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diff --git a/python-image-classifiers.spec b/python-image-classifiers.spec new file mode 100644 index 0000000..009af62 --- /dev/null +++ b/python-image-classifiers.spec @@ -0,0 +1,618 @@ +%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 + +[](https://badge.fury.io/py/image-classifiers) [](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 + +[](https://badge.fury.io/py/image-classifiers) [](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 + +[](https://badge.fury.io/py/image-classifiers) [](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 |