{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/very-deep-convolutional-networks-for-large","title":"Very Deep Convolutional Networks for Large-Scale Image Recognition","arxiv_id":"1409.1556","date":"2014-09-04","proceeding":null,"authors":["Karen Simonyan","Andrew Zisserman"],"abstract":"In this work we investigate the effect of the convolutional network depth on\nits accuracy in the large-scale image recognition setting. Our main\ncontribution is a thorough evaluation of networks of increasing depth using an\narchitecture with very small (3x3) convolution filters, which shows that a\nsignificant improvement on the prior-art configurations can be achieved by\npushing the depth to 16-19 weight layers. These findings were the basis of our\nImageNet Challenge 2014 submission, where our team secured the first and the\nsecond places in the localisation and classification tracks respectively. We\nalso show that our representations generalise well to other datasets, where\nthey achieve state-of-the-art results. We have made our two best-performing\nConvNet models publicly available to facilitate further research on the use of\ndeep visual representations in computer vision.","url_abs":"http://arxiv.org/abs/1409.1556v6","url_pdf":"http://arxiv.org/pdf/1409.1556v6.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 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