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keras-vggface Build Status PyPI Status PyPI Status

Oxford VGGFace Implementation using Keras Functional Framework v2+

  • Models are converted from original caffe networks.
  • It supports only Tensorflow backend.
  • You can also load only feature extraction layers with VGGFace(include_top=False) initiation.
  • When you use it for the first time , weights are downloaded and stored in ~/.keras/models/vggface folder.
  • If you don't know where to start check the blog posts that are using this library.
# Most Recent One (Suggested)
pip install git+https://github.com/rcmalli/keras-vggface.git
# Release Version
pip install keras_vggface

Library Versions

  • Keras v2.2.4
  • Tensorflow v1.14.0
  • Warning: Theano backend is not supported/tested for now.

Example Usage

Available Models

from keras_vggface.vggface import VGGFace

# Based on VGG16 architecture -> old paper(2015)
vggface = VGGFace(model='vgg16') # or VGGFace() as default

# Based on RESNET50 architecture -> new paper(2017)
vggface = VGGFace(model='resnet50')

# Based on SENET50 architecture -> new paper(2017)
vggface = VGGFace(model='senet50')

Feature Extraction

  • Convolution Features

    from keras.engine import  Model
from keras.layers import Input
from keras_vggface.vggface import VGGFace

# Convolution Features
vgg_features = VGGFace(include_top=False, input_shape=(224, 224, 3), pooling='avg') # pooling: None, avg or max

# After this point you can use your model to predict.
# ...

  • Specific Layer Features

    from keras.engine import  Model
from keras.layers import Input
from keras_vggface.vggface import VGGFace

# Layer Features
layer_name = 'layer_name' # edit this line
vgg_model = VGGFace() # pooling: None, avg or max
out = vgg_model.get_layer(layer_name).output
vgg_model_new = Model(vgg_model.input, out)

# After this point you can use your model to predict.
# ...

Finetuning

  • VGG16

    from keras.engine import  Model
from keras.layers import Flatten, Dense, Input
from keras_vggface.vggface import VGGFace

#custom parameters
nb_class = 2
hidden_dim = 512

vgg_model = VGGFace(include_top=False, input_shape=(224, 224, 3))
last_layer = vgg_model.get_layer('pool5').output
x = Flatten(name='flatten')(last_layer)
x = Dense(hidden_dim, activation='relu', name='fc6')(x)
x = Dense(hidden_dim, activation='relu', name='fc7')(x)
out = Dense(nb_class, activation='softmax', name='fc8')(x)
custom_vgg_model = Model(vgg_model.input, out)

# Train your model as usual.
# ...

  • RESNET50 or SENET50

    from keras.engine import  Model
from keras.layers import Flatten, Dense, Input
from keras_vggface.vggface import VGGFace

#custom parameters
nb_class = 2

vgg_model = VGGFace(include_top=False, input_shape=(224, 224, 3))
last_layer = vgg_model.get_layer('avg_pool').output
x = Flatten(name='flatten')(last_layer)
out = Dense(nb_class, activation='softmax', name='classifier')(x)
custom_vgg_model = Model(vgg_model.input, out)

# Train your model as usual.
# ...

Prediction

  • Use utils.preprocess_input(x, version=1) for VGG16

  • Use utils.preprocess_input(x, version=2) for RESNET50 or SENET50

    import numpy as np
from keras.preprocessing import image
from keras_vggface.vggface import VGGFace
from keras_vggface import utils

# tensorflow
model = VGGFace() # default : VGG16 , you can use model='resnet50' or 'senet50'

# Change the image path with yours.
img = image.load_img('../image/ajb.jpg', target_size=(224, 224))
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
x = utils.preprocess_input(x, version=1) # or version=2
preds = model.predict(x)
print('Predicted:', utils.decode_predictions(preds))

References

Licence

  • Check Oxford Webpage for the license of the original models.

  • The code that provided in this project is under MIT License.

Projects / Blog Posts

If you find this project useful, please include reference link in your work. You can create PR's to this document with your project/blog link.