转：Awesome Image/Video segmentation

淩亂°似流年 2022-01-09 11:13 198阅读 0赞

## ##

### Semantic segmentation ###

*  U-Net \[[https://arxiv.org/pdf/1505.04597.pdf][https_arxiv.org_pdf_1505.04597.pdf]\] \[2015\]
    
     *  [https://github.com/zhixuhao/unet][https_github.com_zhixuhao_unet] \[Keras\][![GitHub stars][]][https_github.com_zhixuhao_unet]
     *  [/images/20220109/f2dc64bdec9141e1bd102a2be4d23c19.png][https_github.com_jocicmarko_ultrasound-nerve-segmentation] \[Keras\][![GitHub stars][GitHub stars 1]][https_github.com_jocicmarko_ultrasound-nerve-segmentation]
     *  [/images/20220109/334c228d38d44ba792ce28eab766dd87.png][https_github.com_EdwardTyantov_ultrasound-nerve-segmentation] \[Keras\][![GitHub stars][GitHub stars 2]][https_github.com_EdwardTyantov_ultrasound-nerve-segmentation]
     *  [https://github.com/ZFTurbo/ZF\_UNET\_224\_Pretrained\_Model][https_github.com_ZFTurbo_ZF_UNET_224_Pretrained_Model] \[Keras\][![GitHub stars][GitHub stars 3]][https_github.com_ZFTurbo_ZF_UNET_224_Pretrained_Model]
     *  [/images/20220109/3102765c4eb342fb9f736a9a6085d884.png][https_github.com_yihui-he_u-net] \[Keras\][![GitHub stars][GitHub stars 4]][https_github.com_yihui-he_u-net]
     *  [https://github.com/jakeret/tf\_unet][https_github.com_jakeret_tf_unet] \[Tensorflow\][![GitHub stars][GitHub stars 5]][https_github.com_jakeret_tf_unet]
     *  [/images/20220109/bb9483d0911a4f399db55befde17e4a0.png][https_github.com_divamgupta_image-segmentation-keras] \[Keras\][![GitHub stars][GitHub stars 6]][https_github.com_divamgupta_image-segmentation-keras]
     *  [/images/20220109/e997a99e1ceb42f2a4ded045e01deee2.png][https_github.com_ZijunDeng_pytorch-semantic-segmentation] \[PyTorch\][![GitHub stars][GitHub stars 7]][https_github.com_ZijunDeng_pytorch-semantic-segmentation]
     *  [/images/20220109/ca1923aa9e984e98b2a34bfeb640cd3b.png][https_github.com_akirasosa_mobile-semantic-segmentation] \[Keras\][![GitHub stars][GitHub stars 8]][https_github.com_akirasosa_mobile-semantic-segmentation]
     *  [/images/20220109/5c6c11b514f24108993e1be3a58136c2.png][https_github.com_orobix_retina-unet] \[Keras\][![GitHub stars][GitHub stars 9]][https_github.com_orobix_retina-unet]
     *  [https://github.com/qureai/ultrasound-nerve-segmentation-using-torchnet][https_github.com_qureai_ultrasound-nerve-segmentation-using-torchnet] \[Torch\][![GitHub stars][GitHub stars 10]][https_github.com_orobix_retina-unet]
     *  [/images/20220109/b6db3feab8cf4b6fa7145dbb70ef2f6d.png][https_github.com_ternaus_TernausNet] \[PyTorch\][![GitHub stars][GitHub stars 11]][https_github.com_ternaus_TernausNet]
     *  [https://github.com/qubvel/segmentation\_models][https_github.com_qubvel_segmentation_models] \[Keras\][![GitHub stars][GitHub stars 12]][https_github.com_qubvel_segmentation_models]
     *  [https://github.com/LeeJunHyun/Image\_Segmentation\#u-net][https_github.com_LeeJunHyun_Image_Segmentation_u-net] \[PyTorch\][![GitHub stars][GitHub stars 13]][GitHub stars_GitHub stars 13]
     *  [https://lmb.informatik.uni-freiburg.de/people/ronneber/u-net/][https_lmb.informatik.uni-freiburg.de_people_ronneber_u-net] \[Caffe + Matlab\]
 *  SegNet \[[https://arxiv.org/pdf/1511.00561.pdf][https_arxiv.org_pdf_1511.00561.pdf]\] \[2016\]
    
     *  [https://github.com/alexgkendall/caffe-segnet][https_github.com_alexgkendall_caffe-segnet] \[Caffe\]
     *  [https://github.com/developmentseed/caffe/tree/segnet-multi-gpu][https_github.com_developmentseed_caffe_tree_segnet-multi-gpu] \[Caffe\]
     *  [https://github.com/preddy5/segnet][https_github.com_preddy5_segnet] \[Keras\]
     *  [https://github.com/imlab-uiip/keras-segnet][https_github.com_imlab-uiip_keras-segnet] \[Keras\]
     *  [https://github.com/andreaazzini/segnet][https_github.com_andreaazzini_segnet] \[Tensorflow\]
     *  [https://github.com/fedor-chervinskii/segnet-torch][https_github.com_fedor-chervinskii_segnet-torch] \[Torch\]
     *  [https://github.com/0bserver07/Keras-SegNet-Basic][https_github.com_0bserver07_Keras-SegNet-Basic] \[Keras\]
     *  [https://github.com/tkuanlun350/Tensorflow-SegNet][https_github.com_tkuanlun350_Tensorflow-SegNet] \[Tensorflow\]
     *  [/images/20220109/bb9483d0911a4f399db55befde17e4a0.png][https_github.com_divamgupta_image-segmentation-keras] \[Keras\]
     *  [/images/20220109/e997a99e1ceb42f2a4ded045e01deee2.png][https_github.com_ZijunDeng_pytorch-semantic-segmentation] \[PyTorch\]
     *  [https://github.com/chainer/chainercv/tree/master/examples/segnet][https_github.com_chainer_chainercv_tree_master_examples_segnet] \[Chainer\]
     *  [https://github.com/ykamikawa/keras-SegNet][https_github.com_ykamikawa_keras-SegNet] \[Keras\]
     *  [https://github.com/ykamikawa/tf-keras-SegNet][https_github.com_ykamikawa_tf-keras-SegNet] \[Keras\]
 *  DeepLab \[[https://arxiv.org/pdf/1606.00915.pdf][https_arxiv.org_pdf_1606.00915.pdf]\] \[2017\]
    
     *  [https://bitbucket.org/deeplab/deeplab-public/][https_bitbucket.org_deeplab_deeplab-public] \[Caffe\]
     *  [https://github.com/cdmh/deeplab-public][https_github.com_cdmh_deeplab-public] \[Caffe\]
     *  [https://bitbucket.org/aquariusjay/deeplab-public-ver2][https_bitbucket.org_aquariusjay_deeplab-public-ver2] \[Caffe\]
     *  [https://github.com/TheLegendAli/DeepLab-Context][https_github.com_TheLegendAli_DeepLab-Context] \[Caffe\]
     *  [https://github.com/msracver/Deformable-ConvNets/tree/master/deeplab][https_github.com_msracver_Deformable-ConvNets_tree_master_deeplab] \[MXNet\]
     *  [https://github.com/DrSleep/tensorflow-deeplab-resnet][https_github.com_DrSleep_tensorflow-deeplab-resnet] \[Tensorflow\]
     *  [https://github.com/muyang0320/tensorflow-deeplab-resnet-crf][https_github.com_muyang0320_tensorflow-deeplab-resnet-crf] \[TensorFlow\]
     *  [https://github.com/isht7/pytorch-deeplab-resnet][https_github.com_isht7_pytorch-deeplab-resnet] \[PyTorch\]
     *  [https://github.com/bermanmaxim/jaccardSegment][https_github.com_bermanmaxim_jaccardSegment] \[PyTorch\]
     *  [https://github.com/martinkersner/train-DeepLab][https_github.com_martinkersner_train-DeepLab] \[Caffe\]
     *  [https://github.com/chenxi116/TF-deeplab][https_github.com_chenxi116_TF-deeplab] \[Tensorflow\]
     *  [https://github.com/bonlime/keras-deeplab-v3-plus][https_github.com_bonlime_keras-deeplab-v3-plus] \[Keras\]
     *  [https://github.com/tensorflow/models/tree/master/research/deeplab][https_github.com_tensorflow_models_tree_master_research_deeplab] \[Tensorflow\]
     *  [https://github.com/speedinghzl/pytorch-segmentation-toolbox][https_github.com_speedinghzl_pytorch-segmentation-toolbox] \[PyTorch\]
     *  [https://github.com/kazuto1011/deeplab-pytorch][https_github.com_kazuto1011_deeplab-pytorch] \[PyTorch\]
     *  [https://github.com/youansheng/torchcv][https_github.com_youansheng_torchcv] \[PyTorch\]
 *  FCN \[[https://arxiv.org/pdf/1605.06211.pdf][https_arxiv.org_pdf_1605.06211.pdf]\] \[2016\]
    
     *  [https://github.com/vlfeat/matconvnet-fcn][https_github.com_vlfeat_matconvnet-fcn] \[MatConvNet\]
     *  [https://github.com/shelhamer/fcn.berkeleyvision.org][https_github.com_shelhamer_fcn.berkeleyvision.org] \[Caffe\]
     *  [https://github.com/MarvinTeichmann/tensorflow-fcn][https_github.com_MarvinTeichmann_tensorflow-fcn] \[Tensorflow\]
     *  [https://github.com/aurora95/Keras-FCN][https_github.com_aurora95_Keras-FCN] \[Keras\]
     *  [https://github.com/mzaradzki/neuralnets/tree/master/vgg\_segmentation\_keras][https_github.com_mzaradzki_neuralnets_tree_master_vgg_segmentation_keras] \[Keras\]
     *  [https://github.com/k3nt0w/FCN\_via\_keras][https_github.com_k3nt0w_FCN_via_keras] \[Keras\]
     *  [https://github.com/shekkizh/FCN.tensorflow][https_github.com_shekkizh_FCN.tensorflow] \[Tensorflow\]
     *  [https://github.com/seewalker/tf-pixelwise][https_github.com_seewalker_tf-pixelwise] \[Tensorflow\]
     *  [/images/20220109/bb9483d0911a4f399db55befde17e4a0.png][https_github.com_divamgupta_image-segmentation-keras] \[Keras\]
     *  [/images/20220109/e997a99e1ceb42f2a4ded045e01deee2.png][https_github.com_ZijunDeng_pytorch-semantic-segmentation] \[PyTorch\]
     *  [https://github.com/wkentaro/pytorch-fcn][https_github.com_wkentaro_pytorch-fcn] \[PyTorch\]
     *  [https://github.com/wkentaro/fcn][https_github.com_wkentaro_fcn] \[Chainer\]
     *  [https://github.com/apache/incubator-mxnet/tree/master/example/fcn-xs][https_github.com_apache_incubator-mxnet_tree_master_example_fcn-xs] \[MxNet\]
     *  [https://github.com/muyang0320/tf-fcn][https_github.com_muyang0320_tf-fcn] \[Tensorflow\]
     *  [https://github.com/ycszen/pytorch-seg][https_github.com_ycszen_pytorch-seg] \[PyTorch\]
     *  [https://github.com/Kaixhin/FCN-semantic-segmentation][https_github.com_Kaixhin_FCN-semantic-segmentation] \[PyTorch\]
     *  [https://github.com/petrama/VGGSegmentation][https_github.com_petrama_VGGSegmentation] \[Tensorflow\]
     *  [https://github.com/simonguist/testing-fcn-for-cityscapes][https_github.com_simonguist_testing-fcn-for-cityscapes] \[Caffe\]
     *  [https://github.com/hellochick/semantic-segmentation-tensorflow][https_github.com_hellochick_semantic-segmentation-tensorflow] \[Tensorflow\]
     *  [https://github.com/pierluigiferrari/fcn8s\_tensorflow][https_github.com_pierluigiferrari_fcn8s_tensorflow] \[Tensorflow\]
     *  [https://github.com/theduynguyen/Keras-FCN][https_github.com_theduynguyen_Keras-FCN] \[Keras\]
     *  [https://github.com/JihongJu/keras-fcn][https_github.com_JihongJu_keras-fcn] \[Keras\]
 *  ENet \[[https://arxiv.org/pdf/1606.02147.pdf][https_arxiv.org_pdf_1606.02147.pdf]\] \[2016\]
    
     *  [https://github.com/TimoSaemann/ENet][https_github.com_TimoSaemann_ENet] \[Caffe\]
     *  [https://github.com/e-lab/ENet-training][https_github.com_e-lab_ENet-training] \[Torch\]
     *  [https://github.com/PavlosMelissinos/enet-keras][https_github.com_PavlosMelissinos_enet-keras] \[Keras\]
     *  [https://github.com/fregu856/segmentation][https_github.com_fregu856_segmentation] \[Tensorflow\]
     *  [https://github.com/kwotsin/TensorFlow-ENet][https_github.com_kwotsin_TensorFlow-ENet] \[Tensorflow\]
     *  [https://github.com/davidtvs/PyTorch-ENet][https_github.com_davidtvs_PyTorch-ENet] \[PyTorch\]
 *  LinkNet \[[https://arxiv.org/pdf/1707.03718.pdf][https_arxiv.org_pdf_1707.03718.pdf]\] \[2017\]
    
     *  [https://github.com/e-lab/LinkNet][https_github.com_e-lab_LinkNet] \[Torch\]
     *  [https://github.com/qubvel/segmentation\_models][https_github.com_qubvel_segmentation_models] \[Keras\]
 *  DenseNet \[[https://arxiv.org/pdf/1608.06993.pdf][https_arxiv.org_pdf_1608.06993.pdf]\] \[2018\]
    
     *  [https://github.com/flyyufelix/DenseNet-Keras][https_github.com_flyyufelix_DenseNet-Keras] \[Keras\]
 *  Tiramisu \[[https://arxiv.org/pdf/1611.09326.pdf][https_arxiv.org_pdf_1611.09326.pdf]\] \[2017\]
    
     *  [https://github.com/0bserver07/One-Hundred-Layers-Tiramisu][https_github.com_0bserver07_One-Hundred-Layers-Tiramisu] \[Keras\]
     *  [https://github.com/SimJeg/FC-DenseNet][https_github.com_SimJeg_FC-DenseNet] \[Lasagne\]
 *  DilatedNet \[[https://arxiv.org/pdf/1511.07122.pdf][https_arxiv.org_pdf_1511.07122.pdf]\] \[2016\]
    
     *  [https://github.com/nicolov/segmentation\_keras][https_github.com_nicolov_segmentation_keras] \[Keras\]
     *  [https://github.com/fyu/dilation][https_github.com_fyu_dilation] \[Caffe\]
     *  [https://github.com/fyu/drn\#semantic-image-segmentataion][https_github.com_fyu_drn_semantic-image-segmentataion] \[PyTorch\]
     *  [https://github.com/hangzhaomit/semantic-segmentation-pytorch][https_github.com_hangzhaomit_semantic-segmentation-pytorch] \[PyTorch\]
 *  PixelNet \[[https://arxiv.org/pdf/1609.06694.pdf][https_arxiv.org_pdf_1609.06694.pdf]\] \[2016\]
    
     *  [https://github.com/aayushbansal/PixelNet][https_github.com_aayushbansal_PixelNet] \[Caffe\]
 *  ICNet \[[https://arxiv.org/pdf/1704.08545.pdf][https_arxiv.org_pdf_1704.08545.pdf]\] \[2017\]
    
     *  [https://github.com/hszhao/ICNet][https_github.com_hszhao_ICNet] \[Caffe\]
     *  [https://github.com/ai-tor/Keras-ICNet][https_github.com_ai-tor_Keras-ICNet] \[Keras\]
     *  [https://github.com/hellochick/ICNet-tensorflow][https_github.com_hellochick_ICNet-tensorflow] \[Tensorflow\]
     *  [https://github.com/oandrienko/fast-semantic-segmentation][https_github.com_oandrienko_fast-semantic-segmentation] \[Tensorflow\]
 *  ERFNet \[[http://www.robesafe.uah.es/personal/eduardo.romera/pdfs/Romera17iv.pdf][http_www.robesafe.uah.es_personal_eduardo.romera_pdfs_Romera17iv.pdf]\] \[?\]
    
     *  [https://github.com/Eromera/erfnet][https_github.com_Eromera_erfnet] \[Torch\]
     *  [https://github.com/Eromera/erfnet\_pytorch][https_github.com_Eromera_erfnet_pytorch] \[PyTorch\]
 *  RefineNet \[[https://arxiv.org/pdf/1611.06612.pdf][https_arxiv.org_pdf_1611.06612.pdf]\] \[2016\]
    
     *  [https://github.com/guosheng/refinenet][https_github.com_guosheng_refinenet] \[MatConvNet\]
 *  PSPNet \[[https://arxiv.org/pdf/1612.01105.pdf,https://hszhao.github.io/projects/pspnet/][https_arxiv.org_pdf_1612.01105.pdf_https_hszhao.github.io_projects_pspnet]\] \[2017\]
    
     *  [https://github.com/hszhao/PSPNet][https_github.com_hszhao_PSPNet] \[Caffe\]
     *  [/images/20220109/e997a99e1ceb42f2a4ded045e01deee2.png][https_github.com_ZijunDeng_pytorch-semantic-segmentation] \[PyTorch\]
     *  [https://github.com/mitmul/chainer-pspnet][https_github.com_mitmul_chainer-pspnet] \[Chainer\]
     *  [https://github.com/Vladkryvoruchko/PSPNet-Keras-tensorflow][https_github.com_Vladkryvoruchko_PSPNet-Keras-tensorflow] \[Keras/Tensorflow\]
     *  [https://github.com/pudae/tensorflow-pspnet][https_github.com_pudae_tensorflow-pspnet] \[Tensorflow\]
     *  [https://github.com/hellochick/PSPNet-tensorflow][https_github.com_hellochick_PSPNet-tensorflow] \[Tensorflow\]
     *  [https://github.com/hellochick/semantic-segmentation-tensorflow][https_github.com_hellochick_semantic-segmentation-tensorflow] \[Tensorflow\]
     *  [https://github.com/qubvel/segmentation\_models][https_github.com_qubvel_segmentation_models] \[Keras\]
     *  [https://github.com/oandrienko/fast-semantic-segmentation][https_github.com_oandrienko_fast-semantic-segmentation] \[Tensorflow\]
     *  [https://github.com/speedinghzl/pytorch-segmentation-toolbox][https_github.com_speedinghzl_pytorch-segmentation-toolbox] \[PyTorch\]
     *  [https://github.com/youansheng/torchcv][https_github.com_youansheng_torchcv] \[PyTorch\]
 *  DeconvNet \[[https://arxiv.org/pdf/1505.04366.pdf][https_arxiv.org_pdf_1505.04366.pdf]\] \[2015\]
    
     *  [http://cvlab.postech.ac.kr/research/deconvnet/][http_cvlab.postech.ac.kr_research_deconvnet] \[Caffe\]
     *  [https://github.com/HyeonwooNoh/DeconvNet][https_github.com_HyeonwooNoh_DeconvNet] \[Caffe\]
     *  [https://github.com/fabianbormann/Tensorflow-DeconvNet-Segmentation][https_github.com_fabianbormann_Tensorflow-DeconvNet-Segmentation] \[Tensorflow\]
 *  FRRN \[[https://arxiv.org/pdf/1611.08323.pdf][https_arxiv.org_pdf_1611.08323.pdf]\] \[2016\]
    
     *  [https://github.com/TobyPDE/FRRN][https_github.com_TobyPDE_FRRN] \[Lasagne\]
 *  GCN \[[https://arxiv.org/pdf/1703.02719.pdf][https_arxiv.org_pdf_1703.02719.pdf]\] \[2017\]
    
     *  [/images/20220109/e997a99e1ceb42f2a4ded045e01deee2.png][https_github.com_ZijunDeng_pytorch-semantic-segmentation] \[PyTorch\]
     *  [https://github.com/ycszen/pytorch-seg][https_github.com_ycszen_pytorch-seg] \[PyTorch\]
 *  LRR \[[https://arxiv.org/pdf/1605.02264.pdf][https_arxiv.org_pdf_1605.02264.pdf]\] \[2016\]
    
     *  [https://github.com/golnazghiasi/LRR][https_github.com_golnazghiasi_LRR] \[Matconvnet\]
 *  DUC, HDC \[[https://arxiv.org/pdf/1702.08502.pdf][https_arxiv.org_pdf_1702.08502.pdf]\] \[2017\]
    
     *  [/images/20220109/e997a99e1ceb42f2a4ded045e01deee2.png][https_github.com_ZijunDeng_pytorch-semantic-segmentation] \[PyTorch\]
     *  [https://github.com/ycszen/pytorch-seg][https_github.com_ycszen_pytorch-seg] \[PyTorch\]
 *  MultiNet \[[https://arxiv.org/pdf/1612.07695.pdf][https_arxiv.org_pdf_1612.07695.pdf]\] \[2016\]
    
     *  [https://github.com/MarvinTeichmann/MultiNet][https_github.com_MarvinTeichmann_MultiNet]
     *  [https://github.com/MarvinTeichmann/KittiSeg][https_github.com_MarvinTeichmann_KittiSeg]
 *  Segaware \[[https://arxiv.org/pdf/1708.04607.pdf][https_arxiv.org_pdf_1708.04607.pdf]\] \[2017\]
    
     *  [https://github.com/aharley/segaware][https_github.com_aharley_segaware] \[Caffe\]
 *  Semantic Segmentation using Adversarial Networks \[[https://arxiv.org/pdf/1611.08408.pdf][https_arxiv.org_pdf_1611.08408.pdf]\] \[2016\]
    
     *  [https://github.com/oyam/Semantic-Segmentation-using-Adversarial-Networks][https_github.com_oyam_Semantic-Segmentation-using-Adversarial-Networks] \[Chainer\]
 *  PixelDCN \[[https://arxiv.org/pdf/1705.06820.pdf][https_arxiv.org_pdf_1705.06820.pdf]\] \[2017\]
    
     *  [https://github.com/HongyangGao/PixelDCN][https_github.com_HongyangGao_PixelDCN] \[Tensorflow\]
 *  ShuffleSeg \[[https://arxiv.org/pdf/1803.03816.pdf][https_arxiv.org_pdf_1803.03816.pdf]\] \[2018\]
    
     *  [https://github.com/MSiam/TFSegmentation][https_github.com_MSiam_TFSegmentation] \[TensorFlow\]
 *  AdaptSegNet \[[https://arxiv.org/pdf/1802.10349.pdf][https_arxiv.org_pdf_1802.10349.pdf]\] \[2018\]
    
     *  [https://github.com/wasidennis/AdaptSegNet][https_github.com_wasidennis_AdaptSegNet] \[PyTorch\]
 *  TuSimple-DUC \[[https://arxiv.org/pdf/1702.08502.pdf][https_arxiv.org_pdf_1702.08502.pdf]\] \[2018\]
    
     *  [https://github.com/TuSimple/TuSimple-DUC][https_github.com_TuSimple_TuSimple-DUC] \[MxNet\]
 *  FPN \[[http://presentations.cocodataset.org/COCO17-Stuff-FAIR.pdf][http_presentations.cocodataset.org_COCO17-Stuff-FAIR.pdf]\] \[2017\]
    
     *  [https://github.com/qubvel/segmentation\_models][https_github.com_qubvel_segmentation_models] \[Keras\]
 *  R2U-Net \[[https://arxiv.org/ftp/arxiv/papers/1802/1802.06955.pdf][https_arxiv.org_ftp_arxiv_papers_1802_1802.06955.pdf]\] \[2018\]
    
     *  [https://github.com/LeeJunHyun/Image\_Segmentation\#r2u-net][https_github.com_LeeJunHyun_Image_Segmentation_r2u-net] \[PyTorch\]
 *  Attention U-Net \[[https://arxiv.org/pdf/1804.03999.pdf][https_arxiv.org_pdf_1804.03999.pdf]\] \[2018\]
    
     *  [https://github.com/LeeJunHyun/Image\_Segmentation\#attention-u-net][https_github.com_LeeJunHyun_Image_Segmentation_attention-u-net] \[PyTorch\]
     *  [https://github.com/ozan-oktay/Attention-Gated-Networks][https_github.com_ozan-oktay_Attention-Gated-Networks] \[PyTorch\]
 *  DANet \[[https://arxiv.org/pdf/1809.02983.pdf][https_arxiv.org_pdf_1809.02983.pdf]\] \[2018\]
    
     *  [https://github.com/junfu1115/DANet][https_github.com_junfu1115_DANet] \[PyTorch\]
 *  ShelfNet \[[https://arxiv.org/pdf/1811.11254.pdf][https_arxiv.org_pdf_1811.11254.pdf]\] \[2018\]
    
     *  [https://github.com/juntang-zhuang/ShelfNet][https_github.com_juntang-zhuang_ShelfNet] \[PyTorch\]
 *  LadderNet \[[https://arxiv.org/pdf/1810.07810.pdf][https_arxiv.org_pdf_1810.07810.pdf]\] \[2018\]
    
     *  [https://github.com/juntang-zhuang/LadderNet][https_github.com_juntang-zhuang_LadderNet] \[PyTorch\]
 *  BiSeNet \[[https://arxiv.org/pdf/1808.00897.pdf][https_arxiv.org_pdf_1808.00897.pdf]\] \[2018\]
    
     *  [https://github.com/ooooverflow/BiSeNet][https_github.com_ooooverflow_BiSeNet] \[PyTorch\]
     *  [https://github.com/ycszen/TorchSeg][https_github.com_ycszen_TorchSeg] \[PyTorch\]
 *  ESPNet \[[https://arxiv.org/pdf/1803.06815.pdf][https_arxiv.org_pdf_1803.06815.pdf]\] \[2018\]
    
     *  [https://github.com/sacmehta/ESPNet][https_github.com_sacmehta_ESPNet] \[PyTorch\]
 *  DFN \[[https://arxiv.org/pdf/1804.09337.pdf][https_arxiv.org_pdf_1804.09337.pdf]\] \[2018\]
    
     *  [https://github.com/ycszen/TorchSeg][https_github.com_ycszen_TorchSeg] \[PyTorch\]
 *  CCNet \[[https://arxiv.org/pdf/1811.11721.pdf][https_arxiv.org_pdf_1811.11721.pdf]\] \[2018\]
    
     *  [https://github.com/speedinghzl/CCNet][https_github.com_speedinghzl_CCNet] \[PyTorch\]
 *  DenseASPP \[[http://openaccess.thecvf.com/content\_cvpr\_2018/papers/Yang\_DenseASPP\_for\_Semantic\_CVPR\_2018\_paper.pdf][http_openaccess.thecvf.com_content_cvpr_2018_papers_Yang_DenseASPP_for_Semantic_CVPR_2018_paper.pdf]\] \[2018\]
    
     *  [https://github.com/youansheng/torchcv][https_github.com_youansheng_torchcv] \[PyTorch\]

## ##

### Instance aware segmentation ###

*  FCIS \[[https://arxiv.org/pdf/1611.07709.pdf][https_arxiv.org_pdf_1611.07709.pdf]\]
    
     *  [https://github.com/msracver/FCIS][https_github.com_msracver_FCIS] \[MxNet\]
 *  MNC \[[https://arxiv.org/pdf/1512.04412.pdf][https_arxiv.org_pdf_1512.04412.pdf]\]
    
     *  [https://github.com/daijifeng001/MNC][https_github.com_daijifeng001_MNC] \[Caffe\]
 *  DeepMask \[[https://arxiv.org/pdf/1506.06204.pdf][https_arxiv.org_pdf_1506.06204.pdf]\]
    
     *  [https://github.com/facebookresearch/deepmask][https_github.com_facebookresearch_deepmask] \[Torch\]
 *  SharpMask \[[https://arxiv.org/pdf/1603.08695.pdf][https_arxiv.org_pdf_1603.08695.pdf]\]
    
     *  [https://github.com/facebookresearch/deepmask][https_github.com_facebookresearch_deepmask] \[Torch\]
 *  Mask-RCNN \[[https://arxiv.org/pdf/1703.06870.pdf][https_arxiv.org_pdf_1703.06870.pdf]\]
    
     *  [https://github.com/CharlesShang/FastMaskRCNN][https_github.com_CharlesShang_FastMaskRCNN] \[Tensorflow\]
     *  [https://github.com/jasjeetIM/Mask-RCNN][https_github.com_jasjeetIM_Mask-RCNN] \[Caffe\]
     *  [https://github.com/TuSimple/mx-maskrcnn][https_github.com_TuSimple_mx-maskrcnn] \[MxNet\]
     *  [https://github.com/matterport/Mask\_RCNN][https_github.com_matterport_Mask_RCNN] \[Keras\]
     *  [https://github.com/facebookresearch/maskrcnn-benchmark][https_github.com_facebookresearch_maskrcnn-benchmark] \[PyTorch\]
 *  RIS \[[https://arxiv.org/pdf/1511.08250.pdf][https_arxiv.org_pdf_1511.08250.pdf]\]
    
     *  [https://github.com/bernard24/RIS][https_github.com_bernard24_RIS] \[Torch\]
 *  FastMask \[[https://arxiv.org/pdf/1612.08843.pdf][https_arxiv.org_pdf_1612.08843.pdf]\]
    
     *  [https://github.com/voidrank/FastMask][https_github.com_voidrank_FastMask] \[Caffe\]
 *  BlitzNet \[[https://arxiv.org/pdf/1708.02813.pdf][https_arxiv.org_pdf_1708.02813.pdf]\]
    
     *  [https://github.com/dvornikita/blitznet][https_github.com_dvornikita_blitznet] \[Tensorflow\]
 *  PANet \[[https://arxiv.org/pdf/1803.01534.pdf][https_arxiv.org_pdf_1803.01534.pdf]\] \[2018\]
    
     *  [https://github.com/ShuLiu1993/PANet][https_github.com_ShuLiu1993_PANet] \[Caffe\]
 *  TernausNetV2 \[[https://arxiv.org/pdf/1806.00844.pdf][https_arxiv.org_pdf_1806.00844.pdf]\] \[2018\]
    
     *  [/images/20220109/b6db3feab8cf4b6fa7145dbb70ef2f6d.pngV2][https_github.com_ternaus_TernausNetV2] \[PyTorch\]

## ##

### Weakly-supervised segmentation ###

*  SEC \[[https://arxiv.org/pdf/1603.06098.pdf][https_arxiv.org_pdf_1603.06098.pdf]\]
    
     *  [https://github.com/kolesman/SEC][https_github.com_kolesman_SEC] \[Caffe\]

# #

## RNN ##

*  ReNet \[[https://arxiv.org/pdf/1505.00393.pdf][https_arxiv.org_pdf_1505.00393.pdf]\]
    
     *  [https://github.com/fvisin/reseg][https_github.com_fvisin_reseg] \[Lasagne\]
 *  ReSeg \[[https://arxiv.org/pdf/1511.07053.pdf][https_arxiv.org_pdf_1511.07053.pdf]\]
    
     *  [https://github.com/Wizaron/reseg-pytorch][https_github.com_Wizaron_reseg-pytorch] \[PyTorch\]
     *  [https://github.com/fvisin/reseg][https_github.com_fvisin_reseg] \[Lasagne\]
 *  RIS \[[https://arxiv.org/pdf/1511.08250.pdf][https_arxiv.org_pdf_1511.08250.pdf]\]
    
     *  [https://github.com/bernard24/RIS][https_github.com_bernard24_RIS] \[Torch\]
 *  CRF-RNN \[[http://www.robots.ox.ac.uk/%7Eszheng/papers/CRFasRNN.pdf][http_www.robots.ox.ac.uk_7Eszheng_papers_CRFasRNN.pdf]\]
    
     *  [https://github.com/martinkersner/train-CRF-RNN][https_github.com_martinkersner_train-CRF-RNN] \[Caffe\]
     *  [https://github.com/torrvision/crfasrnn][https_github.com_torrvision_crfasrnn] \[Caffe\]
     *  [https://github.com/NP-coder/CLPS1520Project][https_github.com_NP-coder_CLPS1520Project] \[Tensorflow\]
     *  [https://github.com/renmengye/rec-attend-public][https_github.com_renmengye_rec-attend-public] \[Tensorflow\]
     *  [https://github.com/sadeepj/crfasrnn\_keras][https_github.com_sadeepj_crfasrnn_keras] \[Keras\]

# #

## GANS ##

*  pix2pixHD \[[https://arxiv.org/pdf/1711.11585.pdf][https_arxiv.org_pdf_1711.11585.pdf]\] \[2018\]
    
     *  [https://github.com/NVIDIA/pix2pixHD][https_github.com_NVIDIA_pix2pixHD]
 *  Probalistic Unet \[[https://arxiv.org/pdf/1806.05034.pdf][https_arxiv.org_pdf_1806.05034.pdf]\] \[2018\]
    
     *  [https://github.com/SimonKohl/probabilistic\_unet][https_github.com_SimonKohl_probabilistic_unet]

# #

## Graphical Models (CRF, MRF) ##

*  [https://github.com/cvlab-epfl/densecrf][https_github.com_cvlab-epfl_densecrf]
 *  [http://vladlen.info/publications/efficient-inference-in-fully-connected-crfs-with-gaussian-edge-potentials/][http_vladlen.info_publications_efficient-inference-in-fully-connected-crfs-with-gaussian-edge-potentials]
 *  [http://www.philkr.net/home/densecrf][http_www.philkr.net_home_densecrf]
 *  [http://graphics.stanford.edu/projects/densecrf/][http_graphics.stanford.edu_projects_densecrf]
 *  [https://github.com/amiltonwong/segmentation/blob/master/segmentation.ipynb][https_github.com_amiltonwong_segmentation_blob_master_segmentation.ipynb]
 *  [https://github.com/jliemansifry/super-simple-semantic-segmentation][https_github.com_jliemansifry_super-simple-semantic-segmentation]
 *  [http://users.cecs.anu.edu.au/~jdomke/JGMT/][http_users.cecs.anu.edu.au_jdomke_JGMT]
 *  [https://www.quora.com/How-can-one-train-and-test-conditional-random-field-CRF-in-Python-on-our-own-training-testing-dataset][https_www.quora.com_How-can-one-train-and-test-conditional-random-field-CRF-in-Python-on-our-own-training-testing-dataset]
 *  [https://github.com/tpeng/python-crfsuite][https_github.com_tpeng_python-crfsuite]
 *  [https://github.com/chokkan/crfsuite][https_github.com_chokkan_crfsuite]
 *  [https://sites.google.com/site/zeppethefake/semantic-segmentation-crf-baseline][https_sites.google.com_site_zeppethefake_semantic-segmentation-crf-baseline]
 *  [https://github.com/lucasb-eyer/pydensecrf][https_github.com_lucasb-eyer_pydensecrf]

# #

## Datasets: ##

*  [Stanford Background Dataset][]
 *  [Sift Flow Dataset][]
 *  [Barcelona Dataset][]
 *  [Microsoft COCO dataset][]
 *  [MSRC Dataset][]
 *  [LITS Liver Tumor Segmentation Dataset][]
 *  [KITTI][]
 *  [Pascal Context][]
 *  [Data from Games dataset][]
 *  [Human parsing dataset][]
 *  [Mapillary Vistas Dataset][]
 *  [Microsoft AirSim][]
 *  [MIT Scene Parsing Benchmark][]
 *  [COCO 2017 Stuff Segmentation Challenge][]
 *  [ADE20K Dataset][]
 *  [INRIA Annotations for Graz-02][]
 *  [Daimler dataset][]
 *  [ISBI Challenge: Segmentation of neuronal structures in EM stacks][ISBI Challenge_ Segmentation of neuronal structures in EM stacks]
 *  [INRIA Annotations for Graz-02 (IG02)][INRIA Annotations for Graz-02 _IG02]
 *  [Pratheepan Dataset][]
 *  [Clothing Co-Parsing (CCP) Dataset][Clothing Co-Parsing _CCP_ Dataset]
 *  [Inria Aerial Image][]
 *  [ApolloScape][]
 *  [UrbanMapper3D][]
 *  [RoadDetector][]
 *  [Cityscapes][]
 *  [CamVid][]

# #

## Benchmarks ##

*  [/images/20220109/e997a99e1ceb42f2a4ded045e01deee2.png][https_github.com_ZijunDeng_pytorch-semantic-segmentation] \[PyTorch\]
 *  [https://github.com/meetshah1995/pytorch-semseg][https_github.com_meetshah1995_pytorch-semseg] \[PyTorch\]
 *  [https://github.com/GeorgeSeif/Semantic-Segmentation-Suite][https_github.com_GeorgeSeif_Semantic-Segmentation-Suite] \[Tensorflow\]
 *  [https://github.com/MSiam/TFSegmentation][https_github.com_MSiam_TFSegmentation] \[Tensorflow\]
 *  [https://github.com/CSAILVision/sceneparsing][https_github.com_CSAILVision_sceneparsing] \[Caffe+Matlab\]
 *  [https://github.com/BloodAxe/segmentation-networks-benchmark][https_github.com_BloodAxe_segmentation-networks-benchmark] \[PyTorch\]
 *  [https://github.com/warmspringwinds/pytorch-segmentation-detection][https_github.com_warmspringwinds_pytorch-segmentation-detection] \[PyTorch\]
 *  [https://github.com/ycszen/TorchSeg][https_github.com_ycszen_TorchSeg] \[PyTorch\]
 *  [https://github.com/qubvel/segmentation\_models][https_github.com_qubvel_segmentation_models] \[Keras\]
 *  [https://github.com/qubvel/segmentation\_models.pytorch][https_github.com_qubvel_segmentation_models.pytorch] \[PyTorch\]

# #

## Starter code ##

*  [https://github.com/mrgloom/keras-semantic-segmentation-example][https_github.com_mrgloom_keras-semantic-segmentation-example]

# #

## Annotation Tools: ##

*  [https://github.com/AKSHAYUBHAT/ImageSegmentation][https_github.com_AKSHAYUBHAT_ImageSegmentation]
 *  [https://github.com/kyamagu/js-segment-annotator][https_github.com_kyamagu_js-segment-annotator]
 *  [https://github.com/CSAILVision/LabelMeAnnotationTool][https_github.com_CSAILVision_LabelMeAnnotationTool]
 *  [https://github.com/seanbell/opensurfaces-segmentation-ui][https_github.com_seanbell_opensurfaces-segmentation-ui]
 *  [https://github.com/lzx1413/labelImgPlus][https_github.com_lzx1413_labelImgPlus]
 *  [https://github.com/wkentaro/labelme][https_github.com_wkentaro_labelme]
 *  [https://github.com/labelbox/labelbox][https_github.com_labelbox_labelbox]
 *  [https://github.com/Deep-Magic/COCO-Style-Dataset-Generator-GUI][https_github.com_Deep-Magic_COCO-Style-Dataset-Generator-GUI]
 *  [https://github.com/Labelbox/Labelbox][https_github.com_Labelbox_Labelbox]
 *  [https://github.com/opencv/cvat][https_github.com_opencv_cvat]

# #

## Results: ##

*  [MSRC-21][]
 *  [Cityscapes][Cityscapes 1]
 *  [VOC2012][]
 *  [https://paperswithcode.com/task/semantic-segmentation][https_paperswithcode.com_task_semantic-segmentation]

# #

## Metrics ##

*  [https://github.com/martinkersner/py\_img\_seg\_eval][https_github.com_martinkersner_py_img_seg_eval]

# #

## Losses ##

*  [http://www.cs.umanitoba.ca/~ywang/papers/isvc16.pdf][http_www.cs.umanitoba.ca_ywang_papers_isvc16.pdf]
 *  [https://arxiv.org/pdf/1705.08790.pdf][https_arxiv.org_pdf_1705.08790.pdf]
 *  [https://arxiv.org/pdf/1707.03237.pdf][https_arxiv.org_pdf_1707.03237.pdf]
 *  [http://www.bmva.org/bmvc/2013/Papers/paper0032/paper0032.pdf][http_www.bmva.org_bmvc_2013_Papers_paper0032_paper0032.pdf]

# #

## Other lists ##

*  [https://github.com/tangzhenyu/SemanticSegmentation\_DL][https_github.com_tangzhenyu_SemanticSegmentation_DL]
 *  [https://github.com/nightrome/really-awesome-semantic-segmentation][https_github.com_nightrome_really-awesome-semantic-segmentation]
 *  [https://github.com/JackieZhangdx/InstanceSegmentationList][https_github.com_JackieZhangdx_InstanceSegmentationList]

# #

## Medical image segmentation: ##

*  DIGITS
    
     *  [https://github.com/NVIDIA/DIGITS/tree/master/examples/medical-imaging][https_github.com_NVIDIA_DIGITS_tree_master_examples_medical-imaging]
 *  U-Net: Convolutional Networks for Biomedical Image Segmentation
    
     *  [http://lmb.informatik.uni-freiburg.de/people/ronneber/u-net/][http_lmb.informatik.uni-freiburg.de_people_ronneber_u-net]
     *  [https://github.com/dmlc/mxnet/issues/1514][https_github.com_dmlc_mxnet_issues_1514]
     *  [/images/20220109/5c6c11b514f24108993e1be3a58136c2.png][https_github.com_orobix_retina-unet]
     *  [https://github.com/fvisin/reseg][https_github.com_fvisin_reseg]
     *  [https://github.com/yulequan/melanoma-recognition][https_github.com_yulequan_melanoma-recognition]
     *  [http://www.andrewjanowczyk.com/use-case-1-nuclei-segmentation/][http_www.andrewjanowczyk.com_use-case-1-nuclei-segmentation]
     *  [https://github.com/junyanz/MCILBoost][https_github.com_junyanz_MCILBoost]
     *  [https://github.com/imlab-uiip/lung-segmentation-2d][https_github.com_imlab-uiip_lung-segmentation-2d]
     *  [https://github.com/scottykwok/cervix-roi-segmentation-by-unet][https_github.com_scottykwok_cervix-roi-segmentation-by-unet]
     *  [https://github.com/WeidiXie/cell\_counting\_v2][https_github.com_WeidiXie_cell_counting_v2]
     *  [https://github.com/yandexdataschool/YSDA\_deeplearning17/blob/master/Seminar6/Seminar%206%20-%20segmentation.ipynb][https_github.com_yandexdataschool_YSDA_deeplearning17_blob_master_Seminar6_Seminar_206_20-_20segmentation.ipynb]
 *  Cascaded-FCN
    
     *  [https://github.com/IBBM/Cascaded-FCN][https_github.com_IBBM_Cascaded-FCN]
 *  Keras
    
     *  [/images/20220109/f2dc64bdec9141e1bd102a2be4d23c19.png][https_github.com_jocicmarko_ultrasound-nerve-segmentation]
     *  [/images/20220109/334c228d38d44ba792ce28eab766dd87.png][https_github.com_EdwardTyantov_ultrasound-nerve-segmentation]
     *  [https://github.com/intact-project/ild-cnn][https_github.com_intact-project_ild-cnn]
     *  [https://github.com/scottykwok/cervix-roi-segmentation-by-unet][https_github.com_scottykwok_cervix-roi-segmentation-by-unet]
     *  [https://github.com/lishen/end2end-all-conv][https_github.com_lishen_end2end-all-conv]
 *  Tensorflow
    
     *  [https://github.com/imatge-upc/liverseg-2017-nipsws][https_github.com_imatge-upc_liverseg-2017-nipsws]
     *  [https://github.com/DLTK/DLTK/tree/master/examples/applications/MRBrainS13\_tissue\_segmentation][https_github.com_DLTK_DLTK_tree_master_examples_applications_MRBrainS13_tissue_segmentation]
 *  Using Convolutional Neural Networks (CNN) for Semantic Segmentation of Breast Cancer Lesions (BRCA)
    
     *  [https://github.com/ecobost/cnn4brca][https_github.com_ecobost_cnn4brca]
 *  Papers:
    
     *  [https://www2.warwick.ac.uk/fac/sci/dcs/people/research/csrkbb/tmi2016\_ks.pdf][https_www2.warwick.ac.uk_fac_sci_dcs_people_research_csrkbb_tmi2016_ks.pdf]
     *  Sliding window approach
        
         *  [http://people.idsia.ch/~juergen/nips2012.pdf][http_people.idsia.ch_juergen_nips2012.pdf]
     *  [https://github.com/albarqouni/Deep-Learning-for-Medical-Applications\#segmentation][https_github.com_albarqouni_Deep-Learning-for-Medical-Applications_segmentation]
 *  Data:
    
     *  [https://luna16.grand-challenge.org/][https_luna16.grand-challenge.org]
     *  [https://camelyon16.grand-challenge.org/][https_camelyon16.grand-challenge.org]
     *  [https://github.com/beamandrew/medical-data][https_github.com_beamandrew_medical-data]

# #

## Satellite images segmentation ##

*  [https://github.com/mshivaprakash/sat-seg-thesis][https_github.com_mshivaprakash_sat-seg-thesis]
 *  [https://github.com/KGPML/Hyperspectral][https_github.com_KGPML_Hyperspectral]
 *  [https://github.com/lopuhin/kaggle-dstl][https_github.com_lopuhin_kaggle-dstl]
 *  [https://github.com/mitmul/ssai][https_github.com_mitmul_ssai]
 *  [https://github.com/mitmul/ssai-cnn][https_github.com_mitmul_ssai-cnn]
 *  [https://github.com/azavea/raster-vision][https_github.com_azavea_raster-vision]
 *  [https://github.com/nshaud/DeepNetsForEO][https_github.com_nshaud_DeepNetsForEO]
 *  [https://github.com/trailbehind/DeepOSM][https_github.com_trailbehind_DeepOSM]
 *  [https://github.com/mapbox/robosat][https_github.com_mapbox_robosat]
 *  [https://github.com/datapink/robosat.pink][https_github.com_datapink_robosat.pink]

*  Data:
    
     *  [https://github.com/RSIA-LIESMARS-WHU/RSOD-Dataset-][https_github.com_RSIA-LIESMARS-WHU_RSOD-Dataset-]
     *  SpaceNet\[[https://spacenetchallenge.github.io/][https_spacenetchallenge.github.io]\]
     *  [https://github.com/chrieke/awesome-satellite-imagery-datasets][https_github.com_chrieke_awesome-satellite-imagery-datasets]

# #

## Video segmentation ##

*  [https://github.com/shelhamer/clockwork-fcn][https_github.com_shelhamer_clockwork-fcn]
 *  [https://github.com/JingchunCheng/Seg-with-SPN][https_github.com_JingchunCheng_Seg-with-SPN]

# #

## Autonomous driving ##

*  [https://github.com/MarvinTeichmann/MultiNet][https_github.com_MarvinTeichmann_MultiNet]
 *  [https://github.com/MarvinTeichmann/KittiSeg][https_github.com_MarvinTeichmann_KittiSeg]
 *  [https://github.com/vxy10/p5\_VehicleDetection\_Unet][https_github.com_vxy10_p5_VehicleDetection_Unet] \[Keras\]
 *  [https://github.com/ndrplz/self-driving-car][https_github.com_ndrplz_self-driving-car]
 *  [https://github.com/mvirgo/MLND-Capstone][https_github.com_mvirgo_MLND-Capstone]
 *  [https://github.com/zhujun98/semantic\_segmentation/tree/master/fcn8s\_road][https_github.com_zhujun98_semantic_segmentation_tree_master_fcn8s_road]
 *  [https://github.com/MaybeShewill-CV/lanenet-lane-detection][https_github.com_MaybeShewill-CV_lanenet-lane-detection]

## ##

### Other ###

# #

## Networks by framework (Older list) ##

*  Keras
    
     *  [https://github.com/gakarak/FCN\_MSCOCO\_Food\_Segmentation][https_github.com_gakarak_FCN_MSCOCO_Food_Segmentation]
     *  [https://github.com/abbypa/NNProject\_DeepMask][https_github.com_abbypa_NNProject_DeepMask]
 *  TensorFlow
    
     *  [https://github.com/warmspringwinds/tf-image-segmentation][https_github.com_warmspringwinds_tf-image-segmentation]
 *  Caffe
    
     *  [https://github.com/xiaolonw/nips14\_loc\_seg\_testonly][https_github.com_xiaolonw_nips14_loc_seg_testonly]
     *  [https://github.com/naibaf7/caffe\_neural\_tool][https_github.com_naibaf7_caffe_neural_tool]
 *  torch
    
     *  [https://github.com/erogol/seg-torch][https_github.com_erogol_seg-torch]
     *  [https://github.com/phillipi/pix2pix][https_github.com_phillipi_pix2pix]
 *  MXNet
    
     *  [https://github.com/itijyou/ademxapp][https_github.com_itijyou_ademxapp]

# #

## Papers and Code (Older list) ##

*  Simultaneous detection and segmentation
    
     *  [http://www.eecs.berkeley.edu/Research/Projects/CS/vision/shape/sds/][http_www.eecs.berkeley.edu_Research_Projects_CS_vision_shape_sds]
     *  [https://github.com/bharath272/sds\_eccv2014][https_github.com_bharath272_sds_eccv2014]
 *  Decoupled Deep Neural Network for Semi-supervised Semantic Segmentation
    
     *  [https://github.com/HyeonwooNoh/DecoupledNet][https_github.com_HyeonwooNoh_DecoupledNet]
 *  Learning to Propose Objects
    
     *  [http://vladlen.info/publications/learning-to-propose-objects/][http_vladlen.info_publications_learning-to-propose-objects]
     *  [https://github.com/philkr/lpo][https_github.com_philkr_lpo]
 *  Nonparametric Scene Parsing via Label Transfer
    
     *  [http://people.csail.mit.edu/celiu/LabelTransfer/code.html][http_people.csail.mit.edu_celiu_LabelTransfer_code.html]
 *  Other
    
     *  [https://github.com/cvjena/cn24][https_github.com_cvjena_cn24]
     *  [http://lmb.informatik.uni-freiburg.de/resources/software.php][http_lmb.informatik.uni-freiburg.de_resources_software.php]
     *  [https://github.com/NVIDIA/DIGITS/tree/master/examples/semantic-segmentation][https_github.com_NVIDIA_DIGITS_tree_master_examples_semantic-segmentation]
     *  [http://jamie.shotton.org/work/code.html][http_jamie.shotton.org_work_code.html]
     *  [https://github.com/amueller/textonboost][https_github.com_amueller_textonboost]

# #

## To look at ##

*  [https://github.com/fchollet/keras/issues/6538][https_github.com_fchollet_keras_issues_6538]
 *  [https://github.com/warmspringwinds/tensorflow\_notes][https_github.com_warmspringwinds_tensorflow_notes]
 *  [https://github.com/kjw0612/awesome-deep-vision\#semantic-segmentation][https_github.com_kjw0612_awesome-deep-vision_semantic-segmentation]
 *  [https://github.com/desimone/segmentation-models][https_github.com_desimone_segmentation-models]
 *  [https://github.com/nightrome/really-awesome-semantic-segmentation][https_github.com_nightrome_really-awesome-semantic-segmentation]
 *  [https://github.com/kjw0612/awesome-deep-vision\#semantic-segmentation][https_github.com_kjw0612_awesome-deep-vision_semantic-segmentation]
 *  [http://www.it-caesar.com/list-of-contemporary-semantic-segmentation-datasets/][http_www.it-caesar.com_list-of-contemporary-semantic-segmentation-datasets]
 *  [https://github.com/MichaelXin/Awesome-Caffe\#23-image-segmentation][https_github.com_MichaelXin_Awesome-Caffe_23-image-segmentation]
 *  [https://github.com/warmspringwinds/pytorch-segmentation-detection][https_github.com_warmspringwinds_pytorch-segmentation-detection]
 *  [https://github.com/neuropoly/axondeepseg][https_github.com_neuropoly_axondeepseg]
 *  [https://github.com/petrochenko-pavel-a/segmentation\_training\_pipeline][https_github.com_petrochenko-pavel-a_segmentation_training_pipeline]

# #

## Blog posts, other: ##

*  [https://handong1587.github.io/deep\_learning/2015/10/09/segmentation.html][https_handong1587.github.io_deep_learning_2015_10_09_segmentation.html]
 *  [http://www.andrewjanowczyk.com/efficient-pixel-wise-deep-learning-on-large-images/][http_www.andrewjanowczyk.com_efficient-pixel-wise-deep-learning-on-large-images]
 *  [https://devblogs.nvidia.com/parallelforall/image-segmentation-using-digits-5/][https_devblogs.nvidia.com_parallelforall_image-segmentation-using-digits-5]
 *  [https://github.com/NVIDIA/DIGITS/tree/master/examples/binary-segmentation][https_github.com_NVIDIA_DIGITS_tree_master_examples_binary-segmentation]
 *  [https://github.com/NVIDIA/DIGITS/tree/master/examples/semantic-segmentation][https_github.com_NVIDIA_DIGITS_tree_master_examples_semantic-segmentation]
 *  [http://blog.qure.ai/notes/semantic-segmentation-deep-learning-review][http_blog.qure.ai_notes_semantic-segmentation-deep-learning-review]
 *  [https://medium.com/@barvinograd1/instance-embedding-instance-segmentation-without-proposals-31946a7c53e1][https_medium.com_barvinograd1_instance-embedding-instance-segmentation-without-proposals-31946a7c53e1]

转载于:https://www.cnblogs.com/augustone/p/10627364.html

[https_arxiv.org_pdf_1505.04597.pdf]: https://arxiv.org/pdf/1505.04597.pdf
[https_github.com_zhixuhao_unet]: https://github.com/zhixuhao/unet
[GitHub stars]: /images/20220109/a2df05f7b01e436294a238dd9380d830.png
[https_github.com_jocicmarko_ultrasound-nerve-segmentation]: /images/20220109/f2dc64bdec9141e1bd102a2be4d23c19.png
[GitHub stars 1]: https://camo.githubusercontent.com/7dce2471cc73140efa99ce45d9bfab0904136241/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f6a6f6369636d61726b6f2f756c747261736f756e642d6e657276652d7365676d656e746174696f6e2e7376673f6c6f676f3d676974687562266c6162656c3d5374617273
[https_github.com_EdwardTyantov_ultrasound-nerve-segmentation]: /images/20220109/334c228d38d44ba792ce28eab766dd87.png
[GitHub stars 2]: https://camo.githubusercontent.com/2278a9f178f90f4e04919e58a1c2da3ca50e513c/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f4564776172645479616e746f762f756c747261736f756e642d6e657276652d7365676d656e746174696f6e2e7376673f6c6f676f3d676974687562266c6162656c3d5374617273
[https_github.com_ZFTurbo_ZF_UNET_224_Pretrained_Model]: /images/20220109/59b8639663ed4acf907187eccffd7a9a.png
[GitHub stars 3]: https://camo.githubusercontent.com/65b4a30c01867373b2312e81dc8c1fb0eda8c256/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f5a46547572626f2f5a465f554e45545f3232345f507265747261696e65645f4d6f64656c2e7376673f6c6f676f3d676974687562266c6162656c3d5374617273
[https_github.com_yihui-he_u-net]: /images/20220109/3102765c4eb342fb9f736a9a6085d884.png
[GitHub stars 4]: https://camo.githubusercontent.com/c3becdb815d3cbc364130b9196f51244db87f3f6/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f79696875692d68652f752d6e65742e7376673f6c6f676f3d676974687562266c6162656c3d5374617273
[https_github.com_jakeret_tf_unet]: /images/20220109/2ed31a0f199b4d2dae8c36d28d11faf6.png
[GitHub stars 5]: https://camo.githubusercontent.com/391e89f2497afe47df2aa70ff84a019f11bdfd3d/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f6a616b657265742f74665f756e65742e7376673f6c6f676f3d676974687562266c6162656c3d5374617273
[https_github.com_divamgupta_image-segmentation-keras]: /images/20220109/bb9483d0911a4f399db55befde17e4a0.png
[GitHub stars 6]: https://camo.githubusercontent.com/e4749d77bd8f8069033653c21a024ea0300a91d7/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f646976616d67757074612f696d6167652d7365676d656e746174696f6e2d6b657261732e7376673f6c6f676f3d676974687562266c6162656c3d5374617273
[https_github.com_ZijunDeng_pytorch-semantic-segmentation]: /images/20220109/e997a99e1ceb42f2a4ded045e01deee2.png
[GitHub stars 7]: https://camo.githubusercontent.com/5815be54f0449070a2f90f7f16f09aed46268f90/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f5a696a756e44656e672f7079746f7263682d73656d616e7469632d7365676d656e746174696f6e2e7376673f6c6f676f3d676974687562266c6162656c3d5374617273
[https_github.com_akirasosa_mobile-semantic-segmentation]: /images/20220109/ca1923aa9e984e98b2a34bfeb640cd3b.png
[GitHub stars 8]: https://camo.githubusercontent.com/cab674a94fa2d6434c3e832b6a0f295d67550019/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f616b697261736f73612f6d6f62696c652d73656d616e7469632d7365676d656e746174696f6e2e7376673f6c6f676f3d676974687562266c6162656c3d5374617273
[https_github.com_orobix_retina-unet]: /images/20220109/5c6c11b514f24108993e1be3a58136c2.png
[GitHub stars 9]: https://camo.githubusercontent.com/a177334bcba216e514dcc3da9e0135f97895d013/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f6f726f6269782f726574696e612d756e65742e7376673f6c6f676f3d676974687562266c6162656c3d5374617273
[https_github.com_qureai_ultrasound-nerve-segmentation-using-torchnet]: https://github.com/qureai/ultrasound-nerve-segmentation-using-torchnet
[GitHub stars 10]: https://camo.githubusercontent.com/4604326234d54b83b4bf25bc56aca922d9dabb8e/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7175726561692f756c747261736f756e642d6e657276652d7365676d656e746174696f6e2d7573696e672d746f7263686e65742e7376673f6c6f676f3d676974687562266c6162656c3d5374617273
[https_github.com_ternaus_TernausNet]: /images/20220109/b6db3feab8cf4b6fa7145dbb70ef2f6d.png
[GitHub stars 11]: https://camo.githubusercontent.com/5481256e0298b436f269ec1da8ddb624a0a596ae/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7465726e6175732f5465726e6175734e65742e7376673f6c6f676f3d676974687562266c6162656c3d5374617273
[https_github.com_qubvel_segmentation_models]: /images/20220109/88d1636470e942d693f5b4a940da34d8.png
[GitHub stars 12]: https://camo.githubusercontent.com/8a0664767566019be860ed02ccf70d2b3df62d95/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f71756276656c2f7365676d656e746174696f6e5f6d6f64656c732e7376673f6c6f676f3d676974687562266c6162656c3d5374617273
[https_github.com_LeeJunHyun_Image_Segmentation_u-net]: /images/20220109/93e69e64f84a4473956a50aa37bbba8d.png#u-net
[GitHub stars 13]: https://camo.githubusercontent.com/12c85ad6290e6dc89c5f97d029e17136f32a84ba/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f4c65654a756e4879756e2f496d6167655f5365676d656e746174696f6e2e7376673f6c6f676f3d676974687562266c6162656c3d5374617273
[GitHub stars_GitHub stars 13]: /images/20220109/93e69e64f84a4473956a50aa37bbba8d.png
[https_lmb.informatik.uni-freiburg.de_people_ronneber_u-net]: https://lmb.informatik.uni-freiburg.de/people/ronneber/u-net/
[https_arxiv.org_pdf_1511.00561.pdf]: https://arxiv.org/pdf/1511.00561.pdf
[https_github.com_alexgkendall_caffe-segnet]: https://github.com/alexgkendall/caffe-segnet
[https_github.com_developmentseed_caffe_tree_segnet-multi-gpu]: https://github.com/developmentseed/caffe/tree/segnet-multi-gpu
[https_github.com_preddy5_segnet]: https://github.com/preddy5/segnet
[https_github.com_imlab-uiip_keras-segnet]: https://github.com/imlab-uiip/keras-segnet
[https_github.com_andreaazzini_segnet]: https://github.com/andreaazzini/segnet
[https_github.com_fedor-chervinskii_segnet-torch]: https://github.com/fedor-chervinskii/segnet-torch
[https_github.com_0bserver07_Keras-SegNet-Basic]: https://github.com/0bserver07/Keras-SegNet-Basic
[https_github.com_tkuanlun350_Tensorflow-SegNet]: https://github.com/tkuanlun350/Tensorflow-SegNet
[https_github.com_chainer_chainercv_tree_master_examples_segnet]: https://github.com/chainer/chainercv/tree/master/examples/segnet
[https_github.com_ykamikawa_keras-SegNet]: https://github.com/ykamikawa/keras-SegNet
[https_github.com_ykamikawa_tf-keras-SegNet]: https://github.com/ykamikawa/tf-keras-SegNet
[https_arxiv.org_pdf_1606.00915.pdf]: https://arxiv.org/pdf/1606.00915.pdf
[https_bitbucket.org_deeplab_deeplab-public]: https://bitbucket.org/deeplab/deeplab-public/
[https_github.com_cdmh_deeplab-public]: https://github.com/cdmh/deeplab-public
[https_bitbucket.org_aquariusjay_deeplab-public-ver2]: https://bitbucket.org/aquariusjay/deeplab-public-ver2
[https_github.com_TheLegendAli_DeepLab-Context]: https://github.com/TheLegendAli/DeepLab-Context
[https_github.com_msracver_Deformable-ConvNets_tree_master_deeplab]: https://github.com/msracver/Deformable-ConvNets/tree/master/deeplab
[https_github.com_DrSleep_tensorflow-deeplab-resnet]: https://github.com/DrSleep/tensorflow-deeplab-resnet
[https_github.com_muyang0320_tensorflow-deeplab-resnet-crf]: https://github.com/muyang0320/tensorflow-deeplab-resnet-crf
[https_github.com_isht7_pytorch-deeplab-resnet]: https://github.com/isht7/pytorch-deeplab-resnet
[https_github.com_bermanmaxim_jaccardSegment]: https://github.com/bermanmaxim/jaccardSegment
[https_github.com_martinkersner_train-DeepLab]: https://github.com/martinkersner/train-DeepLab
[https_github.com_chenxi116_TF-deeplab]: https://github.com/chenxi116/TF-deeplab
[https_github.com_bonlime_keras-deeplab-v3-plus]: https://github.com/bonlime/keras-deeplab-v3-plus
[https_github.com_tensorflow_models_tree_master_research_deeplab]: https://github.com/tensorflow/models/tree/master/research/deeplab
[https_github.com_speedinghzl_pytorch-segmentation-toolbox]: https://github.com/speedinghzl/pytorch-segmentation-toolbox
[https_github.com_kazuto1011_deeplab-pytorch]: https://github.com/kazuto1011/deeplab-pytorch
[https_github.com_youansheng_torchcv]: https://github.com/youansheng/torchcv
[https_arxiv.org_pdf_1605.06211.pdf]: https://arxiv.org/pdf/1605.06211.pdf
[https_github.com_vlfeat_matconvnet-fcn]: https://github.com/vlfeat/matconvnet-fcn
[https_github.com_shelhamer_fcn.berkeleyvision.org]: https://github.com/shelhamer/fcn.berkeleyvision.org
[https_github.com_MarvinTeichmann_tensorflow-fcn]: https://github.com/MarvinTeichmann/tensorflow-fcn
[https_github.com_aurora95_Keras-FCN]: https://github.com/aurora95/Keras-FCN
[https_github.com_mzaradzki_neuralnets_tree_master_vgg_segmentation_keras]: https://github.com/mzaradzki/neuralnets/tree/master/vgg_segmentation_keras
[https_github.com_k3nt0w_FCN_via_keras]: https://github.com/k3nt0w/FCN_via_keras
[https_github.com_shekkizh_FCN.tensorflow]: https://github.com/shekkizh/FCN.tensorflow
[https_github.com_seewalker_tf-pixelwise]: https://github.com/seewalker/tf-pixelwise
[https_github.com_wkentaro_pytorch-fcn]: https://github.com/wkentaro/pytorch-fcn
[https_github.com_wkentaro_fcn]: https://github.com/wkentaro/fcn
[https_github.com_apache_incubator-mxnet_tree_master_example_fcn-xs]: https://github.com/apache/incubator-mxnet/tree/master/example/fcn-xs
[https_github.com_muyang0320_tf-fcn]: https://github.com/muyang0320/tf-fcn
[https_github.com_ycszen_pytorch-seg]: https://github.com/ycszen/pytorch-seg
[https_github.com_Kaixhin_FCN-semantic-segmentation]: https://github.com/Kaixhin/FCN-semantic-segmentation
[https_github.com_petrama_VGGSegmentation]: https://github.com/petrama/VGGSegmentation
[https_github.com_simonguist_testing-fcn-for-cityscapes]: https://github.com/simonguist/testing-fcn-for-cityscapes
[https_github.com_hellochick_semantic-segmentation-tensorflow]: https://github.com/hellochick/semantic-segmentation-tensorflow
[https_github.com_pierluigiferrari_fcn8s_tensorflow]: https://github.com/pierluigiferrari/fcn8s_tensorflow
[https_github.com_theduynguyen_Keras-FCN]: https://github.com/theduynguyen/Keras-FCN
[https_github.com_JihongJu_keras-fcn]: https://github.com/JihongJu/keras-fcn
[https_arxiv.org_pdf_1606.02147.pdf]: https://arxiv.org/pdf/1606.02147.pdf
[https_github.com_TimoSaemann_ENet]: https://github.com/TimoSaemann/ENet
[https_github.com_e-lab_ENet-training]: https://github.com/e-lab/ENet-training
[https_github.com_PavlosMelissinos_enet-keras]: https://github.com/PavlosMelissinos/enet-keras
[https_github.com_fregu856_segmentation]: https://github.com/fregu856/segmentation
[https_github.com_kwotsin_TensorFlow-ENet]: https://github.com/kwotsin/TensorFlow-ENet
[https_github.com_davidtvs_PyTorch-ENet]: https://github.com/davidtvs/PyTorch-ENet
[https_arxiv.org_pdf_1707.03718.pdf]: https://arxiv.org/pdf/1707.03718.pdf
[https_github.com_e-lab_LinkNet]: https://github.com/e-lab/LinkNet
[https_arxiv.org_pdf_1608.06993.pdf]: https://arxiv.org/pdf/1608.06993.pdf
[https_github.com_flyyufelix_DenseNet-Keras]: https://github.com/flyyufelix/DenseNet-Keras
[https_arxiv.org_pdf_1611.09326.pdf]: https://arxiv.org/pdf/1611.09326.pdf
[https_github.com_0bserver07_One-Hundred-Layers-Tiramisu]: https://github.com/0bserver07/One-Hundred-Layers-Tiramisu
[https_github.com_SimJeg_FC-DenseNet]: https://github.com/SimJeg/FC-DenseNet
[https_arxiv.org_pdf_1511.07122.pdf]: https://arxiv.org/pdf/1511.07122.pdf
[https_github.com_nicolov_segmentation_keras]: https://github.com/nicolov/segmentation_keras
[https_github.com_fyu_dilation]: https://github.com/fyu/dilation
[https_github.com_fyu_drn_semantic-image-segmentataion]: https://github.com/fyu/drn#semantic-image-segmentataion
[https_github.com_hangzhaomit_semantic-segmentation-pytorch]: https://github.com/hangzhaomit/semantic-segmentation-pytorch
[https_arxiv.org_pdf_1609.06694.pdf]: https://arxiv.org/pdf/1609.06694.pdf
[https_github.com_aayushbansal_PixelNet]: https://github.com/aayushbansal/PixelNet
[https_arxiv.org_pdf_1704.08545.pdf]: https://arxiv.org/pdf/1704.08545.pdf
[https_github.com_hszhao_ICNet]: https://github.com/hszhao/ICNet
[https_github.com_ai-tor_Keras-ICNet]: https://github.com/ai-tor/Keras-ICNet
[https_github.com_hellochick_ICNet-tensorflow]: https://github.com/hellochick/ICNet-tensorflow
[https_github.com_oandrienko_fast-semantic-segmentation]: https://github.com/oandrienko/fast-semantic-segmentation
[http_www.robesafe.uah.es_personal_eduardo.romera_pdfs_Romera17iv.pdf]: http://www.robesafe.uah.es/personal/eduardo.romera/pdfs/Romera17iv.pdf
[https_github.com_Eromera_erfnet]: https://github.com/Eromera/erfnet
[https_github.com_Eromera_erfnet_pytorch]: https://github.com/Eromera/erfnet_pytorch
[https_arxiv.org_pdf_1611.06612.pdf]: https://arxiv.org/pdf/1611.06612.pdf
[https_github.com_guosheng_refinenet]: https://github.com/guosheng/refinenet
[https_arxiv.org_pdf_1612.01105.pdf_https_hszhao.github.io_projects_pspnet]: https://arxiv.org/pdf/1612.01105.pdf,https://hszhao.github.io/projects/pspnet/
[https_github.com_hszhao_PSPNet]: https://github.com/hszhao/PSPNet
[https_github.com_mitmul_chainer-pspnet]: https://github.com/mitmul/chainer-pspnet
[https_github.com_Vladkryvoruchko_PSPNet-Keras-tensorflow]: https://github.com/Vladkryvoruchko/PSPNet-Keras-tensorflow
[https_github.com_pudae_tensorflow-pspnet]: https://github.com/pudae/tensorflow-pspnet
[https_github.com_hellochick_PSPNet-tensorflow]: https://github.com/hellochick/PSPNet-tensorflow
[https_arxiv.org_pdf_1505.04366.pdf]: https://arxiv.org/pdf/1505.04366.pdf
[http_cvlab.postech.ac.kr_research_deconvnet]: http://cvlab.postech.ac.kr/research/deconvnet/
[https_github.com_HyeonwooNoh_DeconvNet]: https://github.com/HyeonwooNoh/DeconvNet
[https_github.com_fabianbormann_Tensorflow-DeconvNet-Segmentation]: https://github.com/fabianbormann/Tensorflow-DeconvNet-Segmentation
[https_arxiv.org_pdf_1611.08323.pdf]: https://arxiv.org/pdf/1611.08323.pdf
[https_github.com_TobyPDE_FRRN]: https://github.com/TobyPDE/FRRN
[https_arxiv.org_pdf_1703.02719.pdf]: https://arxiv.org/pdf/1703.02719.pdf
[https_arxiv.org_pdf_1605.02264.pdf]: https://arxiv.org/pdf/1605.02264.pdf
[https_github.com_golnazghiasi_LRR]: https://github.com/golnazghiasi/LRR
[https_arxiv.org_pdf_1702.08502.pdf]: https://arxiv.org/pdf/1702.08502.pdf
[https_arxiv.org_pdf_1612.07695.pdf]: https://arxiv.org/pdf/1612.07695.pdf
[https_github.com_MarvinTeichmann_MultiNet]: https://github.com/MarvinTeichmann/MultiNet
[https_github.com_MarvinTeichmann_KittiSeg]: https://github.com/MarvinTeichmann/KittiSeg
[https_arxiv.org_pdf_1708.04607.pdf]: https://arxiv.org/pdf/1708.04607.pdf
[https_github.com_aharley_segaware]: https://github.com/aharley/segaware
[https_arxiv.org_pdf_1611.08408.pdf]: https://arxiv.org/pdf/1611.08408.pdf
[https_github.com_oyam_Semantic-Segmentation-using-Adversarial-Networks]: https://github.com/oyam/Semantic-Segmentation-using-Adversarial-Networks
[https_arxiv.org_pdf_1705.06820.pdf]: https://arxiv.org/pdf/1705.06820.pdf
[https_github.com_HongyangGao_PixelDCN]: https://github.com/HongyangGao/PixelDCN
[https_arxiv.org_pdf_1803.03816.pdf]: https://arxiv.org/pdf/1803.03816.pdf
[https_github.com_MSiam_TFSegmentation]: https://github.com/MSiam/TFSegmentation
[https_arxiv.org_pdf_1802.10349.pdf]: https://arxiv.org/pdf/1802.10349.pdf
[https_github.com_wasidennis_AdaptSegNet]: https://github.com/wasidennis/AdaptSegNet
[https_github.com_TuSimple_TuSimple-DUC]: https://github.com/TuSimple/TuSimple-DUC
[http_presentations.cocodataset.org_COCO17-Stuff-FAIR.pdf]: http://presentations.cocodataset.org/COCO17-Stuff-FAIR.pdf
[https_arxiv.org_ftp_arxiv_papers_1802_1802.06955.pdf]: https://arxiv.org/ftp/arxiv/papers/1802/1802.06955.pdf
[https_github.com_LeeJunHyun_Image_Segmentation_r2u-net]: /images/20220109/93e69e64f84a4473956a50aa37bbba8d.png#r2u-net
[https_arxiv.org_pdf_1804.03999.pdf]: https://arxiv.org/pdf/1804.03999.pdf
[https_github.com_LeeJunHyun_Image_Segmentation_attention-u-net]: /images/20220109/93e69e64f84a4473956a50aa37bbba8d.png#attention-u-net
[https_github.com_ozan-oktay_Attention-Gated-Networks]: https://github.com/ozan-oktay/Attention-Gated-Networks
[https_arxiv.org_pdf_1809.02983.pdf]: https://arxiv.org/pdf/1809.02983.pdf
[https_github.com_junfu1115_DANet]: https://github.com/junfu1115/DANet
[https_arxiv.org_pdf_1811.11254.pdf]: https://arxiv.org/pdf/1811.11254.pdf
[https_github.com_juntang-zhuang_ShelfNet]: https://github.com/juntang-zhuang/ShelfNet
[https_arxiv.org_pdf_1810.07810.pdf]: https://arxiv.org/pdf/1810.07810.pdf
[https_github.com_juntang-zhuang_LadderNet]: https://github.com/juntang-zhuang/LadderNet
[https_arxiv.org_pdf_1808.00897.pdf]: https://arxiv.org/pdf/1808.00897.pdf
[https_github.com_ooooverflow_BiSeNet]: https://github.com/ooooverflow/BiSeNet
[https_github.com_ycszen_TorchSeg]: https://github.com/ycszen/TorchSeg
[https_arxiv.org_pdf_1803.06815.pdf]: https://arxiv.org/pdf/1803.06815.pdf
[https_github.com_sacmehta_ESPNet]: https://github.com/sacmehta/ESPNet
[https_arxiv.org_pdf_1804.09337.pdf]: https://arxiv.org/pdf/1804.09337.pdf
[https_arxiv.org_pdf_1811.11721.pdf]: https://arxiv.org/pdf/1811.11721.pdf
[https_github.com_speedinghzl_CCNet]: https://github.com/speedinghzl/CCNet
[http_openaccess.thecvf.com_content_cvpr_2018_papers_Yang_DenseASPP_for_Semantic_CVPR_2018_paper.pdf]: http://openaccess.thecvf.com/content_cvpr_2018/papers/Yang_DenseASPP_for_Semantic_CVPR_2018_paper.pdf
[https_arxiv.org_pdf_1611.07709.pdf]: https://arxiv.org/pdf/1611.07709.pdf
[https_github.com_msracver_FCIS]: https://github.com/msracver/FCIS
[https_arxiv.org_pdf_1512.04412.pdf]: https://arxiv.org/pdf/1512.04412.pdf
[https_github.com_daijifeng001_MNC]: https://github.com/daijifeng001/MNC
[https_arxiv.org_pdf_1506.06204.pdf]: https://arxiv.org/pdf/1506.06204.pdf
[https_github.com_facebookresearch_deepmask]: https://github.com/facebookresearch/deepmask
[https_arxiv.org_pdf_1603.08695.pdf]: https://arxiv.org/pdf/1603.08695.pdf
[https_arxiv.org_pdf_1703.06870.pdf]: https://arxiv.org/pdf/1703.06870.pdf
[https_github.com_CharlesShang_FastMaskRCNN]: https://github.com/CharlesShang/FastMaskRCNN
[https_github.com_jasjeetIM_Mask-RCNN]: https://github.com/jasjeetIM/Mask-RCNN
[https_github.com_TuSimple_mx-maskrcnn]: https://github.com/TuSimple/mx-maskrcnn
[https_github.com_matterport_Mask_RCNN]: https://github.com/matterport/Mask_RCNN
[https_github.com_facebookresearch_maskrcnn-benchmark]: https://github.com/facebookresearch/maskrcnn-benchmark
[https_arxiv.org_pdf_1511.08250.pdf]: https://arxiv.org/pdf/1511.08250.pdf
[https_github.com_bernard24_RIS]: https://github.com/bernard24/RIS
[https_arxiv.org_pdf_1612.08843.pdf]: https://arxiv.org/pdf/1612.08843.pdf
[https_github.com_voidrank_FastMask]: https://github.com/voidrank/FastMask
[https_arxiv.org_pdf_1708.02813.pdf]: https://arxiv.org/pdf/1708.02813.pdf
[https_github.com_dvornikita_blitznet]: https://github.com/dvornikita/blitznet
[https_arxiv.org_pdf_1803.01534.pdf]: https://arxiv.org/pdf/1803.01534.pdf
[https_github.com_ShuLiu1993_PANet]: https://github.com/ShuLiu1993/PANet
[https_arxiv.org_pdf_1806.00844.pdf]: https://arxiv.org/pdf/1806.00844.pdf
[https_github.com_ternaus_TernausNetV2]: /images/20220109/b6db3feab8cf4b6fa7145dbb70ef2f6d.pngV2
[https_arxiv.org_pdf_1603.06098.pdf]: https://arxiv.org/pdf/1603.06098.pdf
[https_github.com_kolesman_SEC]: https://github.com/kolesman/SEC
[https_arxiv.org_pdf_1505.00393.pdf]: https://arxiv.org/pdf/1505.00393.pdf
[https_github.com_fvisin_reseg]: https://github.com/fvisin/reseg
[https_arxiv.org_pdf_1511.07053.pdf]: https://arxiv.org/pdf/1511.07053.pdf
[https_github.com_Wizaron_reseg-pytorch]: https://github.com/Wizaron/reseg-pytorch
[http_www.robots.ox.ac.uk_7Eszheng_papers_CRFasRNN.pdf]: http://www.robots.ox.ac.uk/~szheng/papers/CRFasRNN.pdf
[https_github.com_martinkersner_train-CRF-RNN]: https://github.com/martinkersner/train-CRF-RNN
[https_github.com_torrvision_crfasrnn]: https://github.com/torrvision/crfasrnn
[https_github.com_NP-coder_CLPS1520Project]: https://github.com/NP-coder/CLPS1520Project
[https_github.com_renmengye_rec-attend-public]: https://github.com/renmengye/rec-attend-public
[https_github.com_sadeepj_crfasrnn_keras]: https://github.com/sadeepj/crfasrnn_keras
[https_arxiv.org_pdf_1711.11585.pdf]: https://arxiv.org/pdf/1711.11585.pdf
[https_github.com_NVIDIA_pix2pixHD]: https://github.com/NVIDIA/pix2pixHD
[https_arxiv.org_pdf_1806.05034.pdf]: https://arxiv.org/pdf/1806.05034.pdf
[https_github.com_SimonKohl_probabilistic_unet]: https://github.com/SimonKohl/probabilistic_unet
[https_github.com_cvlab-epfl_densecrf]: https://github.com/cvlab-epfl/densecrf
[http_vladlen.info_publications_efficient-inference-in-fully-connected-crfs-with-gaussian-edge-potentials]: http://vladlen.info/publications/efficient-inference-in-fully-connected-crfs-with-gaussian-edge-potentials/
[http_www.philkr.net_home_densecrf]: http://www.philkr.net/home/densecrf
[http_graphics.stanford.edu_projects_densecrf]: http://graphics.stanford.edu/projects/densecrf/
[https_github.com_amiltonwong_segmentation_blob_master_segmentation.ipynb]: https://github.com/amiltonwong/segmentation/blob/master/segmentation.ipynb
[https_github.com_jliemansifry_super-simple-semantic-segmentation]: https://github.com/jliemansifry/super-simple-semantic-segmentation
[http_users.cecs.anu.edu.au_jdomke_JGMT]: http://users.cecs.anu.edu.au/~jdomke/JGMT/
[https_www.quora.com_How-can-one-train-and-test-conditional-random-field-CRF-in-Python-on-our-own-training-testing-dataset]: https://www.quora.com/How-can-one-train-and-test-conditional-random-field-CRF-in-Python-on-our-own-training-testing-dataset
[https_github.com_tpeng_python-crfsuite]: https://github.com/tpeng/python-crfsuite
[https_github.com_chokkan_crfsuite]: https://github.com/chokkan/crfsuite
[https_sites.google.com_site_zeppethefake_semantic-segmentation-crf-baseline]: https://sites.google.com/site/zeppethefake/semantic-segmentation-crf-baseline
[https_github.com_lucasb-eyer_pydensecrf]: https://github.com/lucasb-eyer/pydensecrf
[Stanford Background Dataset]: http://dags.stanford.edu/projects/scenedataset.html
[Sift Flow Dataset]: http://people.csail.mit.edu/celiu/SIFTflow/
[Barcelona Dataset]: http://www.cs.unc.edu/~jtighe/Papers/ECCV10/
[Microsoft COCO dataset]: http://mscoco.org/
[MSRC Dataset]: http://research.microsoft.com/en-us/projects/objectclassrecognition/
[LITS Liver Tumor Segmentation Dataset]: https://competitions.codalab.org/competitions/15595
[KITTI]: http://www.cvlibs.net/datasets/kitti/eval_road.php
[Pascal Context]: http://www.cs.stanford.edu/~roozbeh/pascal-context/
[Data from Games dataset]: https://download.visinf.tu-darmstadt.de/data/from_games/
[Human parsing dataset]: https://github.com/lemondan/HumanParsing-Dataset
[Mapillary Vistas Dataset]: https://www.mapillary.com/dataset/vistas
[Microsoft AirSim]: https://github.com/Microsoft/AirSim
[MIT Scene Parsing Benchmark]: http://sceneparsing.csail.mit.edu/
[COCO 2017 Stuff Segmentation Challenge]: http://cocodataset.org/#stuff-challenge2017
[ADE20K Dataset]: http://groups.csail.mit.edu/vision/datasets/ADE20K/
[INRIA Annotations for Graz-02]: http://lear.inrialpes.fr/people/marszalek/data/ig02/
[Daimler dataset]: http://www.gavrila.net/Datasets/Daimler_Pedestrian_Benchmark_D/daimler_pedestrian_benchmark_d.html
[ISBI Challenge_ Segmentation of neuronal structures in EM stacks]: http://brainiac2.mit.edu/isbi_challenge/
[INRIA Annotations for Graz-02 _IG02]: https://lear.inrialpes.fr/people/marszalek/data/ig02/
[Pratheepan Dataset]: http://cs-chan.com/downloads_skin_dataset.html
[Clothing Co-Parsing _CCP_ Dataset]: https://github.com/bearpaw/clothing-co-parsing
[Inria Aerial Image]: https://project.inria.fr/aerialimagelabeling/
[ApolloScape]: http://apolloscape.auto/scene.html
[UrbanMapper3D]: https://community.topcoder.com/longcontest/?module=ViewProblemStatement&rd=17007&pm=14703
[RoadDetector]: https://community.topcoder.com/longcontest/?module=ViewProblemStatement&rd=17036&pm=14735
[Cityscapes]: https://www.cityscapes-dataset.com/
[CamVid]: http://mi.eng.cam.ac.uk/research/projects/VideoRec/CamVid/
[https_github.com_meetshah1995_pytorch-semseg]: https://github.com/meetshah1995/pytorch-semseg
[https_github.com_GeorgeSeif_Semantic-Segmentation-Suite]: https://github.com/GeorgeSeif/Semantic-Segmentation-Suite
[https_github.com_CSAILVision_sceneparsing]: https://github.com/CSAILVision/sceneparsing
[https_github.com_BloodAxe_segmentation-networks-benchmark]: https://github.com/BloodAxe/segmentation-networks-benchmark
[https_github.com_warmspringwinds_pytorch-segmentation-detection]: https://github.com/warmspringwinds/pytorch-segmentation-detection
[https_github.com_qubvel_segmentation_models.pytorch]: /images/20220109/88d1636470e942d693f5b4a940da34d8.png.pytorch
[https_github.com_mrgloom_keras-semantic-segmentation-example]: https://github.com/mrgloom/keras-semantic-segmentation-example
[https_github.com_AKSHAYUBHAT_ImageSegmentation]: https://github.com/AKSHAYUBHAT/ImageSegmentation
[https_github.com_kyamagu_js-segment-annotator]: https://github.com/kyamagu/js-segment-annotator
[https_github.com_CSAILVision_LabelMeAnnotationTool]: https://github.com/CSAILVision/LabelMeAnnotationTool
[https_github.com_seanbell_opensurfaces-segmentation-ui]: https://github.com/seanbell/opensurfaces-segmentation-ui
[https_github.com_lzx1413_labelImgPlus]: https://github.com/lzx1413/labelImgPlus
[https_github.com_wkentaro_labelme]: https://github.com/wkentaro/labelme
[https_github.com_labelbox_labelbox]: https://github.com/labelbox/labelbox
[https_github.com_Deep-Magic_COCO-Style-Dataset-Generator-GUI]: https://github.com/Deep-Magic/COCO-Style-Dataset-Generator-GUI
[https_github.com_Labelbox_Labelbox]: https://github.com/Labelbox/Labelbox
[https_github.com_opencv_cvat]: https://github.com/opencv/cvat
[MSRC-21]: http://rodrigob.github.io/are_we_there_yet/build/semantic_labeling_datasets_results.html
[Cityscapes 1]: https://www.cityscapes-dataset.com/benchmarks/
[VOC2012]: http://host.robots.ox.ac.uk:8080/leaderboard/displaylb.php?challengeid=11&compid=6
[https_paperswithcode.com_task_semantic-segmentation]: https://paperswithcode.com/task/semantic-segmentation
[https_github.com_martinkersner_py_img_seg_eval]: https://github.com/martinkersner/py_img_seg_eval
[http_www.cs.umanitoba.ca_ywang_papers_isvc16.pdf]: http://www.cs.umanitoba.ca/~ywang/papers/isvc16.pdf
[https_arxiv.org_pdf_1705.08790.pdf]: https://arxiv.org/pdf/1705.08790.pdf
[https_arxiv.org_pdf_1707.03237.pdf]: https://arxiv.org/pdf/1707.03237.pdf
[http_www.bmva.org_bmvc_2013_Papers_paper0032_paper0032.pdf]: http://www.bmva.org/bmvc/2013/Papers/paper0032/paper0032.pdf
[https_github.com_tangzhenyu_SemanticSegmentation_DL]: https://github.com/tangzhenyu/SemanticSegmentation_DL
[https_github.com_nightrome_really-awesome-semantic-segmentation]: https://github.com/nightrome/really-awesome-semantic-segmentation
[https_github.com_JackieZhangdx_InstanceSegmentationList]: https://github.com/JackieZhangdx/InstanceSegmentationList
[https_github.com_NVIDIA_DIGITS_tree_master_examples_medical-imaging]: https://github.com/NVIDIA/DIGITS/tree/master/examples/medical-imaging
[http_lmb.informatik.uni-freiburg.de_people_ronneber_u-net]: http://lmb.informatik.uni-freiburg.de/people/ronneber/u-net/
[https_github.com_dmlc_mxnet_issues_1514]: https://github.com/dmlc/mxnet/issues/1514
[https_github.com_yulequan_melanoma-recognition]: https://github.com/yulequan/melanoma-recognition
[http_www.andrewjanowczyk.com_use-case-1-nuclei-segmentation]: http://www.andrewjanowczyk.com/use-case-1-nuclei-segmentation/
[https_github.com_junyanz_MCILBoost]: https://github.com/junyanz/MCILBoost
[https_github.com_imlab-uiip_lung-segmentation-2d]: https://github.com/imlab-uiip/lung-segmentation-2d
[https_github.com_scottykwok_cervix-roi-segmentation-by-unet]: https://github.com/scottykwok/cervix-roi-segmentation-by-unet
[https_github.com_WeidiXie_cell_counting_v2]: https://github.com/WeidiXie/cell_counting_v2
[https_github.com_yandexdataschool_YSDA_deeplearning17_blob_master_Seminar6_Seminar_206_20-_20segmentation.ipynb]: https://github.com/yandexdataschool/YSDA_deeplearning17/blob/master/Seminar6/Seminar%206%20-%20segmentation.ipynb
[https_github.com_IBBM_Cascaded-FCN]: https://github.com/IBBM/Cascaded-FCN
[https_github.com_intact-project_ild-cnn]: https://github.com/intact-project/ild-cnn
[https_github.com_lishen_end2end-all-conv]: https://github.com/lishen/end2end-all-conv
[https_github.com_imatge-upc_liverseg-2017-nipsws]: https://github.com/imatge-upc/liverseg-2017-nipsws
[https_github.com_DLTK_DLTK_tree_master_examples_applications_MRBrainS13_tissue_segmentation]: https://github.com/DLTK/DLTK/tree/master/examples/applications/MRBrainS13_tissue_segmentation
[https_github.com_ecobost_cnn4brca]: https://github.com/ecobost/cnn4brca
[https_www2.warwick.ac.uk_fac_sci_dcs_people_research_csrkbb_tmi2016_ks.pdf]: https://www2.warwick.ac.uk/fac/sci/dcs/people/research/csrkbb/tmi2016_ks.pdf
[http_people.idsia.ch_juergen_nips2012.pdf]: http://people.idsia.ch/~juergen/nips2012.pdf
[https_github.com_albarqouni_Deep-Learning-for-Medical-Applications_segmentation]: https://github.com/albarqouni/Deep-Learning-for-Medical-Applications#segmentation
[https_luna16.grand-challenge.org]: https://luna16.grand-challenge.org/
[https_camelyon16.grand-challenge.org]: https://camelyon16.grand-challenge.org/
[https_github.com_beamandrew_medical-data]: https://github.com/beamandrew/medical-data
[https_github.com_mshivaprakash_sat-seg-thesis]: https://github.com/mshivaprakash/sat-seg-thesis
[https_github.com_KGPML_Hyperspectral]: https://github.com/KGPML/Hyperspectral
[https_github.com_lopuhin_kaggle-dstl]: https://github.com/lopuhin/kaggle-dstl
[https_github.com_mitmul_ssai]: https://github.com/mitmul/ssai
[https_github.com_mitmul_ssai-cnn]: https://github.com/mitmul/ssai-cnn
[https_github.com_azavea_raster-vision]: https://github.com/azavea/raster-vision
[https_github.com_nshaud_DeepNetsForEO]: https://github.com/nshaud/DeepNetsForEO
[https_github.com_trailbehind_DeepOSM]: https://github.com/trailbehind/DeepOSM
[https_github.com_mapbox_robosat]: https://github.com/mapbox/robosat
[https_github.com_datapink_robosat.pink]: https://github.com/datapink/robosat.pink
[https_github.com_RSIA-LIESMARS-WHU_RSOD-Dataset-]: https://github.com/RSIA-LIESMARS-WHU/RSOD-Dataset-
[https_spacenetchallenge.github.io]: https://spacenetchallenge.github.io/
[https_github.com_chrieke_awesome-satellite-imagery-datasets]: https://github.com/chrieke/awesome-satellite-imagery-datasets
[https_github.com_shelhamer_clockwork-fcn]: https://github.com/shelhamer/clockwork-fcn
[https_github.com_JingchunCheng_Seg-with-SPN]: https://github.com/JingchunCheng/Seg-with-SPN
[https_github.com_vxy10_p5_VehicleDetection_Unet]: https://github.com/vxy10/p5_VehicleDetection_Unet
[https_github.com_ndrplz_self-driving-car]: https://github.com/ndrplz/self-driving-car
[https_github.com_mvirgo_MLND-Capstone]: https://github.com/mvirgo/MLND-Capstone
[https_github.com_zhujun98_semantic_segmentation_tree_master_fcn8s_road]: https://github.com/zhujun98/semantic_segmentation/tree/master/fcn8s_road
[https_github.com_MaybeShewill-CV_lanenet-lane-detection]: https://github.com/MaybeShewill-CV/lanenet-lane-detection
[https_github.com_gakarak_FCN_MSCOCO_Food_Segmentation]: https://github.com/gakarak/FCN_MSCOCO_Food_Segmentation
[https_github.com_abbypa_NNProject_DeepMask]: https://github.com/abbypa/NNProject_DeepMask
[https_github.com_warmspringwinds_tf-image-segmentation]: https://github.com/warmspringwinds/tf-image-segmentation
[https_github.com_xiaolonw_nips14_loc_seg_testonly]: https://github.com/xiaolonw/nips14_loc_seg_testonly
[https_github.com_naibaf7_caffe_neural_tool]: https://github.com/naibaf7/caffe_neural_tool
[https_github.com_erogol_seg-torch]: https://github.com/erogol/seg-torch
[https_github.com_phillipi_pix2pix]: https://github.com/phillipi/pix2pix
[https_github.com_itijyou_ademxapp]: https://github.com/itijyou/ademxapp
[http_www.eecs.berkeley.edu_Research_Projects_CS_vision_shape_sds]: http://www.eecs.berkeley.edu/Research/Projects/CS/vision/shape/sds/
[https_github.com_bharath272_sds_eccv2014]: https://github.com/bharath272/sds_eccv2014
[https_github.com_HyeonwooNoh_DecoupledNet]: https://github.com/HyeonwooNoh/DecoupledNet
[http_vladlen.info_publications_learning-to-propose-objects]: http://vladlen.info/publications/learning-to-propose-objects/
[https_github.com_philkr_lpo]: https://github.com/philkr/lpo
[http_people.csail.mit.edu_celiu_LabelTransfer_code.html]: http://people.csail.mit.edu/celiu/LabelTransfer/code.html
[https_github.com_cvjena_cn24]: https://github.com/cvjena/cn24
[http_lmb.informatik.uni-freiburg.de_resources_software.php]: http://lmb.informatik.uni-freiburg.de/resources/software.php
[https_github.com_NVIDIA_DIGITS_tree_master_examples_semantic-segmentation]: https://github.com/NVIDIA/DIGITS/tree/master/examples/semantic-segmentation
[http_jamie.shotton.org_work_code.html]: http://jamie.shotton.org/work/code.html
[https_github.com_amueller_textonboost]: https://github.com/amueller/textonboost
[https_github.com_fchollet_keras_issues_6538]: https://github.com/fchollet/keras/issues/6538
[https_github.com_warmspringwinds_tensorflow_notes]: https://github.com/warmspringwinds/tensorflow_notes
[https_github.com_kjw0612_awesome-deep-vision_semantic-segmentation]: https://github.com/kjw0612/awesome-deep-vision#semantic-segmentation
[https_github.com_desimone_segmentation-models]: https://github.com/desimone/segmentation-models
[http_www.it-caesar.com_list-of-contemporary-semantic-segmentation-datasets]: http://www.it-caesar.com/list-of-contemporary-semantic-segmentation-datasets/
[https_github.com_MichaelXin_Awesome-Caffe_23-image-segmentation]: https://github.com/MichaelXin/Awesome-Caffe#23-image-segmentation
[https_github.com_neuropoly_axondeepseg]: https://github.com/neuropoly/axondeepseg
[https_github.com_petrochenko-pavel-a_segmentation_training_pipeline]: https://github.com/petrochenko-pavel-a/segmentation_training_pipeline
[https_handong1587.github.io_deep_learning_2015_10_09_segmentation.html]: https://handong1587.github.io/deep_learning/2015/10/09/segmentation.html
[http_www.andrewjanowczyk.com_efficient-pixel-wise-deep-learning-on-large-images]: http://www.andrewjanowczyk.com/efficient-pixel-wise-deep-learning-on-large-images/
[https_devblogs.nvidia.com_parallelforall_image-segmentation-using-digits-5]: https://devblogs.nvidia.com/parallelforall/image-segmentation-using-digits-5/
[https_github.com_NVIDIA_DIGITS_tree_master_examples_binary-segmentation]: https://github.com/NVIDIA/DIGITS/tree/master/examples/binary-segmentation
[http_blog.qure.ai_notes_semantic-segmentation-deep-learning-review]: http://blog.qure.ai/notes/semantic-segmentation-deep-learning-review
[https_medium.com_barvinograd1_instance-embedding-instance-segmentation-without-proposals-31946a7c53e1]: https://medium.com/@barvinograd1/instance-embedding-instance-segmentation-without-proposals-31946a7c53e1