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VGG-Face model for keras

VGG-Face model for Keras

This is the Keras model of VGG-Face.

It has been obtained through the following method:

  • vgg-face-keras:directly convert the vgg-face matconvnet model to keras model
  • vgg-face-keras-fc:first convert vgg-face caffe model to mxnet model,and then convert it to keras model

Details about the network architecture can be found in the following paper:

Deep Face Recognition
O. M. Parkhi, A. Vedaldi, A. Zisserman
British Machine Vision Conference, 2015

Please cite the paper if you use the models.

Contents:

model and usage demo: see vgg-face-keras.py or vgg-face-keras-fc.py

The only difference between them is the last few layers(see the code and you'll understand),but they produce the same result.

weights: 

Notice:

Please use this model in theano mode.

from keras.models import Model
from keras.layers import Input, Convolution2D, ZeroPadding2D, MaxPooling2D, Flatten, Dense, Dropout
from PIL import Image
import numpy as np
def vgg_face(weights_path=None):
img = Input(shape=(3, 224, 224))
pad1_1 = ZeroPadding2D(padding=(1, 1))(img)
conv1_1 = Convolution2D(64, 3, 3, activation='relu', name='conv1_1')(pad1_1)
pad1_2 = ZeroPadding2D(padding=(1, 1))(conv1_1)
conv1_2 = Convolution2D(64, 3, 3, activation='relu', name='conv1_2')(pad1_2)
pool1 = MaxPooling2D((2, 2), strides=(2, 2))(conv1_2)
pad2_1 = ZeroPadding2D((1, 1))(pool1)
conv2_1 = Convolution2D(128, 3, 3, activation='relu', name='conv2_1')(pad2_1)
pad2_2 = ZeroPadding2D((1, 1))(conv2_1)
conv2_2 = Convolution2D(128, 3, 3, activation='relu', name='conv2_2')(pad2_2)
pool2 = MaxPooling2D((2, 2), strides=(2, 2))(conv2_2)
pad3_1 = ZeroPadding2D((1, 1))(pool2)
conv3_1 = Convolution2D(256, 3, 3, activation='relu', name='conv3_1')(pad3_1)
pad3_2 = ZeroPadding2D((1, 1))(conv3_1)
conv3_2 = Convolution2D(256, 3, 3, activation='relu', name='conv3_2')(pad3_2)
pad3_3 = ZeroPadding2D((1, 1))(conv3_2)
conv3_3 = Convolution2D(256, 3, 3, activation='relu', name='conv3_3')(pad3_3)
pool3 = MaxPooling2D((2, 2), strides=(2, 2))(conv3_3)
pad4_1 = ZeroPadding2D((1, 1))(pool3)
conv4_1 = Convolution2D(512, 3, 3, activation='relu', name='conv4_1')(pad4_1)
pad4_2 = ZeroPadding2D((1, 1))(conv4_1)
conv4_2 = Convolution2D(512, 3, 3, activation='relu', name='conv4_2')(pad4_2)
pad4_3 = ZeroPadding2D((1, 1))(conv4_2)
conv4_3 = Convolution2D(512, 3, 3, activation='relu', name='conv4_3')(pad4_3)
pool4 = MaxPooling2D((2, 2), strides=(2, 2))(conv4_3)
pad5_1 = ZeroPadding2D((1, 1))(pool4)
conv5_1 = Convolution2D(512, 3, 3, activation='relu', name='conv5_1')(pad5_1)
pad5_2 = ZeroPadding2D((1, 1))(conv5_1)
conv5_2 = Convolution2D(512, 3, 3, activation='relu', name='conv5_2')(pad5_2)
pad5_3 = ZeroPadding2D((1, 1))(conv5_2)
conv5_3 = Convolution2D(512, 3, 3, activation='relu', name='conv5_3')(pad5_3)
pool5 = MaxPooling2D((2, 2), strides=(2, 2))(conv5_3)
flat = Flatten()(pool5)
fc6 = Dense(4096, activation='relu', name='fc6')(flat)
fc6_drop = Dropout(0.5)(fc6)
fc7 = Dense(4096, activation='relu', name='fc7')(fc6_drop)
fc7_drop = Dropout(0.5)(fc7)
out = Dense(2622, activation='softmax', name='fc8')(fc7_drop)
model = Model(input=img, output=out)
if weights_path:
model.load_weights(weights_path)
return model
if __name__ == "__main__":
im = Image.open('A.J._Buckley.jpg')
im = im.resize((224,224))
im = np.array(im).astype(np.float32)
# im[:,:,0] -= 129.1863
# im[:,:,1] -= 104.7624
# im[:,:,2] -= 93.5940
im = im.transpose((2,0,1))
im = np.expand_dims(im, axis=0)
# Test pretrained model
model = vgg_face('vgg-face-keras-fc.h5')
out = model.predict(im)
print(out[0][0])
from keras.models import Model
from keras.layers import Input, Convolution2D, ZeroPadding2D, MaxPooling2D, Flatten, Dropout, Activation
from PIL import Image
import numpy as np
def vgg_face(weights_path=None):
img = Input(shape=(3, 224, 224))
pad1_1 = ZeroPadding2D(padding=(1, 1))(img)
conv1_1 = Convolution2D(64, 3, 3, activation='relu', name='conv1_1')(pad1_1)
pad1_2 = ZeroPadding2D(padding=(1, 1))(conv1_1)
conv1_2 = Convolution2D(64, 3, 3, activation='relu', name='conv1_2')(pad1_2)
pool1 = MaxPooling2D((2, 2), strides=(2, 2))(conv1_2)
pad2_1 = ZeroPadding2D((1, 1))(pool1)
conv2_1 = Convolution2D(128, 3, 3, activation='relu', name='conv2_1')(pad2_1)
pad2_2 = ZeroPadding2D((1, 1))(conv2_1)
conv2_2 = Convolution2D(128, 3, 3, activation='relu', name='conv2_2')(pad2_2)
pool2 = MaxPooling2D((2, 2), strides=(2, 2))(conv2_2)
pad3_1 = ZeroPadding2D((1, 1))(pool2)
conv3_1 = Convolution2D(256, 3, 3, activation='relu', name='conv3_1')(pad3_1)
pad3_2 = ZeroPadding2D((1, 1))(conv3_1)
conv3_2 = Convolution2D(256, 3, 3, activation='relu', name='conv3_2')(pad3_2)
pad3_3 = ZeroPadding2D((1, 1))(conv3_2)
conv3_3 = Convolution2D(256, 3, 3, activation='relu', name='conv3_3')(pad3_3)
pool3 = MaxPooling2D((2, 2), strides=(2, 2))(conv3_3)
pad4_1 = ZeroPadding2D((1, 1))(pool3)
conv4_1 = Convolution2D(512, 3, 3, activation='relu', name='conv4_1')(pad4_1)
pad4_2 = ZeroPadding2D((1, 1))(conv4_1)
conv4_2 = Convolution2D(512, 3, 3, activation='relu', name='conv4_2')(pad4_2)
pad4_3 = ZeroPadding2D((1, 1))(conv4_2)
conv4_3 = Convolution2D(512, 3, 3, activation='relu', name='conv4_3')(pad4_3)
pool4 = MaxPooling2D((2, 2), strides=(2, 2))(conv4_3)
pad5_1 = ZeroPadding2D((1, 1))(pool4)
conv5_1 = Convolution2D(512, 3, 3, activation='relu', name='conv5_1')(pad5_1)
pad5_2 = ZeroPadding2D((1, 1))(conv5_1)
conv5_2 = Convolution2D(512, 3, 3, activation='relu', name='conv5_2')(pad5_2)
pad5_3 = ZeroPadding2D((1, 1))(conv5_2)
conv5_3 = Convolution2D(512, 3, 3, activation='relu', name='conv5_3')(pad5_3)
pool5 = MaxPooling2D((2, 2), strides=(2, 2))(conv5_3)
fc6 = Convolution2D(4096, 7, 7, activation='relu', name='fc6')(pool5)
fc6_drop = Dropout(0.5)(fc6)
fc7 = Convolution2D(4096, 1, 1, activation='relu', name='fc7')(fc6_drop)
fc7_drop = Dropout(0.5)(fc7)
fc8 = Convolution2D(2622, 1, 1, name='fc8')(fc7_drop)
flat = Flatten()(fc8)
out = Activation('softmax')(flat)
model = Model(input=img, output=out)
if weights_path:
model.load_weights(weights_path)
return model
if __name__ == "__main__":
im = Image.open('A.J._Buckley.jpg')
im = im.resize((224,224))
im = np.array(im).astype(np.float32)
# im[:,:,0] -= 129.1863
# im[:,:,1] -= 104.7624
# im[:,:,2] -= 93.5940
im = im.transpose((2,0,1))
im = np.expand_dims(im, axis=0)
# Test pretrained model
model = vgg_face('vgg-face-keras.h5')
out = model.predict(im)
print(out[0][0])
@rhlshah
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rhlshah commented Jun 11, 2019

@EncodeTS,
I run it with tf backend but I am getting system error :" Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2".Pls help me out.

@moe-assal
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@rhlshah
don't worry about this problem. any program you run will work as usual. the reason this happening is that you are using a CPU that supports AVX2, which is a set of instructions that your CPU can do. it will accelerate some vector operations. you can install another binary of TensorFlow but i don't recommend that since it's a hard thing to do.

@wayne841213
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@sohaibwaheedgithub
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I am getting this error while using "vgg-face-keras.h5"
Negative dimension size caused by subtracting 2 from 1 for '{{node max_pooling2d_2/MaxPool}} = MaxPoolT=DT_FLOAT, data_format="NCHW", explicit_paddings=[], ksize=[1, 1, 2, 2], padding="VALID", strides=[1, 1, 2, 2]' with input shapes: [?,256,1,1].

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