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)

Hi,
The input size of images is 2828. However I want to train them via VGG 16 where input size of image should be 244244.
How can I do that? I tried several methods but I am not successful.
I found flow_from_directory() function from keras but it requires path of directory, how I am directly accessing data from load_data().

Could you please tell me how can I achieve this?

F o r t h e l a z y :)

from keras import backend as K
from keras.models import Sequential
from keras.layers.core import Flatten, Dense, Dropout
from keras.layers.convolutional import Conv2D, MaxPooling2D, ZeroPadding2D
from keras.layers.core import Activation
from keras.optimizers import SGD
import cv2, numpy as np

K.set_image_data_format('channels_first')

def VGG_16(weights_path=None):
    model = Sequential()
    
  
    model.add(ZeroPadding2D((1,1),input_shape=(3,224,224)))
    model.add(Conv2D(64, (3, 3),padding='same'))
    model.add(Activation('relu'))  


    model.add(ZeroPadding2D((1,1)))
    model.add(Conv2D(64, (3, 3),padding='same'))
    model.add(Activation('relu'))     
    model.add(MaxPooling2D(pool_size=(2,2), strides=(2,2)))

    model.add(ZeroPadding2D((1,1)))
    model.add(Conv2D(128, (3, 3),padding='same'))
    model.add(Activation('relu')) 

    model.add(ZeroPadding2D((1,1)))
    model.add(Conv2D(128, (3, 3),padding='same'))
    model.add(Activation('relu')) 
    model.add(MaxPooling2D(pool_size=(2,2), strides=(2,2)))

    model.add(ZeroPadding2D((1,1)))
    model.add(Conv2D(256, (3, 3),padding='same'))
    model.add(Activation('relu')) 

    model.add(ZeroPadding2D((1,1)))
    model.add(Conv2D(256, (3, 3),padding='same'))
    model.add(Activation('relu')) 

    model.add(ZeroPadding2D((1,1)))
    model.add(Conv2D(256, (3, 3),padding='same'))
    model.add(Activation('relu')) 
    model.add(MaxPooling2D(pool_size=(2,2), strides=(2,2)))

    model.add(ZeroPadding2D((1,1)))
    model.add(Conv2D(512, (3, 3),padding='same'))
    model.add(Activation('relu')) 

    model.add(ZeroPadding2D((1,1)))
    model.add(Conv2D(512, (3, 3),padding='same'))
    model.add(Activation('relu')) 

    model.add(ZeroPadding2D((1,1)))
    model.add(Conv2D(512, (3, 3),padding='same'))
    model.add(Activation('relu')) 
    model.add(MaxPooling2D(pool_size=(2,2), strides=(2,2)))

    model.add(ZeroPadding2D((1,1)))
    model.add(Conv2D(512, (3, 3),padding='same'))
    model.add(Activation('relu')) 

    model.add(ZeroPadding2D((1,1)))
    model.add(Conv2D(512, (3, 3),padding='same'))
    model.add(Activation('relu')) 

    model.add(ZeroPadding2D((1,1)))
    model.add(Conv2D(512, (3, 3),padding='same'))
    model.add(Activation('relu')) 
    model.add(MaxPooling2D(pool_size=(2,2), strides=(2,2)))
    

    model.add(Flatten())
    model.add(Dense(4096))
    model.add(Activation('relu')) 
    model.add(Dropout(0.5))
    
    model.add(Dense(4096))
    model.add(Activation('relu')) 
    model.add(Dropout(0.5))

    model.add(Dense(1000))
    model.add(Activation('softmax')) 


    if weights_path:
        model.load_weights(weights_path,by_name=True)

    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)
    

    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))

Do you still have the weight file vgg16_weights.h5, I found the original google drive link is broken.

file is in user's trash

@baraldilorenzo (and @Zebreu!) thanks for this!

Is there a way I can use this to actually label images? I can get an index into the class vector (my 'cat.jpg' comes out to class 669) but I've no idea how to actually find the class labels the index refers to...

Tensorflow outputs in alphabetical order so if you have a list of all classes, sort them and find the label at index 669

The above-mentioned topic includes the long historical timeline discussions on the VGG16 model and need time to consume. I am thinking whether the following keras models can be used.

A Concise VGG16 Model
Release Date: Dec 3, 2019
https://github.com/1297rohit/VGG16-In-Keras

Official Keras VGG16 Model by the keras team
Release Data: Mar 29, 2019
https://github.com/keras-team/keras-applications/blob/master/keras_applications/vgg16.py

Can you please provide the weight files the above download link isn't working.

Thanks for your response. While using the weights provided by François Chollet, it can run. In this condition, I use Chollet's code with VGG16 model.

is the SGD the optimizer? I want to write the same to the paper in code, i didnt read the SGD, somebody friends.. i dont understand , this code is the same from the paper , even the optimizer? ..

@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

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i was able to download

…

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the file for the weights doesn't exist

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