{"url":"/task/unet-segmentation","name":"UNET Segmentation","slug":"unet-segmentation","description_markdown":"U-Net is an architecture for semantic segmentation. It consists of a contracting path (Up to down) and an expanding path (Down to up). During the contraction, the spatial information is reduced while feature information is increased.\r\nThe contracting path follows the typical architecture of a convolutional network. It consists of the repeated application of two 3x3 convolutions (unpadded convolutions), each followed by a rectified linear unit (ReLU) and a 2x2 max pooling operation with stride 2 for downsampling. At each downsampling step, we double the number of feature channels. Every step in the expansive path consists of an upsampling of the feature map followed by a 2x2 convolution (“up-convolution”) that halves the number of feature channels, a concatenation with the correspondingly cropped feature map from the contracting path, and two 3x3 convolutions, each followed by a ReLU. The cropping is necessary due to the loss of border pixels in every convolution. At the final layer, a 1x1 convolution is used to map each 64-component feature vector to the desired number of classes. In total the network has 23 convolutional layers.","categories":[{"name":"Computer Code","url":"/area/computer-code"},{"name":"Computer Vision","url":"/area/computer-vision"},{"name":"Medical","url":"/area/medical"},{"name":"Robots","url":"/area/robots"}],"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","slug_source":"archive_url"},"counts":{"papers_tagged":24,"papers_with_code":11,"benchmarks":1,"benchmark_tables_in_archive":1,"benchmark_tables_shown":1,"benchmark_tables_withheld_as_spam":0,"benchmark_definition":"a leaderboard table with at least one row; benchmark_tables_shown also counts the zero-row tables; benchmark_tables_in_archive adds the tables withheld as spam","datasets":4,"subtasks":0,"parent_tasks":1},"benchmarks":[{"leaderboard":"/sota/unet-segmentation-on-munich-sentinel2-crop-1","slug":"unet-segmentation-on-munich-sentinel2-crop-1","dataset":"Munich Sentinel2 Crop Segmentation","dataset_url":"/dataset/munich-sentinel2-crop-segmentation","rows_in_archive":5,"metrics":["Overall Accuracy"],"first_row_in_archive_order":{"model":"Swin UNETR","paper_title":"Enhancing crop segmentation in satellite image time-series with transformer networks","paper_url":"/paper/enhancing-crop-segmentation-in-satellite","paper_date":"2024-04-03","arxiv_id":null,"code_links":[{"title":"mattiagatti/sentinel2-crop-mapping-models","url":"https://gitlab.com/mattiagatti/sentinel2-crop-mapping-models"}],"syntology":null}}],"datasets":[{"url":"/dataset/abdomenct-1k","name":"AbdomenCT-1K","full_name":"","num_papers_in_archive":46},{"url":"/dataset/munich-sentinel2-crop-segmentation","name":"Munich Sentinel2 Crop Segmentation","full_name":"","num_papers_in_archive":3},{"url":"/dataset/pothole-mix","name":"Pothole Mix","full_name":"Pothole Mix Semantic Segmentation Dataset for Road Damage Detection and Segmentation","num_papers_in_archive":2},{"url":"/dataset/mgpfd","name":"MGPFD","full_name":"multi-goal path finding dataset","num_papers_in_archive":1}],"subtasks":[],"parent_tasks":[{"url":"/task/semantic-segmentation","name":"Semantic Segmentation"}],"papers":{"order":"repositories listed in the archive (desc), then date (desc); the archive holds no stars","population":"papers tagged with this task that list at least one repository in the archive","shown":11,"of":11,"tagged_in_all":24,"items":[{"url":"/paper/uctransnet-rethinking-the-skip-connections-in","title":"UCTransNet: Rethinking the Skip Connections in U-Net from a Channel-wise Perspective with Transformer","date":"2021-09-09","arxiv_id":"2109.04335","repositories_listed":3,"syntology":{"n":1,"n_ran":1,"n_unverified":0,"n_pointer_only":1}},{"url":"/paper/optimized-high-resolution-3d-dense-u-net","title":"Optimized High Resolution 3D Dense-U-Net Network for Brain and Spine Segmentation","date":"2019-01-25","arxiv_id":null,"repositories_listed":2,"syntology":null},{"url":"/paper/a-high-order-focus-interaction-model-and-oral","title":"A high-order focus interaction model and oral ulcer dataset for oral ulcer segmentation","date":"2024-08-29","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/enhancing-crop-segmentation-in-satellite","title":"Enhancing crop segmentation in satellite image time-series with transformer networks","date":"2024-04-03","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/automated-identification-and-segmentation-of","title":"Automated Identification and Segmentation of Hi Sources in CRAFTS Using Deep Learning Method","date":"2024-03-29","arxiv_id":"2403.19912","repositories_listed":1,"syntology":null},{"url":"/paper/agileformer-spatially-agile-transformer-unet","title":"AgileFormer: Spatially Agile Transformer UNet for Medical Image Segmentation","date":"2024-03-29","arxiv_id":"2404.00122","repositories_listed":1,"syntology":null},{"url":"/paper/convolutional-proteinunetlm-competitive-with","title":"Convolutional ProteinUnetLM competitive with long short-term memory-based protein secondary structure predictors","date":"2022-11-30","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/sentinel-2-time-series-analysis-with-3d","title":"Sentinel 2 Time Series Analysis with 3D Feature Pyramid Network and Time Domain Class Activation Intervals for Crop Mapping","date":"2021-10-07","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/segmentation-of-drilled-holes-in-texture","title":"Segmentation of Drilled Holes in Texture Wooden Furniture Panels Using Deep Neural Network","date":"2021-05-23","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/gandlf-a-generally-nuanced-deep-learning","title":"GaNDLF: A Generally Nuanced Deep Learning Framework for Scalable End-to-End Clinical Workflows in Medical Imaging","date":"2021-02-26","arxiv_id":"2103.01006","repositories_listed":1,"syntology":null},{"url":"/paper/fully-automated-and-standardized-segmentation","title":"Fully Automated and Standardized Segmentation of Adipose Tissue Compartments by Deep Learning in Three-dimensional Whole-body MRI of Epidemiological Cohort Studies","date":"2020-08-05","arxiv_id":"2008.02251","repositories_listed":1,"syntology":null}],"syntology_records":1,"syntology_note":"a paper without a record is not a recorded non-run: it may lack an arXiv id or simply be absent from the graph layer"},"description_links":{"kept":0,"unwrapped_to_text":0,"bare_urls_linked":0,"relative_images_dropped":0,"rule":"internal links are kept only when the target slug exists in the catalog"},"syntology":{"read_at":"2026-09-24T18:15:14+00:00","claim":"Per-sample execution status on synthesized fixtures ('ran N of M samples'); not a correctness claim and not a ranking signal.","status_vocabulary":{"ran_honours":"ran, honoured the contract we drafted","ran_violates":"ran, violated the contract we drafted","ran_draft_wrong":"ran; our contract draft was wrong, not the code","ran_fixture":"ran; our fixture could not drive it","ran":"ran on a synthesized input","unverified":"unverified (harvested, no recorded run)"}},"not_shown":{"libraries":"the archive has no per-task library table","trend_sparklines":"the Trend column of the benchmarks table was a rendered image; it is not in the archive","social_and_latest_sorts":"stars and social signals are not in the archive"}}