keras-vggface

Oxford VGGFace Implementation using Keras Functional Framework 1.1

• This model is converted from original caffe network. (580 MB)
• It supports both Tensorflow and Theano backeds.
• You can also load only feature extraction layers with VGGFace(include_top=False) initiation (59MB).
• When you use it for the first time , weights are downloaded and stored in ~/.keras folder.

Library Versions

• Keras v1.1
• Tensorflow v10
• Theano v0.8.2

Example Usage

• Tensorflow backend with 'tf' dimension ordering

from scipy import misc
import copy
import numpy as np
from vggface import VGGFace

model = VGGFace()

im = misc.imread('../image/ak.jpg')
im = misc.imresize(im, (224, 224)).astype(np.float32)
aux = copy.copy(im)
im[:, :, 0] = aux[:, :, 2]
im[:, :, 2] = aux[:, :, 0]
# Remove image mean
im[:, :, 0] -= 93.5940
im[:, :, 1] -= 104.7624
im[:, :, 2] -= 129.1863
im = np.expand_dims(im, axis=0)

res = model.predict(im)
print np.argmax(res[0])

• Theano backend with 'th' dimension ordering

from scipy import misc
import copy
import numpy as np
from vggface import VGGFace

model = VGGFace()

im = misc.imread('../image/ak.jpg')
im = misc.imresize(im, (224, 224)).astype(np.float32)
aux = copy.copy(im)
im[:, :, 0] = aux[:, :, 2]
im[:, :, 2] = aux[:, :, 0]
# Remove image mean
im[:, :, 0] -= 93.5940
im[:, :, 1] -= 104.7624
im[:, :, 2] -= 129.1863
im = np.transpose(im, (2, 0, 1))
im = np.expand_dims(im, axis=0)

res = model.predict(im)
print np.argmax(res[0])

References

• Keras Framework

• Oxford VGGFace Website

• Related Paper

###Licence

Original models can be used for non-commercial research purposes under Creative Commons Attribution License.

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