VGG-16 pre-trained model for Keras

readme.md

##VGG16 model for Keras

This is the Keras model of the 16-layer network used by the VGG team in the ILSVRC-2014 competition.

It has been obtained by directly converting the Caffe model provived by the authors.

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

Very Deep Convolutional Networks for Large-Scale Image Recognition
K. Simonyan, A. Zisserman
arXiv:1409.1556

In the paper, the VGG-16 model is denoted as configuration D. It achieves 7.5% top-5 error on ILSVRC-2012-val, 7.4% top-5 error on ILSVRC-2012-test.

Please cite the paper if you use the models.

###Contents:

model and usage demo: see vgg-16_keras.py

weights: vgg16_weights.h5

vgg-16_keras.py

from keras.models import Sequential
from keras.layers.core import Flatten, Dense, Dropout
from keras.layers.convolutional import Convolution2D, MaxPooling2D, ZeroPadding2D
from keras.optimizers import SGD
import cv2, numpy as np

def VGG_16(weights_path=None):
    model = Sequential()
    model.add(ZeroPadding2D((1,1),input_shape=(3,224,224)))
    model.add(Convolution2D(64, 3, 3, activation='relu'))
    model.add(ZeroPadding2D((1,1)))
    model.add(Convolution2D(64, 3, 3, activation='relu'))
    model.add(MaxPooling2D((2,2), strides=(2,2)))

    model.add(ZeroPadding2D((1,1)))
    model.add(Convolution2D(128, 3, 3, activation='relu'))
    model.add(ZeroPadding2D((1,1)))
    model.add(Convolution2D(128, 3, 3, activation='relu'))
    model.add(MaxPooling2D((2,2), strides=(2,2)))

    model.add(ZeroPadding2D((1,1)))
    model.add(Convolution2D(256, 3, 3, activation='relu'))
    model.add(ZeroPadding2D((1,1)))
    model.add(Convolution2D(256, 3, 3, activation='relu'))
    model.add(ZeroPadding2D((1,1)))
    model.add(Convolution2D(256, 3, 3, activation='relu'))
    model.add(MaxPooling2D((2,2), strides=(2,2)))

    model.add(ZeroPadding2D((1,1)))
    model.add(Convolution2D(512, 3, 3, activation='relu'))
    model.add(ZeroPadding2D((1,1)))
    model.add(Convolution2D(512, 3, 3, activation='relu'))
    model.add(ZeroPadding2D((1,1)))
    model.add(Convolution2D(512, 3, 3, activation='relu'))
    model.add(MaxPooling2D((2,2), strides=(2,2)))

    model.add(ZeroPadding2D((1,1)))
    model.add(Convolution2D(512, 3, 3, activation='relu'))
    model.add(ZeroPadding2D((1,1)))
    model.add(Convolution2D(512, 3, 3, activation='relu'))
    model.add(ZeroPadding2D((1,1)))
    model.add(Convolution2D(512, 3, 3, activation='relu'))
    model.add(MaxPooling2D((2,2), strides=(2,2)))

    model.add(Flatten())
    model.add(Dense(4096, activation='relu'))
    model.add(Dropout(0.5))
    model.add(Dense(4096, activation='relu'))
    model.add(Dropout(0.5))
    model.add(Dense(1000, activation='softmax'))

    if weights_path:
        model.load_weights(weights_path)

    return model

if __name__ == "__main__":
    im = cv2.resize(cv2.imread('cat.jpg'), (224, 224)).astype(np.float32)
    im[:,:,0] -= 103.939
    im[:,:,1] -= 116.779
    im[:,:,2] -= 123.68
    im = im.transpose((2,0,1))
    im = np.expand_dims(im, axis=0)

    # Test pretrained model
    model = VGG_16('vgg16_weights.h5')
    sgd = SGD(lr=0.1, decay=1e-6, momentum=0.9, nesterov=True)
    model.compile(optimizer=sgd, loss='categorical_crossentropy')
    out = model.predict(im)
    print np.argmax(out)

@mikedewar You can download it at http://dl.caffe.berkeleyvision.org/caffe_ilsvrc12.tar.gz (there are other files too, but one of them has a thousand lines with synset IDs and names, so you can just use that as an index). Works well in my case.
Also, from the out array, you can do numpy.argmax(out) to get the class with the highest probability.

Also, there is no normalization done in the gist above. If you want accurate results, you better do those steps to any input image:

    img = cv2.resize(cv2.imread('../../Downloads/cat2.jpg'), (224, 224))

    mean_pixel = [103.939, 116.779, 123.68]
    img = img.astype(np.float32, copy=False)
    for c in range(3):
        img[:, :, c] = img[:, :, c] - mean_pixel[c]
    img = img.transpose((2,0,1))
    img = np.expand_dims(img, axis=0)

The mean pixel values are taken from the VGG authors, which are the values computed from the training dataset.

is the sgd the exactly optimizer from the paper ? , like the code .. or only the model vgg16 .. i want the exxactly code , in the paper dont mencione the OPTIMIZER

does anyone have a copy of the weights or any working link for download. the link provided seems dead at this moment

qu3ntinprime you can find the weights here: https://github.com/fchollet/deep-learning-models/releases.

I got this error when runing the code vgg16: "return tf.random.uniform ( ResourceExhaustedError: failed to allocate memory [Op:AddV2]". Could someone help me?

Unable to download vgg16.h5 file,Can please solve this issue.

Unable to download vgg16.h5 file,Can please solve this issue.

https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg16_weights_tf_dim_ordering_tf_kernels.h5

这是来自QQ邮箱的假期自动回复邮件。   你好，你的邮件已收到,我会尽快给你回复。

i was able to download

…

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