext50        |77.36|93.48|37.57|[keras](https://github.com/keras-team/keras-applications)|
+|resnext101       |78.48|94.00|60.07|[keras](https://github.com/keras-team/keras-applications)|
+|densenet121      |74.67|92.04|27.66|[keras](https://github.com/keras-team/keras-applications)|
+|densenet169      |75.85|92.93|33.71|[keras](https://github.com/keras-team/keras-applications)|
+|densenet201      |77.13|93.43|42.40|[keras](https://github.com/keras-team/keras-applications)|
+|inceptionv3      |77.55|93.48|38.94|[keras](https://github.com/keras-team/keras-applications)|
+|xception         |78.87|94.20|42.18|[keras](https://github.com/keras-team/keras-applications)|
+|inceptionresnetv2|80.03|94.89|54.77|[keras](https://github.com/keras-team/keras-applications)|
+|seresnet18       |69.41|88.84|20.19|[pytorch](https://github.com/Cadene/pretrained-models.pytorch)|
+|seresnet34       |72.60|90.91|22.20|[pytorch](https://github.com/Cadene/pretrained-models.pytorch)|
+|seresnet50       |76.44|93.02|23.64|[pytorch](https://github.com/Cadene/pretrained-models.pytorch)|
+|seresnet101      |77.92|94.00|32.55|[pytorch](https://github.com/Cadene/pretrained-models.pytorch)|
+|seresnet152      |78.34|94.08|47.88|[pytorch](https://github.com/Cadene/pretrained-models.pytorch)|
+|seresnext50      |78.74|94.30|38.29|[pytorch](https://github.com/Cadene/pretrained-models.pytorch)|
+|seresnext101     |79.88|94.87|62.80|[pytorch](https://github.com/Cadene/pretrained-models.pytorch)|
+|senet154         |81.06|95.24|137.36|[pytorch](https://github.com/Cadene/pretrained-models.pytorch)|
+|nasnetlarge      |**82.12**|**95.72**|116.53|[keras](https://github.com/keras-team/keras-applications)|
+|nasnetmobile     |74.04|91.54|27.73|[keras](https://github.com/keras-team/keras-applications)|
+|mobilenet        |70.36|89.39|15.50|[keras](https://github.com/keras-team/keras-applications)|
+|mobilenetv2      |71.63|90.35|18.31|[keras](https://github.com/keras-team/keras-applications)|
+
+
+### Weights
+| Name                    |Classes   | Models    |
+|-------------------------|:--------:|:---------:|
+|'imagenet'               |1000      |all models |
+|'imagenet11k-place365ch' |11586     |resnet50   |
+|'imagenet11k'            |11221     |resnet152  |
+
+
+### Installation
+
+Requirements:
+- Keras >= 2.2.0 / TensorFlow >= 1.12
+- keras_applications >= 1.0.7
+
+###### Note
+    This library does not have TensorFlow in a requirements for installation. 
+    Please, choose suitable version (‘cpu’/’gpu’) and install it manually using 
+    official Guide (https://www.tensorflow.org/install/).
+
+PyPI stable package:
+```bash
+$ pip install image-classifiers==0.2.2
+```
+
+PyPI latest package:
+```bash
+$ pip install image-classifiers==1.0.0b1
+```
+
+Latest version:
+```bash
+$ pip install git+https://github.com/qubvel/classification_models.git
+```
+
+### Examples 
+
+##### Loading model with `imagenet` weights:
+
+```python
+# for keras
+from classification_models.keras import Classifiers
+
+# for tensorflow.keras
+# from classification_models.tfkeras import Classifiers
+
+ResNet18, preprocess_input = Classifiers.get('resnet18')
+model = ResNet18((224, 224, 3), weights='imagenet')
+```
+
+This way take one additional line of code, however if you would 
+like to train several models you do not need to import them directly, 
+just access everything through `Classifiers`.
+
+You can get all model names using `Classifiers.models_names()` method.
+
+##### Inference example:
+
+```python
+import numpy as np
+from skimage.io import imread
+from skimage.transform import resize
+from keras.applications.imagenet_utils import decode_predictions
+from classification_models.keras import Classifiers
+
+ResNet18, preprocess_input = Classifiers.get('resnet18')
+
+# read and prepare image
+x = imread('./imgs/tests/seagull.jpg')
+x = resize(x, (224, 224)) * 255    # cast back to 0-255 range
+x = preprocess_input(x)
+x = np.expand_dims(x, 0)
+
+# load model
+model = ResNet18(input_shape=(224,224,3), weights='imagenet', classes=1000)
+
+# processing image
+y = model.predict(x)
+
+# result
+print(decode_predictions(y))
+```
+
+##### Model fine-tuning example:
+```python
+import keras
+from classification_models.keras import Classifiers
+
+ResNet18, preprocess_input = Classifiers.get('resnet18')
+
+# prepare your data
+X = ...
+y = ...
+
+X = preprocess_input(X)
+
+n_classes = 10
+
+# build model
+base_model = ResNet18(input_shape=(224,224,3), weights='imagenet', include_top=False)
+x = keras.layers.GlobalAveragePooling2D()(base_model.output)
+output = keras.layers.Dense(n_classes, activation='softmax')(x)
+model = keras.models.Model(inputs=[base_model.input], outputs=[output])
+
+# train
+model.compile(optimizer='SGD', loss='categorical_crossentropy', metrics=['accuracy'])
+model.fit(X, y)
+```
+
+
+
+
+%package -n python3-image-classifiers
+Summary:	Image classification models. Keras.
+Provides:	python-image-classifiers
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-image-classifiers
+
+[![PyPI version](https://badge.fury.io/py/image-classifiers.svg)](https://badge.fury.io/py/image-classifiers) [![Build Status](https://travis-ci.com/qubvel/classification_models.svg?branch=master)](https://travis-ci.com/qubvel/classification_models) 
+# Classification models Zoo - Keras (and TensorFlow Keras)
+Trained on [ImageNet](http://www.image-net.org/) classification models. 
+The library is designed to work both with [Keras](https://keras.io/) and [TensorFlow Keras](https://www.tensorflow.org/guide/keras). See example below.
+
+## Important!
+There was a huge library update **05 of August**. Now classification-models works with both frameworks: `keras` and `tensorflow.keras`.
+If you have models, trained before that date, to load them, please, use `image-classifiers` (PyPI package name) of 0.2.2 version. You can roll back using `pip install -U image-classifiers==0.2.2`.
+
+### Architectures: 
+- [VGG](https://arxiv.org/abs/1409.1556) [16, 19]
+- [ResNet](https://arxiv.org/abs/1512.03385) [18, 34, 50, 101, 152]
+- [ResNeXt](https://arxiv.org/abs/1611.05431) [50, 101]
+- [SE-ResNet](https://arxiv.org/abs/1709.01507) [18, 34, 50, 101, 152]
+- [SE-ResNeXt](https://arxiv.org/abs/1709.01507) [50, 101]
+- [SE-Net](https://arxiv.org/abs/1709.01507) [154]
+- [DenseNet](https://arxiv.org/abs/1608.06993) [121, 169, 201]
+- [Inception ResNet V2](https://arxiv.org/abs/1602.07261)
+- [Inception V3](http://arxiv.org/abs/1512.00567)
+- [Xception](https://arxiv.org/abs/1610.02357)
+- [NASNet](https://arxiv.org/abs/1707.07012) [large, mobile]
+- [MobileNet](https://arxiv.org/pdf/1704.04861.pdf)
+- [MobileNet v2](https://arxiv.org/abs/1801.04381)
+
+### Specification 
+The top-k accuracy were obtained using center single crop on the 
+2012 ILSVRC ImageNet validation set and may differ from the original ones. 
+The input size used was 224x224 (min size 256) for all models except:
+ - NASNetLarge 331x331 (352)
+ - InceptionV3 299x299 (324)
+ - InceptionResNetV2 299x299 (324)
+ - Xception 299x299 (324)  
+
+The inference \*Time was evaluated on 500 batches of size 16. 
+All models have been tested using same hardware and software. 
+Time is listed just for comparison of performance.
+
+| Model           |Acc@1|Acc@5|Time*|Source|
+|-----------------|:---:|:---:|:---:|------|
+|vgg16            |70.79|89.74|24.95|[keras](https://github.com/keras-team/keras-applications)|
+|vgg19            |70.89|89.69|24.95|[keras](https://github.com/keras-team/keras-applications)|
+|resnet18         |68.24|88.49|16.07|[mxnet](https://github.com/Microsoft/MMdnn)|
+|resnet34         |72.17|90.74|17.37|[mxnet](https://github.com/Microsoft/MMdnn)|
+|resnet50         |74.81|92.38|22.62|[mxnet](https://github.com/Microsoft/MMdnn)|
+|resnet101        |76.58|93.10|33.03|[mxnet](https://github.com/Microsoft/MMdnn)|
+|resnet152        |76.66|93.08|42.37|[mxnet](https://github.com/Microsoft/MMdnn)|
+|resnet50v2       |69.73|89.31|19.56|[keras](https://github.com/keras-team/keras-applications)|
+|resnet101v2      |71.93|90.41|28.80|[keras](https://github.com/keras-team/keras-applications)|
+|resnet152v2      |72.29|90.61|41.09|[keras](https://github.com/keras-team/keras-applications)|
+|resnext50        |77.36|93.48|37.57|[keras](https://github.com/keras-team/keras-applications)|
+|resnext101       |78.48|94.00|60.07|[keras](https://github.com/keras-team/keras-applications)|
+|densenet121      |74.67|92.04|27.66|[keras](https://github.com/keras-team/keras-applications)|
+|densenet169      |75.85|92.93|33.71|[keras](https://github.com/keras-team/keras-applications)|
+|densenet201      |77.13|93.43|42.40|[keras](https://github.com/keras-team/keras-applications)|
+|inceptionv3      |77.55|93.48|38.94|[keras](https://github.com/keras-team/keras-applications)|
+|xception         |78.87|94.20|42.18|[keras](https://github.com/keras-team/keras-applications)|
+|inceptionresnetv2|80.03|94.89|54.77|[keras](https://github.com/keras-team/keras-applications)|
+|seresnet18       |69.41|88.84|20.19|[pytorch](https://github.com/Cadene/pretrained-models.pytorch)|
+|seresnet34       |72.60|90.91|22.20|[pytorch](https://github.com/Cadene/pretrained-models.pytorch)|
+|seresnet50       |76.44|93.02|23.64|[pytorch](https://github.com/Cadene/pretrained-models.pytorch)|
+|seresnet101      |77.92|94.00|32.55|[pytorch](https://github.com/Cadene/pretrained-models.pytorch)|
+|seresnet152      |78.34|94.08|47.88|[pytorch](https://github.com/Cadene/pretrained-models.pytorch)|
+|seresnext50      |78.74|94.30|38.29|[pytorch](https://github.com/Cadene/pretrained-models.pytorch)|
+|seresnext101     |79.88|94.87|62.80|[pytorch](https://github.com/Cadene/pretrained-models.pytorch)|
+|senet154         |81.06|95.24|137.36|[pytorch](https://github.com/Cadene/pretrained-models.pytorch)|
+|nasnetlarge      |**82.12**|**95.72**|116.53|[keras](https://github.com/keras-team/keras-applications)|
+|nasnetmobile     |74.04|91.54|27.73|[keras](https://github.com/keras-team/keras-applications)|
+|mobilenet        |70.36|89.39|15.50|[keras](https://github.com/keras-team/keras-applications)|
+|mobilenetv2      |71.63|90.35|18.31|[keras](https://github.com/keras-team/keras-applications)|
+
+
+### Weights
+| Name                    |Classes   | Models    |
+|-------------------------|:--------:|:---------:|
+|'imagenet'               |1000      |all models |
+|'imagenet11k-place365ch' |11586     |resnet50   |
+|'imagenet11k'            |11221     |resnet152  |
+
+
+### Installation
+
+Requirements:
+- Keras >= 2.2.0 / TensorFlow >= 1.12
+- keras_applications >= 1.0.7
+
+###### Note
+    This library does not have TensorFlow in a requirements for installation. 
+    Please, choose suitable version (‘cpu’/’gpu’) and install it manually using 
+    official Guide (https://www.tensorflow.org/install/).
+
+PyPI stable package:
+```bash
+$ pip install image-classifiers==0.2.2
+```
+
+PyPI latest package:
+```bash
+$ pip install image-classifiers==1.0.0b1
+```
+
+Latest version:
+```bash
+$ pip install git+https://github.com/qubvel/classification_models.git
+```
+
+### Examples 
+
+##### Loading model with `imagenet` weights:
+
+```python
+# for keras
+from classification_models.keras import Classifiers
+
+# for tensorflow.keras
+# from classification_models.tfkeras import Classifiers
+
+ResNet18, preprocess_input = Classifiers.get('resnet18')
+model = ResNet18((224, 224, 3), weights='imagenet')
+```
+
+This way take one additional line of code, however if you would 
+like to train several models you do not need to import them directly, 
+just access everything through `Classifiers`.
+
+You can get all model names using `Classifiers.models_names()` method.
+
+##### Inference example:
+
+```python
+import numpy as np
+from skimage.io import imread
+from skimage.transform import resize
+from keras.applications.imagenet_utils import decode_predictions
+from classification_models.keras import Classifiers
+
+ResNet18, preprocess_input = Classifiers.get('resnet18')
+
+# read and prepare image
+x = imread('./imgs/tests/seagull.jpg')
+x = resize(x, (224, 224)) * 255    # cast back to 0-255 range
+x = preprocess_input(x)
+x = np.expand_dims(x, 0)
+
+# load model
+model = ResNet18(input_shape=(224,224,3), weights='imagenet', classes=1000)
+
+# processing image
+y = model.predict(x)
+
+# result
+print(decode_predictions(y))
+```
+
+##### Model fine-tuning example:
+```python
+import keras
+from classification_models.keras import Classifiers
+
+ResNet18, preprocess_input = Classifiers.get('resnet18')
+
+# prepare your data
+X = ...
+y = ...
+
+X = preprocess_input(X)
+
+n_classes = 10
+
+# build model
+base_model = ResNet18(input_shape=(224,224,3), weights='imagenet', include_top=False)
+x = keras.layers.GlobalAveragePooling2D()(base_model.output)
+output = keras.layers.Dense(n_classes, activation='softmax')(x)
+model = keras.models.Model(inputs=[base_model.input], outputs=[output])
+
+# train
+model.compile(optimizer='SGD', loss='categorical_crossentropy', metrics=['accuracy'])
+model.fit(X, y)
+```
+
+
+
+
+%package help
+Summary:	Development documents and examples for image-classifiers
+Provides:	python3-image-classifiers-doc
+%description help
+
+[![PyPI version](https://badge.fury.io/py/image-classifiers.svg)](https://badge.fury.io/py/image-classifiers) [![Build Status](https://travis-ci.com/qubvel/classification_models.svg?branch=master)](https://travis-ci.com/qubvel/classification_models) 
+# Classification models Zoo - Keras (and TensorFlow Keras)
+Trained on [ImageNet](http://www.image-net.org/) classification models. 
+The library is designed to work both with [Keras](https://keras.io/) and [TensorFlow Keras](https://www.tensorflow.org/guide/keras). See example below.
+
+## Important!
+There was a huge library update **05 of August**. Now classification-models works with both frameworks: `keras` and `tensorflow.keras`.
+If you have models, trained before that date, to load them, please, use `image-classifiers` (PyPI package name) of 0.2.2 version. You can roll back using `pip install -U image-classifiers==0.2.2`.
+
+### Architectures: 
+- [VGG](https://arxiv.org/abs/1409.1556) [16, 19]
+- [ResNet](https://arxiv.org/abs/1512.03385) [18, 34, 50, 101, 152]
+- [ResNeXt](https://arxiv.org/abs/1611.05431) [50, 101]
+- [SE-ResNet](https://arxiv.org/abs/1709.01507) [18, 34, 50, 101, 152]
+- [SE-ResNeXt](https://arxiv.org/abs/1709.01507) [50, 101]
+- [SE-Net](https://arxiv.org/abs/1709.01507) [154]
+- [DenseNet](https://arxiv.org/abs/1608.06993) [121, 169, 201]
+- [Inception ResNet V2](https://arxiv.org/abs/1602.07261)
+- [Inception V3](http://arxiv.org/abs/1512.00567)
+- [Xception](https://arxiv.org/abs/1610.02357)
+- [NASNet](https://arxiv.org/abs/1707.07012) [large, mobile]
+- [MobileNet](https://arxiv.org/pdf/1704.04861.pdf)
+- [MobileNet v2](https://arxiv.org/abs/1801.04381)
+
+### Specification 
+The top-k accuracy were obtained using center single crop on the 
+2012 ILSVRC ImageNet validation set and may differ from the original ones. 
+The input size used was 224x224 (min size 256) for all models except:
+ - NASNetLarge 331x331 (352)
+ - InceptionV3 299x299 (324)
+ - InceptionResNetV2 299x299 (324)
+ - Xception 299x299 (324)  
+
+The inference \*Time was evaluated on 500 batches of size 16. 
+All models have been tested using same hardware and software. 
+Time is listed just for comparison of performance.
+
+| Model           |Acc@1|Acc@5|Time*|Source|
+|-----------------|:---:|:---:|:---:|------|
+|vgg16            |70.79|89.74|24.95|[keras](https://github.com/keras-team/keras-applications)|
+|vgg19            |70.89|89.69|24.95|[keras](https://github.com/keras-team/keras-applications)|
+|resnet18         |68.24|88.49|16.07|[mxnet](https://github.com/Microsoft/MMdnn)|
+|resnet34         |72.17|90.74|17.37|[mxnet](https://github.com/Microsoft/MMdnn)|
+|resnet50         |74.81|92.38|22.62|[mxnet](https://github.com/Microsoft/MMdnn)|
+|resnet101        |76.58|93.10|33.03|[mxnet](https://github.com/Microsoft/MMdnn)|
+|resnet152        |76.66|93.08|42.37|[mxnet](https://github.com/Microsoft/MMdnn)|
+|resnet50v2       |69.73|89.31|19.56|[keras](https://github.com/keras-team/keras-applications)|
+|resnet101v2      |71.93|90.41|28.80|[keras](https://github.com/keras-team/keras-applications)|
+|resnet152v2      |72.29|90.61|41.09|[keras](https://github.com/keras-team/keras-applications)|
+|resnext50        |77.36|93.48|37.57|[keras](https://github.com/keras-team/keras-applications)|
+|resnext101       |78.48|94.00|60.07|[keras](https://github.com/keras-team/keras-applications)|
+|densenet121      |74.67|92.04|27.66|[keras](https://github.com/keras-team/keras-applications)|
+|densenet169      |75.85|92.93|33.71|[keras](https://github.com/keras-team/keras-applications)|
+|densenet201      |77.13|93.43|42.40|[keras](https://github.com/keras-team/keras-applications)|
+|inceptionv3      |77.55|93.48|38.94|[keras](https://github.com/keras-team/keras-applications)|
+|xception         |78.87|94.20|42.18|[keras](https://github.com/keras-team/keras-applications)|
+|inceptionresnetv2|80.03|94.89|54.77|[keras](https://github.com/keras-team/keras-applications)|
+|seresnet18       |69.41|88.84|20.19|[pytorch](https://github.com/Cadene/pretrained-models.pytorch)|
+|seresnet34       |72.60|90.91|22.20|[pytorch](https://github.com/Cadene/pretrained-models.pytorch)|
+|seresnet50       |76.44|93.02|23.64|[pytorch](https://github.com/Cadene/pretrained-models.pytorch)|
+|seresnet101      |77.92|94.00|32.55|[pytorch](https://github.com/Cadene/pretrained-models.pytorch)|
+|seresnet152      |78.34|94.08|47.88|[pytorch](https://github.com/Cadene/pretrained-models.pytorch)|
+|seresnext50      |78.74|94.30|38.29|[pytorch](https://github.com/Cadene/pretrained-models.pytorch)|
+|seresnext101     |79.88|94.87|62.80|[pytorch](https://github.com/Cadene/pretrained-models.pytorch)|
+|senet154         |81.06|95.24|137.36|[pytorch](https://github.com/Cadene/pretrained-models.pytorch)|
+|nasnetlarge      |**82.12**|**95.72**|116.53|[keras](https://github.com/keras-team/keras-applications)|
+|nasnetmobile     |74.04|91.54|27.73|[keras](https://github.com/keras-team/keras-applications)|
+|mobilenet        |70.36|89.39|15.50|[keras](https://github.com/keras-team/keras-applications)|
+|mobilenetv2      |71.63|90.35|18.31|[keras](https://github.com/keras-team/keras-applications)|
+
+
+### Weights
+| Name                    |Classes   | Models    |
+|-------------------------|:--------:|:---------:|
+|'imagenet'               |1000      |all models |
+|'imagenet11k-place365ch' |11586     |resnet50   |
+|'imagenet11k'            |11221     |resnet152  |
+
+
+### Installation
+
+Requirements:
+- Keras >= 2.2.0 / TensorFlow >= 1.12
+- keras_applications >= 1.0.7
+
+###### Note
+    This library does not have TensorFlow in a requirements for installation. 
+    Please, choose suitable version (‘cpu’/’gpu’) and install it manually using 
+    official Guide (https://www.tensorflow.org/install/).
+
+PyPI stable package:
+```bash
+$ pip install image-classifiers==0.2.2
+```
+
+PyPI latest package:
+```bash
+$ pip install image-classifiers==1.0.0b1
+```
+
+Latest version:
+```bash
+$ pip install git+https://github.com/qubvel/classification_models.git
+```
+
+### Examples 
+
+##### Loading model with `imagenet` weights:
+
+```python
+# for keras
+from classification_models.keras import Classifiers
+
+# for tensorflow.keras
+# from classification_models.tfkeras import Classifiers
+
+ResNet18, preprocess_input = Classifiers.get('resnet18')
+model = ResNet18((224, 224, 3), weights='imagenet')
+```
+
+This way take one additional line of code, however if you would 
+like to train several models you do not need to import them directly, 
+just access everything through `Classifiers`.
+
+You can get all model names using `Classifiers.models_names()` method.
+
+##### Inference example:
+
+```python
+import numpy as np
+from skimage.io import imread
+from skimage.transform import resize
+from keras.applications.imagenet_utils import decode_predictions
+from classification_models.keras import Classifiers
+
+ResNet18, preprocess_input = Classifiers.get('resnet18')
+
+# read and prepare image
+x = imread('./imgs/tests/seagull.jpg')
+x = resize(x, (224, 224)) * 255    # cast back to 0-255 range
+x = preprocess_input(x)
+x = np.expand_dims(x, 0)
+
+# load model
+model = ResNet18(input_shape=(224,224,3), weights='imagenet', classes=1000)
+
+# processing image
+y = model.predict(x)
+
+# result
+print(decode_predictions(y))
+```
+
+##### Model fine-tuning example:
+```python
+import keras
+from classification_models.keras import Classifiers
+
+ResNet18, preprocess_input = Classifiers.get('resnet18')
+
+# prepare your data
+X = ...
+y = ...
+
+X = preprocess_input(X)
+
+n_classes = 10
+
+# build model
+base_model = ResNet18(input_shape=(224,224,3), weights='imagenet', include_top=False)
+x = keras.layers.GlobalAveragePooling2D()(base_model.output)
+output = keras.layers.Dense(n_classes, activation='softmax')(x)
+model = keras.models.Model(inputs=[base_model.input], outputs=[output])
+
+# train
+model.compile(optimizer='SGD', loss='categorical_crossentropy', metrics=['accuracy'])
+model.fit(X, y)
+```
+
+
+
+
+%prep
+%autosetup -n image-classifiers-1.0.0
+
+%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-image-classifiers -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Mon Apr 10 2023 Python_Bot <Python_Bot@openeuler.org> - 1.0.0-1
+- Package Spec generated