CiteWeb id: 20140000041

CiteWeb score: 1744

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 improve ment on the prior-art configurations can be achieved by pushing th e depth to 16‐19 weight layers. These findings were the basis of our ImageNet C hallenge 2014 submission, where our team secured the first and the second pl aces in the localisation and classification tracks respectively. We also show th at 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 facilitate further research on the use of deep visual representati ons in computer vision.