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Convolutional Neural Networks
Very deep convolutional networks
for large-scale image recognition
During 2014, an interesting contribution to image recognition was presented with
the paper, Very Deep Convolutional Networks for Large-Scale Image Recognition, K.
Simonyan and A. Zisserman [4]. The paper showed that a "significant improvement
on the prior-art configurations can be achieved by pushing the depth to 16-19 weight
layers." One model in the paper denoted as D or VGG-16 had 16 deep layers.
An implementation in Java Caffe (http://caffe.berkeleyvision.org/) was used
for training the model on the ImageNet ILSVRC-2012 (http://image-net.org/
challenges/LSVRC/2012/) dataset, which includes images of 1,000 classes, and is
split into three sets: training (1.3 million images), validation (50,000 images), and
testing (100,000 images). Each image is (224×224) on 3 channels. The model achieves
7.5% top-5 error on ILSVRC-2012-val, 7.4% top-5 error on ILSVRC-2012-test.
According to the ImageNet site, "The goal of this competition is to estimate the
content of photographs for the purpose of retrieval and automatic annotation using
a subset of the large hand-labeled ImageNet dataset (10,000,000 labeled images
depicting 10,000+ object categories) as training. Test images will be presented with
no initial annotation – no segmentation or labels – and algorithms will have to
produce labelings specifying what objects are present in the images."
The weights learned by the model implemented in Caffe have been directly
converted (https://gist.github.com/baraldilorenzo/07d7802847aaad0a35d3)
in tf.Keras and can be used by preloading them into the tf.Keras model, which is
implemented as follows, as described in the paper:
import tensorflow as tf
from tensorflow.keras import layers, models
# define a VGG16 network
def VGG_16(weights_path=None):
model = models.Sequential()
model.add(layers.ZeroPadding2D((1,1),input_shape=(224,224, 3)))
model.add(layers.Convolution2D(64, (3, 3), activation='relu'))
model.add(layers.ZeroPadding2D((1,1)))
model.add(layers.Convolution2D(64, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2,2), strides=(2,2)))
model.add(layers.ZeroPadding2D((1,1)))
model.add(layers.Convolution2D(128, (3, 3), activation='relu'))
model.add(layers.ZeroPadding2D((1,1)))
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