VERY DEEP CONVOLUTIONAL NETWORKS FOR LARGE-SCALE IMAGE RECOGNITI...
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arXiv:1409.1556v6 [cs.CV] 10 Apr 2015
Published as a conference paper at ICLR 2015
VERY DEEP CONVOLUTIONAL NETWORKS
FOR LARGE-SCALE IMAGE RECOGNITION
Karen Simonyan
∗
& Andrew Zisserman
+
Visual Geometry Group, Department of Engineering Science, University of Oxford
{karen,az}@robots.ox.ac.uk
ABSTRACT
In this work we investigate the effect of the convolutional network depth on its
accuracy in the large-scale image recognition setting. Our main contribution is
a thorough evaluation of networks of increasing depth using an architecture with
very small (3 × 3) convolution filters, which shows that a significant improvement
on the prior-art configurations can be achieved by pushing the depth to 16–19
weight layers. These findings were the basis of our ImageNet Challenge 2014
submission, where our team secured the first and the second places in the localisa-
tion and classification tracks respectively. We also show that our representations
generalise well to other datasets, where they achieve state-of-the-art results. We
have made our two best-performing ConvNet models publicly available to facili-
tate further research on the use of deep visual representations in computer vision.
1 INTRODUCTION
Convolutional networks (ConvNets) have recently enjoyed a great success in large-scale im-
age and video recognition (Krizhevsky et al., 2012; Zeiler & Fergus, 2013; Sermanet et al., 2014;
Simonyan & Zisserman, 2014) which has become possible due to the large public image reposito-
ries, such as ImageNet (Deng et al., 2009), and high-performancecomputing systems, such as GPUs
or large-scale distributed clusters (Dean et al., 2012). In particular, an important role in the advance
of deep visual recognition architectures has been played by the ImageNet Large-Scale Visual Recog-
nition Challenge (ILSVRC) (Russakovsky et al., 2014), which has served as a testbed for a few
generations of large-scale image classification systems, from high-dimensional shallow feature en-
codings (Perronnin et al., 2010) (the winner of ILSVRC-2011) to deep ConvNets (Krizhevsky et al.,
2012) (the winner of ILSVRC-2012).
With ConvNets becoming more of a commodity in the computer vision field, a number of at-
tempts have been made to improve the original architecture of Krizhevsky et al. (2012) in a
bid to achieve better accuracy. For instance, the best-performing submissions to the ILSVRC-
2013 (Zeiler & Fergus, 2013; Sermanet et al., 2014) utilised smaller receptive window size and
smaller stride of the first convolutional layer. Another line of improvements dealt with training
and testing the networks densely over the whole image and over multiple scales (Sermanet et al.,
2014; Howard, 2014). In this paper, we address another important aspect of ConvNet architecture
design – its depth. To this end, we fix other parameters of the architecture, and steadily increase the
depth of the network by adding more convolutional layers, which is feasible due to the use of very
small (3 × 3) convolution filters in all layers.
As a result, we come up with significantly more accurate ConvNet architectures, which not only
achieve the state-of-the-art accuracy on ILSVRC classification and localisation tasks, but are also
applicable to other image recognition datasets, where they achieve excellent performance even when
used as a part of a relatively simple pipelines (e.g. deep features classified by a linear SVM without
fine-tuning). We have released our two best-performing models
1
to facilitate further research.
The rest of the paper is organised as follows. In Sect. 2, we describe our ConvNet configurations.
The details of the image classification training and evaluation are then presented in Sect. 3, and the
∗
current affiliation: Google DeepMind
+
current affiliation: University of Oxford and Google DeepMind
1
http://www.robots.ox.ac.uk/
˜
vgg/research/very_deep/
1
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