{"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/ternausnet-u-net-with-vgg11-encoder-pre","title":"TernausNet: U-Net with VGG11 Encoder Pre-Trained on ImageNet for Image Segmentation","arxiv_id":"1801.05746","date":"2018-01-17","proceeding":null,"authors":["Vladimir Iglovikov","Alexey Shvets"],"abstract":"Pixel-wise image segmentation is demanding task in computer vision. Classical\nU-Net architectures composed of encoders and decoders are very popular for\nsegmentation of medical images, satellite images etc. Typically, neural network\ninitialized with weights from a network pre-trained on a large data set like\nImageNet shows better performance than those trained from scratch on a small\ndataset. In some practical applications, particularly in medicine and traffic\nsafety, the accuracy of the models is of utmost importance. In this paper, we\ndemonstrate how the U-Net type architecture can be improved by the use of the\npre-trained encoder. Our code and corresponding pre-trained weights are\npublicly available at https://github.com/ternaus/TernausNet. We compare three\nweight initialization schemes: LeCun uniform, the encoder with weights from\nVGG11 and full network trained on the Carvana dataset. This network\narchitecture was a part of the winning solution (1st out of 735) in the Kaggle:\nCarvana Image Masking Challenge.","url_abs":"http://arxiv.org/abs/1801.05746v1","url_pdf":"http://arxiv.org/pdf/1801.05746v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"ternausnet-u-net-with-vgg11-encoder-pre","repo_url":"https://github.com/ternaus/TernausNet","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"ternausnet-u-net-with-vgg11-encoder-pre","repo_url":"https://github.com/IlliaOvcharenko/lung-segmentation","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}},{"paper_slug":"ternausnet-u-net-with-vgg11-encoder-pre","repo_url":"https://github.com/IzPerfect/CT_Image_Segmentation","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}},{"paper_slug":"ternausnet-u-net-with-vgg11-encoder-pre","repo_url":"https://github.com/MatusChladek/Semantic-Tissue-Segmentation","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}},{"paper_slug":"ternausnet-u-net-with-vgg11-encoder-pre","repo_url":"https://github.com/intsco/am-segmentation","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}},{"paper_slug":"ternausnet-u-net-with-vgg11-encoder-pre","repo_url":"https://github.com/kaichoulyc/tgs-salts","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}},{"paper_slug":"ternausnet-u-net-with-vgg11-encoder-pre","repo_url":"https://github.com/od-crypto/aerial","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}},{"paper_slug":"ternausnet-u-net-with-vgg11-encoder-pre","repo_url":"https://github.com/tarolangner/ukb_segmentation","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}},{"paper_slug":"ternausnet-u-net-with-vgg11-encoder-pre","repo_url":"https://github.com/ternaus/TernausNetV2","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"ternausnet-u-net-with-vgg11-encoder-pre","repo_url":"https://github.com/ternaus/angiodysplasia-segmentation","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"ternausnet-u-net-with-vgg11-encoder-pre","repo_url":"https://github.com/trupewate/lung_segmentation_tutorial","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"ternausnet-u-net-with-vgg11-encoder-pre","repo_url":"https://github.com/yassineAlouini/airbus_ship_detection","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}},{"paper_slug":"ternausnet-u-net-with-vgg11-encoder-pre","repo_url":"https://github.com/yxinjiang/Unet-for-foreground-segmentation","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"image-segmentation","task_name":"Image Segmentation"},{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"}],"methods":[{"method_slug":"concatenated-skip-connection","method_name":"Concatenated Skip Connection"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"max-pooling","method_name":"Max Pooling"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"u-net","method_name":"U-Net"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1801.05746","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1801.05746"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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