{"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/hardnet-mseg-a-simple-encoder-decoder-polyp","title":"HarDNet-MSEG: A Simple Encoder-Decoder Polyp Segmentation Neural Network that Achieves over 0.9 Mean Dice and 86 FPS","arxiv_id":"2101.07172","date":"2021-01-18","proceeding":null,"authors":["Chien-Hsiang Huang","Hung-Yu Wu","Youn-Long Lin"],"abstract":"We propose a new convolution neural network called HarDNet-MSEG for polyp segmentation. It achieves SOTA in both accuracy and inference speed on five popular datasets. For Kvasir-SEG, HarDNet-MSEG delivers 0.904 mean Dice running at 86.7 FPS on a GeForce RTX 2080 Ti GPU. It consists of a backbone and a decoder. The backbone is a low memory traffic CNN called HarDNet68, which has been successfully applied to various CV tasks including image classification, object detection, multi-object tracking and semantic segmentation, etc. The decoder part is inspired by the Cascaded Partial Decoder, known for fast and accurate salient object detection. We have evaluated HarDNet-MSEG using those five popular datasets. The code and all experiment details are available at Github. https://github.com/james128333/HarDNet-MSEG","url_abs":"https://arxiv.org/abs/2101.07172v2","url_pdf":"https://arxiv.org/pdf/2101.07172v2.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":"hardnet-mseg-a-simple-encoder-decoder-polyp","repo_url":"https://github.com/james128333/HarDNet-MSEG","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"hardnet-mseg-a-simple-encoder-decoder-polyp","repo_url":"https://github.com/lanPN85/HarDNet-MSEG","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"hardnet-mseg-a-simple-encoder-decoder-polyp","repo_url":"https://github.com/sahadevpoudel/hardnet-mseg","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"hardnet-mseg-a-simple-encoder-decoder-polyp","repo_url":"https://github.com/2023-MindSpore-4/Code10/tree/main/hardnet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null}],"tasks":[{"task_slug":"decoder","task_name":"Decoder"},{"task_slug":null,"task_name":"GPU"},{"task_slug":"image-classification","task_name":"Image Classification"},{"task_slug":"medical-image-segmentation","task_name":"Medical Image Segmentation"},{"task_slug":"multi-object-tracking","task_name":"Multi-Object Tracking"},{"task_slug":"object","task_name":"Object"},{"task_slug":"object-detection","task_name":"Object Detection"},{"task_slug":"object-tracking","task_name":"Object Tracking"},{"task_slug":"salient-object-detection-1","task_name":"Salient Object Detection"},{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"},{"task_slug":"image-classification","task_name":"image-classification"},{"task_slug":"object-detection-1","task_name":"object-detection"}],"methods":[{"method_slug":"convolution","method_name":"Convolution"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/medical-image-segmentation-on-cvc-clinicdb","task":"Medical Image Segmentation","dataset":"CVC-ClinicDB","model":"HarDNet-MSEG","rank_in_archive_order":29,"of":48,"metrics":{"mean Dice":"0.9320"},"uses_additional_data":false},{"leaderboard":"/sota/medical-image-segmentation-on-cvc-colondb","task":"Medical Image Segmentation","dataset":"CVC-ColonDB","model":"HarDNet-MSEG","rank_in_archive_order":24,"of":25,"metrics":{"mIoU":"0.660","mean Dice":"0.731"},"uses_additional_data":false},{"leaderboard":"/sota/medical-image-segmentation-on-etis","task":"Medical Image Segmentation","dataset":"ETIS-LARIBPOLYPDB","model":"HarDNet-MSEG","rank_in_archive_order":22,"of":25,"metrics":{"mIoU":"0.613","mean Dice":"0.677"},"uses_additional_data":false},{"leaderboard":"/sota/medical-image-segmentation-on-kvasir-seg","task":"Medical Image Segmentation","dataset":"Kvasir-SEG","model":"HarDNet-MSEG","rank_in_archive_order":33,"of":58,"metrics":{"Average MAE":"0.025","FPS":"116","S-Measure":"0.923","mIoU":"0.857","max E-Measure":"0.958","mean Dice":"0.912"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2101.07172","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}