Trained image classification models for Keras

THIS REPOSITORY IS DEPRECATED. USE THE MODULEkeras.applicationsINSTEAD.

Pull requests will not be reviewed nor merged. Direct any PRs tokeras.applications.Issues are not monitored either.

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This repository contains code for the following Keras models:

• VGG16
• VGG19
• ResNet50
• Inception v3
• CRNN for music tagging

All architectures are compatible with both TensorFlow and Theano, and upon instantiation the models will be built according to the image dimension ordering set in your Keras configuration file at~/.keras/keras.json.For instance, if you have setimage_dim_ordering=tf,then any model loaded from this repository will get built according to the TensorFlow dimension ordering convention, "Width-Height-Depth".

Pre-trained weights can be automatically loaded upon instantiation (weights='imagenet'argument in model constructor for all image models,weights='msd'for the music tagging model). Weights are automatically downloaded if necessary, and cached locally in~/.keras/models/.

Examples

Classify images

fromresnet50importResNet50
fromkeras.preprocessingimportimage
fromimagenet_utilsimportpreprocess_input,decode_predictions

model=ResNet50(weights='imagenet')

img_path='elephant.jpg'
img=image.load_img(img_path,target_size=(224,224))
x=image.img_to_array(img)
x=np.expand_dims(x,axis=0)
x=preprocess_input(x)

preds=model.predict(x)
print('Predicted:',decode_predictions(preds))
# print: [[u'n02504458', u'African_elephant']]

Extract features from images

fromvgg16importVGG16
fromkeras.preprocessingimportimage
fromimagenet_utilsimportpreprocess_input

model=VGG16(weights='imagenet',include_top=False)

img_path='elephant.jpg'
img=image.load_img(img_path,target_size=(224,224))
x=image.img_to_array(img)
x=np.expand_dims(x,axis=0)
x=preprocess_input(x)

features=model.predict(x)

Extract features from an arbitrary intermediate layer

fromvgg19importVGG19
fromkeras.preprocessingimportimage
fromimagenet_utilsimportpreprocess_input
fromkeras.modelsimportModel

base_model=VGG19(weights='imagenet')
model=Model(input=base_model.input,output=base_model.get_layer('block4_pool').output)

img_path='elephant.jpg'
img=image.load_img(img_path,target_size=(224,224))
x=image.img_to_array(img)
x=np.expand_dims(x,axis=0)
x=preprocess_input(x)

block4_pool_features=model.predict(x)

References

• Very Deep Convolutional Networks for Large-Scale Image Recognition- please cite this paper if you use the VGG models in your work.
• Deep Residual Learning for Image Recognition- please cite this paper if you use the ResNet model in your work.
• Rethinking the Inception Architecture for Computer Vision- please cite this paper if you use the Inception v3 model in your work.
• Music-auto_tagging-keras

Additionally, don't forget tocite Kerasif you use these models.

License

• All code in this repository is under the MIT license as specified by the LICENSE file.
• The ResNet50 weights are ported from the onesreleased by Kaiming Heunder theMIT license.
• The VGG16 and VGG19 weights are ported from the onesreleased by VGG at Oxfordunder theCreative Commons Attribution License.
• The Inception v3 weights are trained by ourselves and are released under the MIT license.