{"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":"/code/unet-2","entry":"Unet","source":"Syntology graph, per-sample; not an archive number","read_at":"2026-09-24T18:15:14+00:00","claim":"Names are grouped by exact entry-name string. Same-named routines are NOT asserted to be equivalent; 'ran' means executed on a synthesized fixture, not correctness. n_samples_ran = sum of by_status over every status except 'unverified' (ran_draft_wrong and ran_fixture are failures of Syntology's instrument, not of the code); n_papers_ran = papers with at least one such sample.","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)"},"n_papers":14,"n_papers_ran":7,"units":"n_samples, n_samples_ran, n_samples_fingerprinted and by_status count distinct code bodies (code_sha256); n_places and n_places_pointer_only count places, one per (paper, code body) pair, which is also the unit of the samples list","n_samples":36,"n_samples_ran":12,"n_samples_fingerprinted":1,"n_places":36,"n_places_pointer_only":22,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":12,"unverified":24},"syntology":{"atlas_url":null,"mcp":null,"mcp_per_sample":{"tool":"get_code","arguments_in":"samples[].mcp_get_code"},"developers":"https://syntology.ai/developers"},"samples":[{"arxiv_id":"2606.21289","paper":"/paper/arxiv-2606-21289","title":"Reconstructing Randomly Masked Spectra Helps DNNs Identify Discriminant Wavenumbers","date":null,"month_inferred_from_arxiv_id":"2026-06","title_source":"syntology","repo":"Chaoscendence/TeaNet","path":"code/train_model.py","file_url":"https://github.com/Chaoscendence/TeaNet/blob/HEAD/code/train_model.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"93427dc71f549566","mcp_get_code":{"code_sha256":"93427dc71f549566"}},{"arxiv_id":"2510.22217","paper":"/paper/arxiv-2510-22217","title":"Enpowering Your Pansharpening Models with Generalizability: Unified Distribution is All You Need","date":null,"month_inferred_from_arxiv_id":"2025-10","title_source":"syntology","repo":"yc-cui/UniPAN","path":"UniPAN/model/UAPN/network.py","file_url":"https://github.com/yc-cui/UniPAN/blob/HEAD/UniPAN/model/UAPN/network.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"8742bb5aa8380b46","mcp_get_code":{"code_sha256":"8742bb5aa8380b46"}},{"arxiv_id":"2410.03396","paper":"/paper/graphcroc-cross-correlation-autoencoder-for","title":"GraphCroc: Cross-Correlation Autoencoder for Graph Structural Reconstruction","date":"2024-10-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sjduan/graphcroc","path":"GraphCroc/UNET.py","file_url":"https://github.com/sjduan/graphcroc/blob/HEAD/GraphCroc/UNET.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"cfcfbfc18037a032","mcp_get_code":{"code_sha256":"cfcfbfc18037a032"}},{"arxiv_id":"2407.16448","paper":"/paper/monowad-weather-adaptive-diffusion-model-for","title":"MonoWAD: Weather-Adaptive Diffusion Model for Robust Monocular 3D Object Detection","date":"2024-07-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"VisualAIKHU/MonoWAD","path":"visualDet3D/networks/detectors/MonoWAD.py","file_url":"https://github.com/VisualAIKHU/MonoWAD/blob/HEAD/visualDet3D/networks/detectors/MonoWAD.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e275d60eaaba6a01","mcp_get_code":{"code_sha256":"e275d60eaaba6a01"}},{"arxiv_id":"2407.16171","paper":"/paper/learning-trimodal-relation-for-avqa-with","title":"Learning Trimodal Relation for AVQA with Missing Modality","date":"2024-07-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"VisualAIKHU/Missing-AVQA","path":"net_grd_avst/net_avst.py","file_url":"https://github.com/VisualAIKHU/Missing-AVQA/blob/HEAD/net_grd_avst/net_avst.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"44756209b5482bd6","mcp_get_code":{"code_sha256":"44756209b5482bd6"}},{"arxiv_id":"2405.09940","paper":"/paper/robust-singing-voice-transcription-serves","title":"Robust Singing Voice Transcription Serves Synthesis","date":"2024-05-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"RickyL-2000/ROSVOT","path":"modules/rosvot/rosvot.py","file_url":"https://github.com/RickyL-2000/ROSVOT/blob/HEAD/modules/rosvot/rosvot.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ad1e7ac0df9b5540","mcp_get_code":{"code_sha256":"ad1e7ac0df9b5540"}},{"arxiv_id":"2403.13417","paper":"/paper/diversified-and-personalized-multi-rater","title":"Diversified and Personalized Multi-rater Medical Image Segmentation","date":"2024-03-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ycwu1997/D-Persona","path":"D-Persona/code/lib/DPersona.py","file_url":"https://github.com/ycwu1997/D-Persona/blob/HEAD/D-Persona/code/lib/DPersona.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"027e3b920d3ea5ac","mcp_get_code":{"code_sha256":"027e3b920d3ea5ac"}},{"arxiv_id":"2403.00939","paper":"/paper/g3dr-generative-3d-reconstruction-in-imagenet","title":"G3DR: Generative 3D Reconstruction in ImageNet","date":"2024-03-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"preddy5/G3DR","path":"src/unet.py","file_url":"https://github.com/preddy5/G3DR/blob/HEAD/src/unet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"a8fea6a6d1e8a21e","mcp_get_code":{"code_sha256":"a8fea6a6d1e8a21e"}},{"arxiv_id":"2109.02563","paper":"/paper/3d-human-texture-estimation-from-a-single","title":"3D Human Texture Estimation from a Single Image with Transformers","date":"2021-09-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"xuxy09/texformer","path":"transformers/texformer.py","file_url":"https://github.com/xuxy09/texformer/blob/HEAD/transformers/texformer.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"b04049de994ee88d","mcp_get_code":{"code_sha256":"b04049de994ee88d"}},{"arxiv_id":"2103.17022","paper":"/paper/layout-guided-novel-view-synthesis-from-a","title":"Layout-Guided Novel View Synthesis from a Single Indoor Panorama","date":"2021-03-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"facebookresearch/synsin","path":"models/z_buffermodel.py","file_url":"https://github.com/facebookresearch/synsin/blob/HEAD/models/z_buffermodel.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"0ef6dc4f81adc4fb","mcp_get_code":{"code_sha256":"0ef6dc4f81adc4fb"}},{"arxiv_id":"2006.13566","paper":"/paper/disk-learning-local-features-with-policy","title":"DISK: Learning local features with policy gradient","date":"2020-06-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"fabio-sim/LightGlue-ONNX","path":"lightglue_dynamo/models/disk/disk.py","file_url":"https://github.com/fabio-sim/LightGlue-ONNX/blob/HEAD/lightglue_dynamo/models/disk/disk.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"7b149b05ea2f9297","mcp_get_code":{"code_sha256":"7b149b05ea2f9297"}},{"arxiv_id":"2006.11239","paper":"/paper/denoising-diffusion-probabilistic-models","title":"Denoising Diffusion Probabilistic Models","date":"2020-06-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"bot66/mnistdiffusion","path":"model.py","file_url":"https://github.com/bot66/mnistdiffusion/blob/HEAD/model.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"533ba10eceea02a7","mcp_get_code":{"code_sha256":"533ba10eceea02a7"}},{"arxiv_id":"2006.11239","paper":"/paper/denoising-diffusion-probabilistic-models","title":"Denoising Diffusion Probabilistic Models","date":"2020-06-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sak-h/pytorch-Denoising-Diffusion-Probabilistic-Models","path":"models/ddpm.py","file_url":"https://github.com/sak-h/pytorch-Denoising-Diffusion-Probabilistic-Models/blob/HEAD/models/ddpm.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"20198eca190672b9","mcp_get_code":{"code_sha256":"20198eca190672b9"}},{"arxiv_id":"2006.11239","paper":"/paper/denoising-diffusion-probabilistic-models","title":"Denoising Diffusion Probabilistic Models","date":"2020-06-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yiyixuxu/denoising-diffusion-flax","path":"denoising_diffusion_flax/unet.py","file_url":"https://github.com/yiyixuxu/denoising-diffusion-flax/blob/HEAD/denoising_diffusion_flax/unet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"1a3194eda8c62a2c","mcp_get_code":{"code_sha256":"1a3194eda8c62a2c"}},{"arxiv_id":"2006.11239","paper":"/paper/denoising-diffusion-probabilistic-models","title":"Denoising Diffusion Probabilistic Models","date":"2020-06-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cjfghk5697/Pytorch-Research-Paper-Implementations","path":"Diffusion/DDPM/models/model.py","file_url":"https://github.com/cjfghk5697/Pytorch-Research-Paper-Implementations/blob/HEAD/Diffusion/DDPM/models/model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"bf4172f6a11423b7","mcp_get_code":{"code_sha256":"bf4172f6a11423b7"}},{"arxiv_id":"2006.11239","paper":"/paper/denoising-diffusion-probabilistic-models","title":"Denoising Diffusion Probabilistic Models","date":"2020-06-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"explainingai-code/DDPM-Pytorch","path":"models/unet_base.py","file_url":"https://github.com/explainingai-code/DDPM-Pytorch/blob/HEAD/models/unet_base.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"84ef656ebabf0f3d","mcp_get_code":{"code_sha256":"84ef656ebabf0f3d"}},{"arxiv_id":"1505.04597","paper":"/paper/u-net-convolutional-networks-for-biomedical","title":"U-Net: Convolutional Networks for Biomedical Image Segmentation","date":"2015-05-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sungyoonahn/Hint-based-image-colorization-using-Attention-Unet","path":"myUnet.py","file_url":"https://github.com/sungyoonahn/Hint-based-image-colorization-using-Attention-Unet/blob/HEAD/myUnet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"d878e71d38330aa2","mcp_get_code":{"code_sha256":"d878e71d38330aa2"}},{"arxiv_id":"1505.04597","paper":"/paper/u-net-convolutional-networks-for-biomedical","title":"U-Net: Convolutional Networks for Biomedical Image Segmentation","date":"2015-05-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"irenazra/melanoma_segmentation_Unet","path":"Unet/Unet.py","file_url":"https://github.com/irenazra/melanoma_segmentation_Unet/blob/HEAD/Unet/Unet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"8b8a1c52a4c3de1c","mcp_get_code":{"code_sha256":"8b8a1c52a4c3de1c"}},{"arxiv_id":"1505.04597","paper":"/paper/u-net-convolutional-networks-for-biomedical","title":"U-Net: Convolutional Networks for Biomedical Image Segmentation","date":"2015-05-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lyffly/Segmentation_with_my_dataset","path":"unet.py","file_url":"https://github.com/lyffly/Segmentation_with_my_dataset/blob/HEAD/unet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"7130a92b9a0caab8","mcp_get_code":{"code_sha256":"7130a92b9a0caab8"}},{"arxiv_id":"1505.04597","paper":"/paper/u-net-convolutional-networks-for-biomedical","title":"U-Net: Convolutional Networks for Biomedical Image Segmentation","date":"2015-05-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"pranjalrai-iitd/Fetal-head-segmentation-and-circumference-measurement-from-ultrasound-images","path":"Unet.py","file_url":"https://github.com/pranjalrai-iitd/Fetal-head-segmentation-and-circumference-measurement-from-ultrasound-images/blob/HEAD/Unet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"e421871fef4ab6d4","mcp_get_code":{"code_sha256":"e421871fef4ab6d4"}},{"arxiv_id":"1505.04597","paper":"/paper/u-net-convolutional-networks-for-biomedical","title":"U-Net: Convolutional Networks for Biomedical Image Segmentation","date":"2015-05-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Jo-dsa/SemanticSeg","path":"src/model.py","file_url":"https://github.com/Jo-dsa/SemanticSeg/blob/HEAD/src/model.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"3ce4a2b6ad27ee8e","mcp_get_code":{"code_sha256":"3ce4a2b6ad27ee8e"}},{"arxiv_id":"1505.04597","paper":"/paper/u-net-convolutional-networks-for-biomedical","title":"U-Net: Convolutional Networks for Biomedical Image Segmentation","date":"2015-05-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Miltos-90/UNet_Biomedical_Image_Segmentation","path":"UNet.py","file_url":"https://github.com/Miltos-90/UNet_Biomedical_Image_Segmentation/blob/HEAD/UNet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"6227d45ad8209bf7","mcp_get_code":{"code_sha256":"6227d45ad8209bf7"}},{"arxiv_id":"1505.04597","paper":"/paper/u-net-convolutional-networks-for-biomedical","title":"U-Net: Convolutional Networks for Biomedical Image Segmentation","date":"2015-05-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"shenshutao/image_segmentation","path":"models/unet.py","file_url":"https://github.com/shenshutao/image_segmentation/blob/HEAD/models/unet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"6c174b5ebdff7b45","mcp_get_code":{"code_sha256":"6c174b5ebdff7b45"}},{"arxiv_id":"1505.04597","paper":"/paper/u-net-convolutional-networks-for-biomedical","title":"U-Net: Convolutional Networks for Biomedical Image Segmentation","date":"2015-05-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Nguyendat-bit/U-net","path":"model.py","file_url":"https://github.com/Nguyendat-bit/U-net/blob/HEAD/model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6e32011ea06d1d45","mcp_get_code":{"code_sha256":"6e32011ea06d1d45"}},{"arxiv_id":"1505.04597","paper":"/paper/u-net-convolutional-networks-for-biomedical","title":"U-Net: Convolutional Networks for Biomedical Image Segmentation","date":"2015-05-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"akrsrivastava/Unet","path":"unet.py","file_url":"https://github.com/akrsrivastava/Unet/blob/HEAD/unet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"8c5bbb2c49c175e8","mcp_get_code":{"code_sha256":"8c5bbb2c49c175e8"}},{"arxiv_id":"1505.04597","paper":"/paper/u-net-convolutional-networks-for-biomedical","title":"U-Net: Convolutional Networks for Biomedical Image Segmentation","date":"2015-05-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hoaitrinh459/RobotNhatBongTennis","path":"Server/ShowResult.py","file_url":"https://github.com/hoaitrinh459/RobotNhatBongTennis/blob/HEAD/Server/ShowResult.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"b82a27c6b00f7718","mcp_get_code":{"code_sha256":"b82a27c6b00f7718"}},{"arxiv_id":"1505.04597","paper":"/paper/u-net-convolutional-networks-for-biomedical","title":"U-Net: Convolutional Networks for Biomedical Image Segmentation","date":"2015-05-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"javiergarciamolina/selfie-background-removal","path":"background-removal-app.py","file_url":"https://github.com/javiergarciamolina/selfie-background-removal/blob/HEAD/background-removal-app.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"4fde63fcad295671","mcp_get_code":{"code_sha256":"4fde63fcad295671"}},{"arxiv_id":"1505.04597","paper":"/paper/u-net-convolutional-networks-for-biomedical","title":"U-Net: Convolutional Networks for Biomedical Image Segmentation","date":"2015-05-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mberkay0/pretrained-backbones-unet","path":"backbones_unet/model/unet.py","file_url":"https://github.com/mberkay0/pretrained-backbones-unet/blob/HEAD/backbones_unet/model/unet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"a270e0e64935ab7c","mcp_get_code":{"code_sha256":"a270e0e64935ab7c"}},{"arxiv_id":"1505.04597","paper":"/paper/u-net-convolutional-networks-for-biomedical","title":"U-Net: Convolutional Networks for Biomedical Image Segmentation","date":"2015-05-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jakeret/tf_unet","path":"tf_unet/unet.py","file_url":"https://github.com/jakeret/tf_unet/blob/HEAD/tf_unet/unet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"GPL-3.0","inline_ok":false,"code_sha256_prefix":"636a8a0708f27602","mcp_get_code":{"code_sha256":"636a8a0708f27602"}},{"arxiv_id":"1505.04597","paper":"/paper/u-net-convolutional-networks-for-biomedical","title":"U-Net: Convolutional Networks for Biomedical Image Segmentation","date":"2015-05-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"DeepTrial/Retina-VesselNet","path":"py-network/model/unet.py","file_url":"https://github.com/DeepTrial/Retina-VesselNet/blob/HEAD/py-network/model/unet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"1dc2b7b065535960","mcp_get_code":{"code_sha256":"1dc2b7b065535960"}},{"arxiv_id":"1505.04597","paper":"/paper/u-net-convolutional-networks-for-biomedical","title":"U-Net: Convolutional Networks for Biomedical Image Segmentation","date":"2015-05-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"shirans/cv_course_project","path":"models/our_unet.py","file_url":"https://github.com/shirans/cv_course_project/blob/HEAD/models/our_unet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"36c8a5764f278055","mcp_get_code":{"code_sha256":"36c8a5764f278055"}},{"arxiv_id":"1505.04597","paper":"/paper/u-net-convolutional-networks-for-biomedical","title":"U-Net: Convolutional Networks for Biomedical Image Segmentation","date":"2015-05-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jerichooconnell/tf_unet","path":"tf_unet/unet.py","file_url":"https://github.com/jerichooconnell/tf_unet/blob/HEAD/tf_unet/unet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"GPL-3.0","inline_ok":false,"code_sha256_prefix":"0d4876e845a30b1e","mcp_get_code":{"code_sha256":"0d4876e845a30b1e"}},{"arxiv_id":"1505.04597","paper":"/paper/u-net-convolutional-networks-for-biomedical","title":"U-Net: Convolutional Networks for Biomedical Image Segmentation","date":"2015-05-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"YourGc/Unet_jinnan2","path":"unet/Unet.py","file_url":"https://github.com/YourGc/Unet_jinnan2/blob/HEAD/unet/Unet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"667694cd682b5760","mcp_get_code":{"code_sha256":"667694cd682b5760"}},{"arxiv_id":"1505.04597","paper":"/paper/u-net-convolutional-networks-for-biomedical","title":"U-Net: Convolutional Networks for Biomedical Image Segmentation","date":"2015-05-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"oliverz17/Unet_Keras","path":"Segmentation_Unet.py","file_url":"https://github.com/oliverz17/Unet_Keras/blob/HEAD/Segmentation_Unet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"b98ce0ae5066874b","mcp_get_code":{"code_sha256":"b98ce0ae5066874b"}},{"arxiv_id":"1505.04597","paper":"/paper/u-net-convolutional-networks-for-biomedical","title":"U-Net: Convolutional Networks for Biomedical Image Segmentation","date":"2015-05-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"rrkotik/k_salt_identification","path":"pytorch_zoo/unet.py","file_url":"https://github.com/rrkotik/k_salt_identification/blob/HEAD/pytorch_zoo/unet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"6e2baa24819602ff","mcp_get_code":{"code_sha256":"6e2baa24819602ff"}},{"arxiv_id":"ijcai2025_0890","paper":null,"title":"arXiv:ijcai2025_0890","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"Auguuust/DiffEC","path":"Diffusion_based/DiffusionModels/noisePredictModels/Unet/_1DUNet.py","file_url":"https://github.com/Auguuust/DiffEC/blob/HEAD/Diffusion_based/DiffusionModels/noisePredictModels/Unet/_1DUNet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"42bddf36ab33c011","mcp_get_code":{"code_sha256":"42bddf36ab33c011"}}]}