[ { "mIoU": 56.2, "code_links": [ { "title": "HRNet/HRNet-Semantic-Segmentation", "url": "https://github.com/HRNet/HRNet-Semantic-Segmentation" }, { "title": "openseg-group/OCNet.pytorch", "url": "https://github.com/openseg-group/OCNet.pytorch" }, { "title": "PkuRainBow/OCNet", "url": "https://github.com/PkuRainBow/OCNet" }, { "title": "PkuRainBow/OCNet.pytorch", "url": "https://github.com/PkuRainBow/OCNet.pytorch" }, { "title": "openseg-group/openseg.pytorch", "url": "https://github.com/openseg-group/openseg.pytorch" }, { "title": "rosinality/ocr-pytorch", "url": "https://github.com/rosinality/ocr-pytorch" } ], "date": "2019-09-24", "date2": 20190924, "model": "OCR (HRNetV2-W48)", "paper": { "title": "Object-Contextual Representations for Semantic Segmentation", "url": "https://cknow.io/lib/2425114c988834de" }, "paper_data_uoa": "2425114c988834de" }, { "mIoU": 55.6, "code_links": [], "date": "2020-03-26", "date2": 20200326, "model": "DCNAS", "paper": { "title": "DCNAS: Densely Connected Neural Architecture Search for Semantic Image Segmentation", "url": "https://cknow.io/lib/3a9f5ff508595057" }, "paper_data_uoa": "3a9f5ff508595057" }, { "mIoU": 54.8, "code_links": [ { "title": "HRNet/HRNet-Semantic-Segmentation", "url": "https://github.com/HRNet/HRNet-Semantic-Segmentation" }, { "title": "openseg-group/OCNet.pytorch", "url": "https://github.com/openseg-group/OCNet.pytorch" }, { "title": "PkuRainBow/OCNet", "url": "https://github.com/PkuRainBow/OCNet" }, { "title": "PkuRainBow/OCNet.pytorch", "url": "https://github.com/PkuRainBow/OCNet.pytorch" }, { "title": "openseg-group/openseg.pytorch", "url": "https://github.com/openseg-group/openseg.pytorch" }, { "title": "rosinality/ocr-pytorch", "url": "https://github.com/rosinality/ocr-pytorch" } ], "date": "2019-09-24", "date2": 20190924, "model": "OCR (ResNet-101)", "paper": { "title": "Object-Contextual Representations for Semantic Segmentation", "url": "https://cknow.io/lib/2425114c988834de" }, "paper_data_uoa": "2425114c988834de" }, { "mIoU": 54, "code_links": [ { "title": "CSAILVision/semantic-segmentation-pytorch", "url": "https://github.com/CSAILVision/semantic-segmentation-pytorch" }, { "title": "leoxiaobin/deep-high-resolution-net.pytorch", "url": "https://github.com/leoxiaobin/deep-high-resolution-net.pytorch" }, { "title": "HRNet/HRNet-Semantic-Segmentation", "url": "https://github.com/HRNet/HRNet-Semantic-Segmentation" }, { "title": "HRNet/HRNet-Object-Detection", "url": "https://github.com/HRNet/HRNet-Object-Detection" }, { "title": "HRNet/HRNet-Image-Classification", "url": "https://github.com/HRNet/HRNet-Image-Classification" }, { "title": "HRNet/HRNet-Facial-Landmark-Detection", "url": "https://github.com/HRNet/HRNet-Facial-Landmark-Detection" }, { "title": "HRNet/HRNet-MaskRCNN-Benchmark", "url": "https://github.com/HRNet/HRNet-MaskRCNN-Benchmark" }, { "title": "yuanyuanli85/tf-hrnet", "url": "https://github.com/yuanyuanli85/tf-hrnet" }, { "title": "shijianjian/HRNet_Keras", "url": "https://github.com/shijianjian/HRNet_Keras" } ], "date": "2019-08-20", "date2": 20190820, "model": "HRNetV2 (HRNetV2-W48)", "paper": { "title": "Deep High-Resolution Representation Learning for Visual Recognition", "url": "https://cknow.io/lib/a907f44d2b3b200a" }, "paper_data_uoa": "a907f44d2b3b200a" }, { "mIoU": 54, "code_links": [ { "title": "leoxiaobin/deep-high-resolution-net.pytorch", "url": "https://github.com/leoxiaobin/deep-high-resolution-net.pytorch" }, { "title": "HRNet/HRNet-Semantic-Segmentation", "url": "https://github.com/HRNet/HRNet-Semantic-Segmentation" }, { "title": "HRNet/HRNet-Object-Detection", "url": "https://github.com/HRNet/HRNet-Object-Detection" }, { "title": "HRNet/HRNet-Image-Classification", "url": "https://github.com/HRNet/HRNet-Image-Classification" }, { "title": "HRNet/HRNet-Facial-Landmark-Detection", "url": "https://github.com/HRNet/HRNet-Facial-Landmark-Detection" }, { "title": "HRNet/HRNet-MaskRCNN-Benchmark", "url": "https://github.com/HRNet/HRNet-MaskRCNN-Benchmark" } ], "date": "2019-08-20", "date2": 20190820, "model": "CFNet (ResNet-101)", "paper": { "title": "Deep High-Resolution Representation Learning for Visual Recognition", "url": "https://cknow.io/lib/a907f44d2b3b200a" }, "paper_data_uoa": "a907f44d2b3b200a" }, { "mIoU": 53.9, "code_links": [ { "title": "HolmesShuan/Location-aware-Upsampling-for-Semantic-Segmentation", "url": "https://github.com/HolmesShuan/Location-aware-Upsampling-for-Semantic-Segmentation" } ], "date": "2019-11-13", "date2": 20191113, "model": "LaU-regression-loss (ResNet-101)", "paper": { "title": "Location-aware Upsampling for Semantic Segmentation", "url": "https://cknow.io/lib/2c4353a03029ba80" }, "paper_data_uoa": "2c4353a03029ba80" }, { "mIoU": 53.9, "code_links": [ { "title": "ycszen/ContextPrior", "url": "https://github.com/ycszen/ContextPrior" } ], "date": "2020-04-03", "date2": 20200403, "model": "CPN(ResNet-101)", "paper": { "title": "Context Prior for Scene Segmentation", "url": "https://cknow.io/lib/476b8176f60fc25e" }, "paper_data_uoa": "476b8176f60fc25e" }, { "mIoU": 53.2, "code_links": [], "date": "2019-09-05", "date2": 20190905, "model": "SVCNet (ResNet-101)", "paper": { "title": "Semantic Correlation Promoted Shape-Variant Context for Segmentation", "url": "https://cknow.io/lib/32dfc71b496d9832" }, "paper_data_uoa": "32dfc71b496d9832" }, { "mIoU": 53.1, "code_links": [ { "title": "wuhuikai/FastFCN", "url": "https://github.com/wuhuikai/FastFCN" }, { "title": "srihari-humbarwadi/FastFCN_TF2.0", "url": "https://github.com/srihari-humbarwadi/FastFCN_TF2.0" }, { "title": "2anchao/VovJpu", "url": "https://github.com/2anchao/VovJpu" }, { "title": "yougoforward/Fast_psaa", "url": "https://github.com/yougoforward/Fast_psaa" }, { "title": "DongjuSin/computer-vision-proj", "url": "https://github.com/DongjuSin/computer-vision-proj" } ], "date": "2019-03-28", "date2": 20190328, "model": "Joint Pyramid Upsampling + EncNet", "paper": { "title": "FastFCN: Rethinking Dilated Convolution in the Backbone for Semantic Segmentation", "url": "https://cknow.io/lib/0a914f63be8b6dcd" }, "paper_data_uoa": "0a914f63be8b6dcd" }, { "mIoU": 52.6, "code_links": [ { "title": "junfu1115/DANet", "url": "https://github.com/junfu1115/DANet" }, { "title": "yiskw713/DualAttention_for_Segmentation", "url": "https://github.com/yiskw713/DualAttention_for_Segmentation" }, { "title": "zhenxingsh/Pytorch_DANet", "url": "https://github.com/zhenxingsh/Pytorch_DANet" }, { "title": "hbzhang/AwesomeSelfDriving", "url": "https://github.com/hbzhang/AwesomeSelfDriving" }, { "title": "yougoforward/hlzhu_DANet_git", "url": "https://github.com/yougoforward/hlzhu_DANet_git" } ], "date": "2018-09-09", "date2": 20180909, "model": "DANet (ResNet-101)", "paper": { "title": "Dual Attention Network for Scene Segmentation", "url": "https://cknow.io/lib/9a4a4d5c3c64a07a" }, "paper_data_uoa": "9a4a4d5c3c64a07a" }, { "mIoU": 52.5, "code_links": [ { "title": "LinZhuoChen/DUpsampling", "url": "https://github.com/LinZhuoChen/DUpsampling" }, { "title": "HymEric/Segmentation-Series-Chaos", "url": "https://github.com/HymEric/Segmentation-Series-Chaos" } ], "date": "2019-03-05", "date2": 20190305, "model": "DUpsampling", "paper": { "title": "Decoders Matter for Semantic Segmentation: Data-Dependent Decoding Enables Flexible Feature Aggregation", "url": "https://cknow.io/lib/3b18f4931a6066a9" }, "paper_data_uoa": "3b18f4931a6066a9" }, { "mIoU": 51.7, "code_links": [ { "title": "zhanghang1989/PyTorch-Encoding", "url": "https://github.com/zhanghang1989/PyTorch-Encoding" }, { "title": "zhusiling/EncNet", "url": "https://github.com/zhusiling/EncNet" }, { "title": "RyanHTR/PyTorch-Encoding", "url": "https://github.com/RyanHTR/PyTorch-Encoding" }, { "title": "kmaninis/pytorch-encoding", "url": "https://github.com/kmaninis/pytorch-encoding" }, { "title": "CWanli/myencoding", "url": "https://github.com/CWanli/myencoding" }, { "title": "Praveen94/pytorch-encoding", "url": "https://github.com/Praveen94/pytorch-encoding" } ], "date": "2018-03-23", "date2": 20180323, "model": "EncNet (ResNet-101)", "paper": { "title": "Context Encoding for Semantic Segmentation", "url": "https://cknow.io/lib/ee6cc4c538dd0cad" }, "paper_data_uoa": "ee6cc4c538dd0cad" }, { "mIoU": 51.6, "code_links": [], "date": "2018-06-01", "date2": 20180601, "model": "CCL (ResNet-101)", "paper": { "title": "Context Contrasted Feature and Gated Multi-Scale Aggregation for Scene Segmentation", "url": "https://cknow.io/lib/594924968bafa397" }, "paper_data_uoa": "594924968bafa397" }, { "mIoU": 48.1, "code_links": [ { "title": "itijyou/ademxapp", "url": "https://github.com/itijyou/ademxapp" }, { "title": "ZhaoJ9014/shallower-wider-ResNet-Model-A-SW-ResNet-A", "url": "https://github.com/ZhaoJ9014/shallower-wider-ResNet-Model-A-SW-ResNet-A" } ], "date": "2016-11-30", "date2": 20161130, "model": "ResNet-38", "paper": { "title": "Wider or Deeper: Revisiting the ResNet Model for Visual Recognition", "url": "https://cknow.io/lib/904b1daebe415d5e" }, "paper_data_uoa": "904b1daebe415d5e" }, { "mIoU": 47.8, "code_links": [ { "title": "tensorflow/models", "url": "https://github.com/tensorflow/models/tree/master/research/deeplab" }, { "title": "CSAILVision/semantic-segmentation-pytorch", "url": "https://github.com/CSAILVision/semantic-segmentation-pytorch" }, { "title": "qubvel/segmentation_models", "url": "https://github.com/qubvel/segmentation_models" }, { "title": "divamgupta/image-segmentation-keras", "url": "https://github.com/divamgupta/image-segmentation-keras" }, { "title": "osmr/imgclsmob", "url": "https://github.com/osmr/imgclsmob" }, { "title": "hszhao/PSPNet", "url": "https://github.com/hszhao/PSPNet" }, { "title": "warmspringwinds/pytorch-segmentation-detection", "url": "https://github.com/warmspringwinds/pytorch-segmentation-detection" }, { "title": "y-ouali/pytorch_segmentation", "url": "https://github.com/y-ouali/pytorch_segmentation" }, { "title": "PRBonn/bonnet", "url": "https://github.com/PRBonn/bonnet" }, { "title": "manideep2510/eye-in-the-sky", "url": "https://github.com/manideep2510/eye-in-the-sky" }, { "title": "switchablenorms/SwitchNorm_Segmentation", "url": "https://github.com/switchablenorms/SwitchNorm_Segmentation" }, { "title": "kazuto1011/pspnet-pytorch", "url": "https://github.com/kazuto1011/pspnet-pytorch" }, { "title": "BOBrown/deeparsing-master", "url": "https://github.com/BOBrown/deeparsing-master" }, { "title": "jqueguiner/camembert-as-a-service", "url": "https://github.com/jqueguiner/camembert-as-a-service" }, { "title": "jqueguiner/image-segmentation", "url": "https://github.com/jqueguiner/image-segmentation" }, { "title": "kukby/Mish-semantic-segmentation-pytorch", "url": "https://github.com/kukby/Mish-semantic-segmentation-pytorch" }, { "title": "udacity/MLND-CN-Capstone-TGSImage", "url": "https://github.com/udacity/MLND-CN-Capstone-TGSImage" }, { "title": "leemathew1998/RG", "url": "https://github.com/leemathew1998/RG" }, { "title": "RituYadav92/Image-segmentation", "url": "https://github.com/RituYadav92/Image-segmentation" }, { "title": "monsieurmona/LinkCollectionAutonomousDriving", "url": "https://github.com/monsieurmona/LinkCollectionAutonomousDriving" }, { "title": "leemathew1998/GradientWeight", "url": "https://github.com/leemathew1998/GradientWeight" }, { "title": "RRoundTable/U_net", "url": "https://github.com/RRoundTable/U_net" }, { "title": "Lxrd-AJ/Advanced_ML", "url": "https://github.com/Lxrd-AJ/Advanced_ML" } ], "date": "2016-12-04", "date2": 20161204, "model": "PSPNet (ResNet-101)", "paper": { "title": "Pyramid Scene Parsing Network", "url": "https://cknow.io/lib/59c987cd01bc78f7" }, "paper_data_uoa": "59c987cd01bc78f7" }, { "mIoU": 47.3, "code_links": [ { "title": "Microsoft/USBuildingFootprints", "url": "https://github.com/Microsoft/USBuildingFootprints" }, { "title": "guosheng/refinenet", "url": "https://github.com/guosheng/refinenet" }, { "title": "DrSleep/refinenet-pytorch", "url": "https://github.com/DrSleep/refinenet-pytorch" }, { "title": "oravus/lostX", "url": "https://github.com/oravus/lostX" }, { "title": "Attila94/refinenet-keras", "url": "https://github.com/Attila94/refinenet-keras" } ], "date": "2016-11-20", "date2": 20161120, "model": "RefineNet", "paper": { "title": "RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentation", "url": "https://cknow.io/lib/d57701b48b8ba56c" }, "paper_data_uoa": "d57701b48b8ba56c" }, { "mIoU": 45.7, "code_links": [ { "title": "tensorflow/models", "url": "https://github.com/tensorflow/models/tree/master/research/deeplab" }, { "title": "warmspringwinds/pytorch-segmentation-detection", "url": "https://github.com/warmspringwinds/pytorch-segmentation-detection" }, { "title": "kazuto1011/deeplab-pytorch", "url": "https://github.com/kazuto1011/deeplab-pytorch" }, { "title": "isht7/pytorch-deeplab-resnet", "url": "https://github.com/isht7/pytorch-deeplab-resnet" }, { "title": "DeepMotionAIResearch/DenseASPP", "url": "https://github.com/DeepMotionAIResearch/DenseASPP" }, { "title": "yaq007/Autofocus-Layer", "url": "https://github.com/yaq007/Autofocus-Layer" }, { "title": "switchablenorms/SwitchNorm_Segmentation", "url": "https://github.com/switchablenorms/SwitchNorm_Segmentation" }, { "title": "ShichengChen/WaveUNet", "url": "https://github.com/ShichengChen/WaveUNet" }, { "title": "leimao/DeepLab_v3", "url": "https://github.com/leimao/DeepLab_v3" }, { "title": "wangleihitcs/DeepLab-V1-PyTorch", "url": "https://github.com/wangleihitcs/DeepLab-V1-PyTorch" }, { "title": "kdethoor/panoptictorch", "url": "https://github.com/kdethoor/panoptictorch" }, { "title": "RituYadav92/Image-segmentation", "url": "https://github.com/RituYadav92/Image-segmentation" }, { "title": "OIdiotLin/DeepLab-pytorch", "url": "https://github.com/OIdiotLin/DeepLab-pytorch" }, { "title": "liarba/caffe_dev", "url": "https://github.com/liarba/caffe_dev" }, { "title": "cdmh/deeplab-public-ver2", "url": "https://github.com/cdmh/deeplab-public-ver2" }, { "title": "Qengineering/TensorFlow-Lite-RPi-64-bit-Segmentation", "url": "https://github.com/Qengineering/TensorFlow-Lite-RPi-64-bit-Segmentation" }, { "title": "violin0847/crowdcounting", "url": "https://github.com/violin0847/crowdcounting" }, { "title": "Lxrd-AJ/Advanced_ML", "url": "https://github.com/Lxrd-AJ/Advanced_ML" }, { "title": "purushothamgowthu/deep-photo-styletransfer", "url": "https://github.com/purushothamgowthu/deep-photo-styletransfer" }, { "title": "keb123keb/deeplabv2", "url": "https://github.com/keb123keb/deeplabv2" }, { "title": "z01nl1o02/deeplab-v2", "url": "https://github.com/z01nl1o02/deeplab-v2" }, { "title": "divisionai/deep-photo-styletransfer", "url": "https://github.com/divisionai/deep-photo-styletransfer" }, { "title": "zej-luffy/deeplab-public-ver2", "url": "https://github.com/zej-luffy/deeplab-public-ver2" }, { "title": "open-cv/deeplab-v2", "url": "https://github.com/open-cv/deeplab-v2" }, { "title": "cgsaxner/UB_Segmentation", "url": "https://github.com/cgsaxner/UB_Segmentation" } ], "date": "2016-06-02", "date2": 20160602, "model": "DeepLabV2", "paper": { "title": "DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs", "url": "https://cknow.io/lib/441ed44fe9598407" }, "paper_data_uoa": "441ed44fe9598407" }, { "mIoU": 44.5, "code_links": [], "date": "2016-05-23", "date2": 20160523, "model": "VeryDeep", "paper": { "title": "Bridging Category-level and Instance-level Semantic Image Segmentation", "url": "https://cknow.io/lib/e17bfc329083e7fa" }, "paper_data_uoa": "e17bfc329083e7fa" }, { "mIoU": 43.3, "code_links": [], "date": "2015-04-04", "date2": 20150404, "model": "Piecewise", "paper": { "title": "Efficient piecewise training of deep structured models for semantic segmentation", "url": "https://cknow.io/lib/2d65df44d2794c98" }, "paper_data_uoa": "2d65df44d2794c98" }, { "mIoU": 42.6, "code_links": [ { "title": "SharifAmit/DilatedFCNSegmentation", "url": "https://github.com/SharifAmit/DilatedFCNSegmentation" } ], "date": "2017-07-26", "date2": 20170726, "model": "Dilated-FCN2s", "paper": { "title": "Efficient Yet Deep Convolutional Neural Networks for Semantic Segmentation", "url": "https://cknow.io/lib/e57ad2b70843b182" }, "paper_data_uoa": "e57ad2b70843b182" }, { "mIoU": 41.3, "code_links": [], "date": "2015-11-25", "date2": 20151125, "model": "HO CRF", "paper": { "title": "Higher Order Conditional Random Fields in Deep Neural Networks", "url": "https://cknow.io/lib/2602d457474e89b2" }, "paper_data_uoa": "2602d457474e89b2" }, { "mIoU": 40.5, "code_links": [], "date": "2015-03-05", "date2": 20150305, "model": "BoxSup", "paper": { "title": "BoxSup: Exploiting Bounding Boxes to Supervise Convolutional Networks for Semantic Segmentation", "url": "https://cknow.io/lib/d08557865364a4c2" }, "paper_data_uoa": "d08557865364a4c2" }, { "mIoU": 40.4, "code_links": [ { "title": "tensorflow/models", "url": "https://github.com/tensorflow/models/tree/master/research/deeplab" }, { "title": "xiamenwcy/extended-caffe", "url": "https://github.com/xiamenwcy/extended-caffe" } ], "date": "2015-06-15", "date2": 20150615, "model": "ParseNet ", "paper": { "title": "ParseNet: Looking Wider to See Better", "url": "https://cknow.io/lib/1abf3fc44c90be7d" }, "paper_data_uoa": "1abf3fc44c90be7d" }, { "mIoU": 39.3, "code_links": [ { "title": "torrvision/crfasrnn", "url": "https://github.com/torrvision/crfasrnn" }, { "title": "sadeepj/crfasrnn_keras", "url": "https://github.com/sadeepj/crfasrnn_keras" }, { "title": "MiguelMonteiro/CRFasRNNLayer", "url": "https://github.com/MiguelMonteiro/CRFasRNNLayer" }, { "title": "sadeepj/crfasrnn_pytorch", "url": "https://github.com/sadeepj/crfasrnn_pytorch" }, { "title": "shihui2010/portrait_matting", "url": "https://github.com/shihui2010/portrait_matting" }, { "title": "liyin2015/superpixel_crfasrnn", "url": "https://github.com/liyin2015/superpixel_crfasrnn" }, { "title": "AsafBarZvi/Liver_project", "url": "https://github.com/AsafBarZvi/Liver_project" } ], "date": "2015-02-11", "date2": 20150211, "model": "CRF-RNN", "paper": { "title": "Conditional Random Fields as Recurrent Neural Networks", "url": "https://cknow.io/lib/9c4cfaabde146baa" }, "paper_data_uoa": "9c4cfaabde146baa" }, { "mIoU": 37.8, "code_links": [ { "title": "andyzeng/apc-vision-toolbox", "url": "https://github.com/andyzeng/apc-vision-toolbox" }, { "title": "martinkersner/py_img_seg_eval", "url": "https://github.com/martinkersner/py_img_seg_eval" }, { "title": "shenshutao/image_segmentation", "url": "https://github.com/shenshutao/image_segmentation" }, { "title": "azraelzhor/tf-FCN", "url": "https://github.com/azraelzhor/tf-FCN" }, { "title": "giovanniguidi/FCN-keras", "url": "https://github.com/giovanniguidi/FCN-keras" }, { "title": "jqueguiner/camembert-as-a-service", "url": "https://github.com/jqueguiner/camembert-as-a-service" }, { "title": "tsixta/jnet", "url": "https://github.com/tsixta/jnet" }, { "title": "jqueguiner/image-segmentation", "url": "https://github.com/jqueguiner/image-segmentation" }, { "title": "demul/image_segmentation_project", "url": "https://github.com/demul/image_segmentation_project" }, { "title": "SDMrFeng/quiz-w10-fcn", "url": "https://github.com/SDMrFeng/quiz-w10-fcn" }, { "title": "anoushkrit/Knowledge", "url": "https://github.com/anoushkrit/Knowledge" }, { "title": "YigeunLee/fcn32", "url": "https://github.com/YigeunLee/fcn32" }, { "title": "lanthlove/segmentation-fcn", "url": "https://github.com/lanthlove/segmentation-fcn" }, { "title": "stoensin/w10-cnn-SegmentationClass", "url": "https://github.com/stoensin/w10-cnn-SegmentationClass" }, { "title": "SophiaYuSophiaYu/FCN_SemanticSegmentation", "url": "https://github.com/SophiaYuSophiaYu/FCN_SemanticSegmentation" }, { "title": "koryako/AI-application", "url": "https://github.com/koryako/AI-application" }, { "title": "hitukensinn/quiz-w9-code", "url": "https://github.com/hitukensinn/quiz-w9-code" }, { "title": "sigtot/unet-auto", "url": "https://github.com/sigtot/unet-auto" }, { "title": "LeeMax117/FCN_8s", "url": "https://github.com/LeeMax117/FCN_8s" }, { "title": "GodPater/model_fcn", "url": "https://github.com/GodPater/model_fcn" }, { "title": "kbardool/mrcnn3", "url": "https://github.com/kbardool/mrcnn3" }, { "title": "pessimiss/ai100-w9-master", "url": "https://github.com/pessimiss/ai100-w9-master" }, { "title": "muramasa8191/DeepLearning", "url": "https://github.com/muramasa8191/DeepLearning" }, { "title": "LeeMax117/week10_homework", "url": "https://github.com/LeeMax117/week10_homework" }, { "title": "windar427/W10_work", "url": "https://github.com/windar427/W10_work" }, { "title": "krutikabapat/Sematic_Segmentation_Using_Pytorch", "url": "https://github.com/krutikabapat/Sematic_Segmentation_Using_Pytorch" }, { "title": "guilhermesantos/Semantic-Image-Segmentation", "url": "https://github.com/guilhermesantos/Semantic-Image-Segmentation" }, { "title": "zhikunluo/semantic-segmentation-collection", "url": "https://github.com/zhikunluo/semantic-segmentation-collection" }, { "title": "SethEBaldwin/FCN", "url": "https://github.com/SethEBaldwin/FCN" }, { "title": "fxfviolet/FCN_for_segmantic_segmentation", "url": "https://github.com/fxfviolet/FCN_for_segmantic_segmentation" }, { "title": "Niloy-Chakraborty/Image_Segmentation_Model", "url": "https://github.com/Niloy-Chakraborty/Image_Segmentation_Model" }, { "title": "Muniuliuma/FCN_8s", "url": "https://github.com/Muniuliuma/FCN_8s" }, { "title": "TianchengQ/FCN", "url": "https://github.com/TianchengQ/FCN" }, { "title": "zhuyi55/week10", "url": "https://github.com/zhuyi55/week10" }, { "title": "colorfulxd/WK10_FCN", "url": "https://github.com/colorfulxd/WK10_FCN" }, { "title": "resonance20/segmenter", "url": "https://github.com/resonance20/segmenter" } ], "date": "2014-11-14", "date2": 20141114, "model": "FCN-8s", "paper": { "title": "Fully Convolutional Networks for Semantic Segmentation", "url": "https://cknow.io/lib/d657944208000a35" }, "paper_data_uoa": "d657944208000a35" } ]