haritsahm/keras-vggface

VGGFace implementation with Keras Framework

keras-vggface

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.

pip install keras_vggface

News

• Models that mentioned in the new paper are added. SENET50 is not working for now.

• Label names are available now (Check the prediction code).

Library Versions

• Keras v2.1.1
• Tensorflow v1.4
• 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

• Keras Framework

• Oxford VGGFace Website

• Related Paper 1

• Related Paper 2

Licence

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

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

Projects

• 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.