--- title: "Very Deep Convolutional Networks for — Simonyan 2014" description: "The paper looks at one question in convolutional network design: how much does depth alone help on large-scale image classification? Simonyan and…" authors: ["Karen Simonyan", "Andrew Zisserman"] year: 2014 venue: "arXiv:1409.1556" source: "https://arxiv.org/abs/1409.1556" doi: "10.48550/arxiv.1409.1556" arxiv: "1409.1556" licence: "arXiv — non-exclusive licence to distribute" licence_url: "http://arxiv.org/licenses/nonexclusive-distrib/1.0/" summary_licence: "Elamur Cosmic License" canonical: "https://research.elamur.ai/free/en/papers/simonyan-2014-very-deep-convolutional-networks-large/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/simonyan-2014-very-deep-convolutional-networks-large/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2014](https://md.research.elamur.ai/free/en/papers/2014/page/2/) / Very Deep Convolutional Networks for Large-Scale Image Recognition # Very Deep Convolutional Networks for Large-Scale Image Recognition **Authors:** [Karen Simonyan](https://md.research.elamur.ai/free/en/authors/karen-simonyan/), [Andrew Zisserman](https://md.research.elamur.ai/free/en/authors/andrew-zisserman/) · **Year:** 2014 · **Venue:** arXiv:1409.1556 · **Licence:** [arXiv — non-exclusive licence to distribute](http://arxiv.org/licenses/nonexclusive-distrib/1.0/) **Links:** [Publisher's page](https://arxiv.org/abs/1409.1556) · [PDF](https://arxiv.org/pdf/1409.1556) · [DOI 10.48550/arxiv.1409.1556](https://doi.org/10.48550/arxiv.1409.1556) · [arXiv 1409.1556](https://arxiv.org/abs/1409.1556) · [OpenAlex](https://openalex.org/W1686810756) **Citations:** 71408 citations (openalex-snapshot · 2026-10-14) · [1395](#cited-by) in our library, of the 140884 papers whose bibliographies we read ## Summary The paper looks at one question in convolutional network design: how much does depth alone help on large-scale image classification? Simonyan and Zisserman keep the rest of the design fixed. Every convolution uses a 3×3 filter with stride 1, there are five max-pooling stages, and three fully connected layers sit on top. They then train five configurations (A–E) with 11 to 19 weight layers on ImageNet (ILSVRC). Accuracy goes up steadily as depth increases, and the 16- and 19-layer networks clearly beat earlier, shallower designs. A stack of small filters covers the same receptive field as one large filter but uses fewer parameters and adds more non-linearities. This work was the basis of the team's ILSVRC-2014 entry, which won the localisation track and came second in classification. The authors also took features from the trained networks and fed them to simple linear classifiers on other benchmarks, where they set new state-of-the-art results. They released the two best models publicly. These models, now known as VGG-16 and VGG-19, became standard backbones for detection, segmentation and captioning. ## Key points - Configurations range from net A (8 conv + 3 FC layers) to net E (16 conv + 3 FC). Channel width grows from 64 to 512, and the parameter count barely changes, from 133M to 144M. - Three stacked 3×3 layers cover a 7×7 receptive field with 27C² weights. A single 7×7 layer would need 49C². Local Response Normalisation (LRN) gave no accuracy gain. - Training used SGD with momentum (batch 256), weight decay and dropout, and stopped after about 370K iterations (74 epochs). The deeper nets were initialised from the layers of net A. - ILSVRC-2014: 1st place in localisation and 2nd in classification. The paper says a single VGG network beats a single GoogLeNet network on classification. - Transfer results: more than 6% above the previous best on PASCAL VOC among ILSVRC-pretrained representations, 8.6% above the prior best on Caltech-256, and 84.0 mAP on VOC-2012 action classification. *Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.* Fields: [Other](https://md.research.elamur.ai/free/en/shelves/other/) ## Cited by Cited by 1395 papers of our library cite it (of the 140884 whose bibliographies we read); 52 of them have a page here, 1343 not yet. - [Guided Open Vocabulary Image Captioning with Constrained Beam Search](https://md.research.elamur.ai/free/en/papers/anderson-2017-guided-open-vocabulary-image-captioning/) - [Neural Module Networks](https://md.research.elamur.ai/free/en/papers/andreas-2015-neural-module-networks/) - [Learning to Compose Neural Networks for Question Answering](https://md.research.elamur.ai/free/en/papers/andreas-2016-learning-compose-neural-networks-question/) - [Layer Normalization](https://md.research.elamur.ai/free/en/papers/ba-2016-layer-normalization/) - [On the Opportunities and Risks of Foundation Models](https://md.research.elamur.ai/free/en/papers/bommasani-2021-opportunities-risks-foundation-models/) - [A Simple Framework for Contrastive Learning of Visual Representations](https://md.research.elamur.ai/free/en/papers/chen-2020-simple-framework-contrastive-learning-visual/) - 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[Learning Transferable Architectures for Scalable Image Recognition](https://md.research.elamur.ai/free/en/papers/zoph-2017-learning-transferable-architectures-scalable-image/) ## Cite The paper itself, not this page. BibTeX from the library's record of the work. ```bibtex @misc{simonyan2014very, title = {{Very Deep Convolutional Networks for Large-Scale Image Recognition}}, author = {Simonyan, Karen and Zisserman, Andrew}, year = {2014}, doi = {10.48550/arxiv.1409.1556}, eprint = {1409.1556}, archivePrefix = {arXiv}, url = {https://doi.org/10.48550/arxiv.1409.1556} } ``` - [← previous Verbal Valency in Biblical Hebrew: An Analysis of the Valency of ʿbr](https://md.research.elamur.ai/free/en/papers/hbo-2014-wilson-verbal-valency-biblical-hebrew-analysis/) - [next → Who cares about Sarcastic Tweets? Investigating the Impact of Sarcasm on Sentiment Analysis.](https://md.research.elamur.ai/free/en/papers/maynard-2014-who-cares-about-sarcastic-tweets/) - [↑ back to papers of 2014](https://md.research.elamur.ai/free/en/papers/2014/page/2/) ## References 34 references, 5 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet. 1. 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