VGG-Face model for keras

keras VGG-Face Model.md

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: 

• Google Drive
• Baidu Yun(password:12i0)
• fc-version Google Drive
• fc-version Baidu Yun(password:d318)

Notice:

Please use this model in theano mode.

vgg-face-keras-fc.py

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])
    

vgg-face-keras.py

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])
    

Try this https://github.com/wayne841213/VGGFace/blob/master/vgg-face-keras.py
My result is 0.99910444

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].