└── README.md /README.md: -------------------------------------------------------------------------------- 1 | [![Awesome](https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)](https://github.com/sindresorhus/awesome) 2 | 3 | # Awesome Semantic Segmentation 4 | 5 | ## Networks by architecture 6 | ### Semantic segmentation 7 | - U-Net [https://arxiv.org/pdf/1505.04597.pdf] [2015] 8 | + https://github.com/zhixuhao/unet [Keras][![GitHub stars](https://img.shields.io/github/stars/zhixuhao/unet.svg?logo=github&label=Stars)](https://github.com/zhixuhao/unet) 9 | + https://github.com/jocicmarko/ultrasound-nerve-segmentation [Keras][![GitHub stars](https://img.shields.io/github/stars/jocicmarko/ultrasound-nerve-segmentation.svg?logo=github&label=Stars)](https://github.com/jocicmarko/ultrasound-nerve-segmentation) 10 | + https://github.com/EdwardTyantov/ultrasound-nerve-segmentation [Keras][![GitHub stars](https://img.shields.io/github/stars/EdwardTyantov/ultrasound-nerve-segmentation.svg?logo=github&label=Stars)](https://github.com/EdwardTyantov/ultrasound-nerve-segmentation) 11 | + https://github.com/ZFTurbo/ZF_UNET_224_Pretrained_Model [Keras][![GitHub stars](https://img.shields.io/github/stars/ZFTurbo/ZF_UNET_224_Pretrained_Model.svg?logo=github&label=Stars)](https://github.com/ZFTurbo/ZF_UNET_224_Pretrained_Model) 12 | + https://github.com/yihui-he/u-net [Keras][![GitHub stars](https://img.shields.io/github/stars/yihui-he/u-net.svg?logo=github&label=Stars)](https://github.com/yihui-he/u-net) 13 | + https://github.com/jakeret/tf_unet [Tensorflow][![GitHub stars](https://img.shields.io/github/stars/jakeret/tf_unet.svg?logo=github&label=Stars)](https://github.com/jakeret/tf_unet) 14 | + https://github.com/divamgupta/image-segmentation-keras [Keras][![GitHub stars](https://img.shields.io/github/stars/divamgupta/image-segmentation-keras.svg?logo=github&label=Stars)](https://github.com/divamgupta/image-segmentation-keras) 15 | + https://github.com/ZijunDeng/pytorch-semantic-segmentation [PyTorch][![GitHub stars](https://img.shields.io/github/stars/ZijunDeng/pytorch-semantic-segmentation.svg?logo=github&label=Stars)](https://github.com/ZijunDeng/pytorch-semantic-segmentation) 16 | + https://github.com/akirasosa/mobile-semantic-segmentation [Keras][![GitHub stars](https://img.shields.io/github/stars/akirasosa/mobile-semantic-segmentation.svg?logo=github&label=Stars)](https://github.com/akirasosa/mobile-semantic-segmentation) 17 | + https://github.com/orobix/retina-unet [Keras][![GitHub stars](https://img.shields.io/github/stars/orobix/retina-unet.svg?logo=github&label=Stars)](https://github.com/orobix/retina-unet) 18 | + https://github.com/qureai/ultrasound-nerve-segmentation-using-torchnet [Torch][![GitHub stars](https://img.shields.io/github/stars/qureai/ultrasound-nerve-segmentation-using-torchnet.svg?logo=github&label=Stars)](https://github.com/orobix/retina-unet) 19 | + https://github.com/ternaus/TernausNet [PyTorch][![GitHub stars](https://img.shields.io/github/stars/ternaus/TernausNet.svg?logo=github&label=Stars)](https://github.com/ternaus/TernausNet) 20 | + https://github.com/qubvel/segmentation_models [Keras][![GitHub stars](https://img.shields.io/github/stars/qubvel/segmentation_models.svg?logo=github&label=Stars)](https://github.com/qubvel/segmentation_models) 21 | + https://github.com/LeeJunHyun/Image_Segmentation#u-net [PyTorch][![GitHub stars](https://img.shields.io/github/stars/LeeJunHyun/Image_Segmentation.svg?logo=github&label=Stars)](https://github.com/LeeJunHyun/Image_Segmentation) 22 | + https://github.com/yassouali/pytorch_segmentation [PyTorch][![GitHub stars](https://img.shields.io/github/stars/yassouali/pytorch_segmentation.svg?logo=github&label=Stars)](https://github.com/yassouali/pytorch_segmentation) 23 | + https://lmb.informatik.uni-freiburg.de/people/ronneber/u-net/ [Caffe + Matlab] 24 | - SegNet [https://arxiv.org/pdf/1511.00561.pdf] [2016] 25 | + https://github.com/alexgkendall/caffe-segnet [Caffe] 26 | + https://github.com/developmentseed/caffe/tree/segnet-multi-gpu [Caffe] 27 | + https://github.com/preddy5/segnet [Keras] 28 | + https://github.com/imlab-uiip/keras-segnet [Keras] 29 | + https://github.com/andreaazzini/segnet [Tensorflow] 30 | + https://github.com/fedor-chervinskii/segnet-torch [Torch] 31 | + https://github.com/0bserver07/Keras-SegNet-Basic [Keras] 32 | + https://github.com/tkuanlun350/Tensorflow-SegNet [Tensorflow] 33 | + https://github.com/divamgupta/image-segmentation-keras [Keras] 34 | + https://github.com/ZijunDeng/pytorch-semantic-segmentation [PyTorch] 35 | + https://github.com/chainer/chainercv/tree/master/examples/segnet [Chainer] 36 | + https://github.com/ykamikawa/keras-SegNet [Keras] 37 | + https://github.com/ykamikawa/tf-keras-SegNet [Keras] 38 | + https://github.com/yassouali/pytorch_segmentation [PyTorch][![GitHub stars](https://img.shields.io/github/stars/yassouali/pytorch_segmentation)](https://github.com/yassouali/pytorch_segmentation) 39 | - DeepLab [https://arxiv.org/pdf/1606.00915.pdf] [2017] 40 | + https://bitbucket.org/deeplab/deeplab-public/ [Caffe] 41 | + https://bitbucket.org/aquariusjay/deeplab-public-ver2 [Caffe] 42 | + https://github.com/TheLegendAli/DeepLab-Context [Caffe] 43 | + https://github.com/msracver/Deformable-ConvNets/tree/master/deeplab [MXNet] 44 | + https://github.com/DrSleep/tensorflow-deeplab-resnet [Tensorflow] 45 | + https://github.com/muyang0320/tensorflow-deeplab-resnet-crf [TensorFlow] 46 | + https://github.com/isht7/pytorch-deeplab-resnet [PyTorch] 47 | + https://github.com/bermanmaxim/jaccardSegment [PyTorch] 48 | + https://github.com/martinkersner/train-DeepLab [Caffe] 49 | + https://github.com/chenxi116/TF-deeplab [Tensorflow] 50 | + https://github.com/bonlime/keras-deeplab-v3-plus [Keras] 51 | + https://github.com/tensorflow/models/tree/master/research/deeplab [Tensorflow] 52 | + https://github.com/speedinghzl/pytorch-segmentation-toolbox [PyTorch] 53 | + https://github.com/kazuto1011/deeplab-pytorch [PyTorch] 54 | + https://github.com/youansheng/torchcv [PyTorch] 55 | + https://github.com/yassouali/pytorch_segmentation [PyTorch][![GitHub stars](https://img.shields.io/github/stars/yassouali/pytorch_segmentation)](https://github.com/yassouali/pytorch_segmentation) 56 | + https://github.com/hualin95/Deeplab-v3plus [PyTorch] 57 | - FCN [https://arxiv.org/pdf/1605.06211.pdf] [2016] 58 | + https://github.com/vlfeat/matconvnet-fcn [MatConvNet] 59 | + https://github.com/shelhamer/fcn.berkeleyvision.org [Caffe] 60 | + https://github.com/MarvinTeichmann/tensorflow-fcn [Tensorflow] 61 | + https://github.com/aurora95/Keras-FCN [Keras] 62 | + https://github.com/mzaradzki/neuralnets/tree/master/vgg_segmentation_keras [Keras] 63 | + https://github.com/k3nt0w/FCN_via_keras [Keras] 64 | + https://github.com/shekkizh/FCN.tensorflow [Tensorflow] 65 | + https://github.com/seewalker/tf-pixelwise [Tensorflow] 66 | + https://github.com/divamgupta/image-segmentation-keras [Keras] 67 | + https://github.com/ZijunDeng/pytorch-semantic-segmentation [PyTorch] 68 | + https://github.com/wkentaro/pytorch-fcn [PyTorch] 69 | + https://github.com/wkentaro/fcn [Chainer] 70 | + https://github.com/apache/incubator-mxnet/tree/master/example/fcn-xs [MxNet] 71 | + https://github.com/muyang0320/tf-fcn [Tensorflow] 72 | + https://github.com/ycszen/pytorch-seg [PyTorch] 73 | + https://github.com/Kaixhin/FCN-semantic-segmentation [PyTorch] 74 | + https://github.com/petrama/VGGSegmentation [Tensorflow] 75 | + https://github.com/simonguist/testing-fcn-for-cityscapes [Caffe] 76 | + https://github.com/hellochick/semantic-segmentation-tensorflow [Tensorflow] 77 | + https://github.com/pierluigiferrari/fcn8s_tensorflow [Tensorflow] 78 | + https://github.com/theduynguyen/Keras-FCN [Keras] 79 | + https://github.com/JihongJu/keras-fcn [Keras] 80 | + https://github.com/yassouali/pytorch_segmentation [PyTorch][![GitHub stars](https://img.shields.io/github/stars/yassouali/pytorch_segmentation)](https://github.com/yassouali/pytorch_segmentation) 81 | - ENet [https://arxiv.org/pdf/1606.02147.pdf] [2016] 82 | + https://github.com/TimoSaemann/ENet [Caffe] 83 | + https://github.com/e-lab/ENet-training [Torch] 84 | + https://github.com/PavlosMelissinos/enet-keras [Keras] 85 | + https://github.com/fregu856/segmentation [Tensorflow] 86 | + https://github.com/kwotsin/TensorFlow-ENet [Tensorflow] 87 | + https://github.com/davidtvs/PyTorch-ENet [PyTorch] 88 | + https://github.com/yassouali/pytorch_segmentation [PyTorch][![GitHub stars](https://img.shields.io/github/stars/yassouali/pytorch_segmentation)](https://github.com/yassouali/pytorch_segmentation) 89 | - LinkNet [https://arxiv.org/pdf/1707.03718.pdf] [2017] 90 | + https://github.com/e-lab/LinkNet [Torch] 91 | + https://github.com/qubvel/segmentation_models [Keras] 92 | - DenseNet [https://arxiv.org/pdf/1611.09326.pdf] [2017] 93 | + https://github.com/SimJeg/FC-DenseNet [Lasagne] 94 | + https://github.com/HasnainRaz/FC-DenseNet-TensorFlow [Tensorflow] 95 | + https://github.com/0bserver07/One-Hundred-Layers-Tiramisu [Keras] 96 | - DilatedNet [https://arxiv.org/pdf/1511.07122.pdf] [2016] 97 | + https://github.com/nicolov/segmentation_keras [Keras] 98 | + https://github.com/fyu/dilation [Caffe] 99 | + https://github.com/fyu/drn#semantic-image-segmentataion [PyTorch] 100 | + https://github.com/hangzhaomit/semantic-segmentation-pytorch [PyTorch] 101 | - PixelNet [https://arxiv.org/pdf/1609.06694.pdf] [2016] 102 | + https://github.com/aayushbansal/PixelNet [Caffe] 103 | - ICNet [https://arxiv.org/pdf/1704.08545.pdf] [2017] 104 | + https://github.com/hszhao/ICNet [Caffe] 105 | + https://github.com/aitorzip/Keras-ICNet [Keras] 106 | + https://github.com/hellochick/ICNet-tensorflow [Tensorflow] 107 | + https://github.com/oandrienko/fast-semantic-segmentation [Tensorflow] 108 | + https://github.com/supervisely/supervisely/tree/master/plugins/nn/icnet [PyTorch] 109 | - ERFNet [http://www.robesafe.uah.es/personal/eduardo.romera/pdfs/Romera17iv.pdf] [?] 110 | + https://github.com/Eromera/erfnet [Torch] 111 | + https://github.com/Eromera/erfnet_pytorch [PyTorch] 112 | - RefineNet [https://arxiv.org/pdf/1611.06612.pdf] [2016] 113 | + https://github.com/guosheng/refinenet [MatConvNet] 114 | - PSPNet [https://arxiv.org/pdf/1612.01105.pdf,https://hszhao.github.io/projects/pspnet/] [2017] 115 | + https://github.com/hszhao/PSPNet [Caffe] 116 | + https://github.com/ZijunDeng/pytorch-semantic-segmentation [PyTorch] 117 | + https://github.com/mitmul/chainer-pspnet [Chainer] 118 | + https://github.com/Vladkryvoruchko/PSPNet-Keras-tensorflow [Keras/Tensorflow] 119 | + https://github.com/pudae/tensorflow-pspnet [Tensorflow] 120 | + https://github.com/hellochick/PSPNet-tensorflow [Tensorflow] 121 | + https://github.com/hellochick/semantic-segmentation-tensorflow [Tensorflow] 122 | + https://github.com/qubvel/segmentation_models [Keras] 123 | + https://github.com/oandrienko/fast-semantic-segmentation [Tensorflow] 124 | + https://github.com/speedinghzl/pytorch-segmentation-toolbox [PyTorch] 125 | + https://github.com/youansheng/torchcv [PyTorch] 126 | + https://github.com/yassouali/pytorch_segmentation [PyTorch][![GitHub stars](https://img.shields.io/github/stars/yassouali/pytorch_segmentation)](https://github.com/yassouali/pytorch_segmentation) 127 | + https://github.com/holyseven/PSPNet-TF-Reproduce [Tensorflow][![GitHub stars](https://img.shields.io/github/stars/holyseven/PSPNet-TF-Reproduce)](https://github.com/holyseven/PSPNet-TF-Reproduce) 128 | + https://github.com/kazuto1011/pspnet-pytorch [PyTorch] 129 | - DeconvNet [https://arxiv.org/pdf/1505.04366.pdf] [2015] 130 | + http://cvlab.postech.ac.kr/research/deconvnet/ [Caffe] 131 | + https://github.com/HyeonwooNoh/DeconvNet [Caffe] 132 | + https://github.com/fabianbormann/Tensorflow-DeconvNet-Segmentation [Tensorflow] 133 | - FRRN [https://arxiv.org/pdf/1611.08323.pdf] [2016] 134 | + https://github.com/TobyPDE/FRRN [Lasagne] 135 | - GCN [https://arxiv.org/pdf/1703.02719.pdf] [2017] 136 | + https://github.com/ZijunDeng/pytorch-semantic-segmentation [PyTorch] 137 | + https://github.com/ycszen/pytorch-seg [PyTorch] 138 | + https://github.com/yassouali/pytorch_segmentation [PyTorch][![GitHub stars](https://img.shields.io/github/stars/yassouali/pytorch_segmentation)](https://github.com/yassouali/pytorch_segmentation) 139 | - LRR [https://arxiv.org/pdf/1605.02264.pdf] [2016] 140 | + https://github.com/golnazghiasi/LRR [Matconvnet] 141 | - DUC, HDC [https://arxiv.org/pdf/1702.08502.pdf] [2017] 142 | + https://github.com/ZijunDeng/pytorch-semantic-segmentation [PyTorch] 143 | + https://github.com/ycszen/pytorch-seg [PyTorch] 144 | + https://github.com/yassouali/pytorch_segmentation [PyTorch][![GitHub stars](https://img.shields.io/github/stars/yassouali/pytorch_segmentation)](https://github.com/yassouali/pytorch_segmentation) 145 | - MultiNet [https://arxiv.org/pdf/1612.07695.pdf] [2016] 146 | + https://github.com/MarvinTeichmann/MultiNet 147 | + https://github.com/MarvinTeichmann/KittiSeg 148 | - Segaware [https://arxiv.org/pdf/1708.04607.pdf] [2017] 149 | + https://github.com/aharley/segaware [Caffe] 150 | - Semantic Segmentation using Adversarial Networks [https://arxiv.org/pdf/1611.08408.pdf] [2016] 151 | + https://github.com/oyam/Semantic-Segmentation-using-Adversarial-Networks [Chainer] 152 | - PixelDCN [https://arxiv.org/pdf/1705.06820.pdf] [2017] 153 | + https://github.com/HongyangGao/PixelDCN [Tensorflow] 154 | - ShuffleSeg [https://arxiv.org/pdf/1803.03816.pdf] [2018] 155 | + https://github.com/MSiam/TFSegmentation [TensorFlow] 156 | - AdaptSegNet [https://arxiv.org/pdf/1802.10349.pdf] [2018] 157 | + https://github.com/wasidennis/AdaptSegNet [PyTorch] 158 | - TuSimple-DUC [https://arxiv.org/pdf/1702.08502.pdf] [2018] 159 | + https://github.com/TuSimple/TuSimple-DUC [MxNet] 160 | - FPN [http://presentations.cocodataset.org/COCO17-Stuff-FAIR.pdf] [2017] 161 | + https://github.com/qubvel/segmentation_models [Keras] 162 | - R2U-Net [https://arxiv.org/ftp/arxiv/papers/1802/1802.06955.pdf] [2018] 163 | + https://github.com/LeeJunHyun/Image_Segmentation#r2u-net [PyTorch] 164 | - Attention U-Net [https://arxiv.org/pdf/1804.03999.pdf] [2018] 165 | + https://github.com/LeeJunHyun/Image_Segmentation#attention-u-net [PyTorch] 166 | + https://github.com/ozan-oktay/Attention-Gated-Networks [PyTorch] 167 | - DANet [https://arxiv.org/pdf/1809.02983.pdf] [2018] 168 | + https://github.com/junfu1115/DANet [PyTorch] 169 | - ShelfNet [https://arxiv.org/pdf/1811.11254.pdf] [2018] 170 | + https://github.com/juntang-zhuang/ShelfNet [PyTorch] 171 | - LadderNet [https://arxiv.org/pdf/1810.07810.pdf] [2018] 172 | + https://github.com/juntang-zhuang/LadderNet [PyTorch] 173 | - BiSeNet [https://arxiv.org/pdf/1808.00897.pdf] [2018] 174 | + https://github.com/ooooverflow/BiSeNet [PyTorch] 175 | + https://github.com/ycszen/TorchSeg [PyTorch] 176 | + https://github.com/zllrunning/face-parsing.PyTorch [PyTorch] 177 | - ESPNet [https://arxiv.org/pdf/1803.06815.pdf] [2018] 178 | + https://github.com/sacmehta/ESPNet [PyTorch] 179 | - DFN [https://arxiv.org/pdf/1804.09337.pdf] [2018] 180 | + https://github.com/ycszen/TorchSeg [PyTorch] 181 | - CCNet [https://arxiv.org/pdf/1811.11721.pdf] [2018] 182 | + https://github.com/speedinghzl/CCNet [PyTorch] 183 | - DenseASPP [http://openaccess.thecvf.com/content_cvpr_2018/papers/Yang_DenseASPP_for_Semantic_CVPR_2018_paper.pdf] [2018] 184 | + https://github.com/youansheng/torchcv [PyTorch] 185 | - Fast-SCNN [https://arxiv.org/pdf/1902.04502.pdf] [2019] 186 | + https://github.com/DeepVoltaire/Fast-SCNN [PyTorch] 187 | - HRNet [https://arxiv.org/pdf/1904.04514.pdf] [2019] 188 | + https://github.com/HRNet/HRNet-Semantic-Segmentation [PyTorch] 189 | - PSANet [https://hszhao.github.io/papers/eccv18_psanet.pdf] [2018] 190 | + https://github.com/hszhao/PSANet [Caffe] 191 | - UPSNet [https://arxiv.org/pdf/1901.03784.pdf] [2019] 192 | + https://github.com/uber-research/UPSNet [PyTorch] 193 | - ConvCRF [https://arxiv.org/pdf/1805.04777.pdf] [2018] 194 | + https://github.com/MarvinTeichmann/ConvCRF [PyTorch] 195 | - Multi-scale Guided Attention for Medical Image Segmentation [https://arxiv.org/pdf/1906.02849.pdf] [2019] 196 | + https://github.com/sinAshish/Multi-Scale-Attention [PyTorch] 197 | - DFANet [https://arxiv.org/pdf/1904.02216.pdf] [2019] 198 | + https://github.com/huaifeng1993/DFANet [PyTorch] 199 | - ExtremeC3Net [https://arxiv.org/pdf/1908.03093.pdf] [2019] 200 | + https://github.com/HYOJINPARK/ExtPortraitSeg [PyTorch] 201 | - EncNet [https://arxiv.org/pdf/1803.08904.pdf] [2018] 202 | + https://github.com/zhanghang1989/PyTorch-Encoding [PyTorch] 203 | - Unet++ [https://arxiv.org/pdf/1807.10165.pdf] [2018] 204 | + https://github.com/MrGiovanni/UNetPlusPlus [Keras] 205 | + https://github.com/4uiiurz1/pytorch-nested-unet [PyTorch] 206 | - FastFCN [https://arxiv.org/pdf/1903.11816.pdf] [2019] 207 | + https://github.com/wuhuikai/FastFCN [PyTorch] 208 | - PortraitNet [https://www.yongliangyang.net/docs/mobilePotrait_c&g19.pdf] [2019] 209 | + https://github.com/dong-x16/PortraitNet [PyTorch] 210 | - GSCNN [https://arxiv.org/pdf/1907.05740.pdf] [2019] 211 | + https://github.com/nv-tlabs/gscnn [PyTorch] 212 | 213 | ### Instance aware segmentation 214 | - FCIS [https://arxiv.org/pdf/1611.07709.pdf] 215 | + https://github.com/msracver/FCIS [MxNet] 216 | - MNC [https://arxiv.org/pdf/1512.04412.pdf] 217 | + https://github.com/daijifeng001/MNC [Caffe] 218 | - DeepMask [https://arxiv.org/pdf/1506.06204.pdf] 219 | + https://github.com/facebookresearch/deepmask [Torch] 220 | - SharpMask [https://arxiv.org/pdf/1603.08695.pdf] 221 | + https://github.com/facebookresearch/deepmask [Torch] 222 | - Mask-RCNN [https://arxiv.org/pdf/1703.06870.pdf] 223 | + https://github.com/CharlesShang/FastMaskRCNN [Tensorflow] 224 | + https://github.com/jasjeetIM/Mask-RCNN [Caffe] 225 | + https://github.com/TuSimple/mx-maskrcnn [MxNet] 226 | + https://github.com/matterport/Mask_RCNN [Keras] 227 | + https://github.com/facebookresearch/maskrcnn-benchmark [PyTorch] 228 | + https://github.com/open-mmlab/mmdetection [PyTorch] 229 | + https://github.com/ZFTurbo/Keras-Mask-RCNN-for-Open-Images-2019-Instance-Segmentation [Keras] 230 | - RIS [https://arxiv.org/pdf/1511.08250.pdf] 231 | + https://github.com/bernard24/RIS [Torch] 232 | - FastMask [https://arxiv.org/pdf/1612.08843.pdf] 233 | + https://github.com/voidrank/FastMask [Caffe] 234 | - BlitzNet [https://arxiv.org/pdf/1708.02813.pdf] 235 | + https://github.com/dvornikita/blitznet [Tensorflow] 236 | - PANet [https://arxiv.org/pdf/1803.01534.pdf] [2018] 237 | + https://github.com/ShuLiu1993/PANet [Caffe] 238 | - PAN [https://arxiv.org/pdf/1805.10180.pdf] [2018] 239 | + https://github.com/JaveyWang/Pyramid-Attention-Networks-pytorch [PyTorch] 240 | - TernausNetV2 [https://arxiv.org/pdf/1806.00844.pdf] [2018] 241 | + https://github.com/ternaus/TernausNetV2 [PyTorch] 242 | - MS R-CNN [https://arxiv.org/pdf/1903.00241.pdf] [2019] 243 | + https://github.com/zjhuang22/maskscoring_rcnn [PyTorch] 244 | - AdaptIS [https://arxiv.org/pdf/1909.07829.pdf] [2019] 245 | + https://github.com/saic-vul/adaptis [MxNet][PyTorch] 246 | - Pose2Seg [https://arxiv.org/pdf/1803.10683.pdf] [2019] 247 | + https://github.com/liruilong940607/Pose2Seg [PyTorch] 248 | - YOLACT [https://arxiv.org/pdf/1904.02689.pdf] [2019] 249 | + https://github.com/dbolya/yolact [PyTorch] 250 | - CenterMask [https://arxiv.org/pdf/1911.06667.pdf] [2019] 251 | + https://github.com/youngwanLEE/CenterMask [PyTorch] 252 | + https://github.com/youngwanLEE/centermask2 [PyTorch] 253 | - InstaBoost [https://arxiv.org/pdf/1908.07801.pdf] [2019] 254 | + https://github.com/GothicAi/Instaboost [PyTorch] 255 | - SOLO [https://arxiv.org/pdf/1912.04488.pdf] [2019] 256 | + https://github.com/WXinlong/SOLO [PyTorch] 257 | - SOLOv2 [https://arxiv.org/pdf/2003.10152.pdf] [2020] 258 | + https://github.com/WXinlong/SOLO [PyTorch] 259 | - D2Det [https://openaccess.thecvf.com/content_CVPR_2020/papers/Cao_D2Det_Towards_High_Quality_Object_Detection_and_Instance_Segmentation_CVPR_2020_paper.pdf] [2020] 260 | +https://github.com/JialeCao001/D2Det [PyTorch] 261 | 262 | ### Weakly-supervised segmentation 263 | - SEC [https://arxiv.org/pdf/1603.06098.pdf] 264 | + https://github.com/kolesman/SEC [Caffe] 265 | 266 | ## RNN 267 | - ReNet [https://arxiv.org/pdf/1505.00393.pdf] 268 | + https://github.com/fvisin/reseg [Lasagne] 269 | - ReSeg [https://arxiv.org/pdf/1511.07053.pdf] 270 | + https://github.com/Wizaron/reseg-pytorch [PyTorch] 271 | + https://github.com/fvisin/reseg [Lasagne] 272 | - RIS [https://arxiv.org/pdf/1511.08250.pdf] 273 | + https://github.com/bernard24/RIS [Torch] 274 | - CRF-RNN [http://www.robots.ox.ac.uk/%7Eszheng/papers/CRFasRNN.pdf] 275 | + https://github.com/martinkersner/train-CRF-RNN [Caffe] 276 | + https://github.com/torrvision/crfasrnn [Caffe] 277 | + https://github.com/NP-coder/CLPS1520Project [Tensorflow] 278 | + https://github.com/renmengye/rec-attend-public [Tensorflow] 279 | + https://github.com/sadeepj/crfasrnn_keras [Keras] 280 | 281 | ## GANS 282 | - pix2pix [https://arxiv.org/pdf/1611.07004.pdf] [2018] 283 | + https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix [Pytorch] 284 | + https://github.com/affinelayer/pix2pix-tensorflow [Tensorflow] 285 | - pix2pixHD [https://arxiv.org/pdf/1711.11585.pdf] [2018] 286 | + https://github.com/NVIDIA/pix2pixHD 287 | - Probalistic Unet [https://arxiv.org/pdf/1806.05034.pdf] [2018] 288 | + https://github.com/SimonKohl/probabilistic_unet 289 | 290 | 291 | ## Graphical Models (CRF, MRF) 292 | + https://github.com/cvlab-epfl/densecrf 293 | + http://vladlen.info/publications/efficient-inference-in-fully-connected-crfs-with-gaussian-edge-potentials/ 294 | + http://www.philkr.net/home/densecrf 295 | + http://graphics.stanford.edu/projects/densecrf/ 296 | + https://github.com/amiltonwong/segmentation/blob/master/segmentation.ipynb 297 | + https://github.com/jliemansifry/super-simple-semantic-segmentation 298 | + http://users.cecs.anu.edu.au/~jdomke/JGMT/ 299 | + https://www.quora.com/How-can-one-train-and-test-conditional-random-field-CRF-in-Python-on-our-own-training-testing-dataset 300 | + https://github.com/tpeng/python-crfsuite 301 | + https://github.com/chokkan/crfsuite 302 | + https://sites.google.com/site/zeppethefake/semantic-segmentation-crf-baseline 303 | + https://github.com/lucasb-eyer/pydensecrf 304 | 305 | ## Datasets: 306 | + [Stanford Background Dataset](http://dags.stanford.edu/projects/scenedataset.html) 307 | + [Sift Flow Dataset](http://people.csail.mit.edu/celiu/SIFTflow/) 308 | + [Barcelona Dataset](http://www.cs.unc.edu/~jtighe/Papers/ECCV10/) 309 | + [Microsoft COCO dataset](http://mscoco.org/) 310 | + [MSRC Dataset](http://research.microsoft.com/en-us/projects/objectclassrecognition/) 311 | + [LITS Liver Tumor Segmentation Dataset](https://competitions.codalab.org/competitions/15595) 312 | + [KITTI](http://www.cvlibs.net/datasets/kitti/eval_road.php) 313 | + [Pascal Context](http://www.cs.stanford.edu/~roozbeh/pascal-context/) 314 | + [Data from Games dataset](https://download.visinf.tu-darmstadt.de/data/from_games/) 315 | + [Human parsing dataset](https://github.com/lemondan/HumanParsing-Dataset) 316 | + [Mapillary Vistas Dataset](https://www.mapillary.com/dataset/vistas) 317 | + [Microsoft AirSim](https://github.com/Microsoft/AirSim) 318 | + [MIT Scene Parsing Benchmark](http://sceneparsing.csail.mit.edu/) 319 | + [COCO 2017 Stuff Segmentation Challenge](http://cocodataset.org/#stuff-challenge2017) 320 | + [ADE20K Dataset](http://groups.csail.mit.edu/vision/datasets/ADE20K/) 321 | + [INRIA Annotations for Graz-02](http://lear.inrialpes.fr/people/marszalek/data/ig02/) 322 | + [Daimler dataset](http://www.gavrila.net/Datasets/Daimler_Pedestrian_Benchmark_D/daimler_pedestrian_benchmark_d.html) 323 | + [ISBI Challenge: Segmentation of neuronal structures in EM stacks](http://brainiac2.mit.edu/isbi_challenge/) 324 | + [INRIA Annotations for Graz-02 (IG02)](https://lear.inrialpes.fr/people/marszalek/data/ig02/) 325 | + [Pratheepan Dataset](http://cs-chan.com/downloads_skin_dataset.html) 326 | + [Clothing Co-Parsing (CCP) Dataset](https://github.com/bearpaw/clothing-co-parsing) 327 | + [ApolloScape](http://apolloscape.auto/scene.html) 328 | + [UrbanMapper3D](https://community.topcoder.com/longcontest/?module=ViewProblemStatement&rd=17007&pm=14703) 329 | + [RoadDetector](https://community.topcoder.com/longcontest/?module=ViewProblemStatement&rd=17036&pm=14735) 330 | + [Cityscapes](https://www.cityscapes-dataset.com/) 331 | + [CamVid](http://mi.eng.cam.ac.uk/research/projects/VideoRec/CamVid/) 332 | + [Inria Aerial Image Labeling](https://project.inria.fr/aerialimagelabeling/) 333 | 334 | ## Benchmarks 335 | + https://github.com/openseg-group/openseg.pytorch [PyTorch] 336 | + https://github.com/open-mmlab/mmsegmentation [PyTorch] 337 | + https://github.com/ZijunDeng/pytorch-semantic-segmentation [PyTorch] 338 | + https://github.com/meetshah1995/pytorch-semseg [PyTorch] 339 | + https://github.com/GeorgeSeif/Semantic-Segmentation-Suite [Tensorflow] 340 | + https://github.com/MSiam/TFSegmentation [Tensorflow] 341 | + https://github.com/CSAILVision/sceneparsing [Caffe+Matlab] 342 | + https://github.com/BloodAxe/segmentation-networks-benchmark [PyTorch] 343 | + https://github.com/warmspringwinds/pytorch-segmentation-detection [PyTorch] 344 | + https://github.com/ycszen/TorchSeg [PyTorch] 345 | + https://github.com/qubvel/segmentation_models [Keras] 346 | + https://github.com/qubvel/segmentation_models.pytorch [PyTorch] 347 | + https://github.com/Tramac/awesome-semantic-segmentation-pytorch [PyTorch] 348 | + https://github.com/hszhao/semseg [PyTorch] 349 | + https://github.com/yassouali/pytorch_segmentation [PyTorch] 350 | + https://github.com/divamgupta/image-segmentation-keras [Keras] 351 | + https://github.com/CSAILVision/semantic-segmentation-pytorch [PyTorch] 352 | + https://github.com/thuyngch/Human-Segmentation-PyTorch [PyTorch] 353 | + https://github.com/PaddlePaddle/PaddleSeg [PaddlePaddle] 354 | 355 | ## Evaluation code 356 | + [Cityscapes dataset] https://github.com/phillipi/pix2pix/tree/master/scripts/eval_cityscapes 357 | 358 | ## Starter code 359 | + https://github.com/mrgloom/keras-semantic-segmentation-example 360 | 361 | ## Annotation Tools: 362 | 363 | + https://github.com/AKSHAYUBHAT/ImageSegmentation 364 | + https://github.com/kyamagu/js-segment-annotator 365 | + https://github.com/CSAILVision/LabelMeAnnotationTool 366 | + https://github.com/seanbell/opensurfaces-segmentation-ui 367 | + https://github.com/lzx1413/labelImgPlus 368 | + https://github.com/wkentaro/labelme 369 | + https://github.com/labelbox/labelbox 370 | + https://github.com/Deep-Magic/COCO-Style-Dataset-Generator-GUI 371 | + https://github.com/Labelbox/Labelbox 372 | + https://github.com/opencv/cvat 373 | + https://github.com/saic-vul/fbrs_interactive_segmentation 374 | 375 | ## Results: 376 | 377 | + [MSRC-21](http://rodrigob.github.io/are_we_there_yet/build/semantic_labeling_datasets_results.html) 378 | + [Cityscapes](https://www.cityscapes-dataset.com/benchmarks/) 379 | + [VOC2012](http://host.robots.ox.ac.uk:8080/leaderboard/displaylb.php?challengeid=11&compid=6) 380 | + https://paperswithcode.com/task/semantic-segmentation 381 | 382 | ## Metrics 383 | + https://github.com/martinkersner/py_img_seg_eval 384 | 385 | ## Losses 386 | + https://github.com/JunMa11/SegLoss 387 | + http://www.cs.umanitoba.ca/~ywang/papers/isvc16.pdf 388 | + https://arxiv.org/pdf/1705.08790.pdf 389 | + https://arxiv.org/pdf/1707.03237.pdf 390 | + http://www.bmva.org/bmvc/2013/Papers/paper0032/paper0032.pdf 391 | 392 | ## Other lists 393 | + https://paperswithcode.com/task/semantic-segmentation 394 | + https://github.com/tangzhenyu/SemanticSegmentation_DL 395 | + https://github.com/nightrome/really-awesome-semantic-segmentation 396 | + https://github.com/JackieZhangdx/InstanceSegmentationList 397 | + https://github.com/damminhtien/awesome-semantic-segmentation 398 | 399 | ## Medical image segmentation: 400 | 401 | - DIGITS 402 | + https://github.com/NVIDIA/DIGITS/tree/master/examples/medical-imaging 403 | 404 | - U-Net: Convolutional Networks for Biomedical Image Segmentation 405 | + http://lmb.informatik.uni-freiburg.de/people/ronneber/u-net/ 406 | + https://github.com/dmlc/mxnet/issues/1514 407 | + https://github.com/orobix/retina-unet 408 | + https://github.com/fvisin/reseg 409 | + https://github.com/yulequan/melanoma-recognition 410 | + http://www.andrewjanowczyk.com/use-case-1-nuclei-segmentation/ 411 | + https://github.com/junyanz/MCILBoost 412 | + https://github.com/imlab-uiip/lung-segmentation-2d 413 | + https://github.com/scottykwok/cervix-roi-segmentation-by-unet 414 | + https://github.com/WeidiXie/cell_counting_v2 415 | + https://github.com/yandexdataschool/YSDA_deeplearning17/blob/master/Seminar6/Seminar%206%20-%20segmentation.ipynb 416 | 417 | - Cascaded-FCN 418 | + https://github.com/IBBM/Cascaded-FCN 419 | 420 | - Keras 421 | + https://github.com/jocicmarko/ultrasound-nerve-segmentation 422 | + https://github.com/EdwardTyantov/ultrasound-nerve-segmentation 423 | + https://github.com/intact-project/ild-cnn 424 | + https://github.com/scottykwok/cervix-roi-segmentation-by-unet 425 | + https://github.com/lishen/end2end-all-conv 426 | 427 | - Tensorflow 428 | + https://github.com/imatge-upc/liverseg-2017-nipsws 429 | + https://github.com/DLTK/DLTK/tree/master/examples/applications/MRBrainS13_tissue_segmentation 430 | 431 | - Using Convolutional Neural Networks (CNN) for Semantic Segmentation of Breast Cancer Lesions (BRCA) 432 | + https://github.com/ecobost/cnn4brca 433 | 434 | - Papers: 435 | + https://www2.warwick.ac.uk/fac/sci/dcs/people/research/csrkbb/tmi2016_ks.pdf 436 | + Sliding window approach 437 | - http://people.idsia.ch/~juergen/nips2012.pdf 438 | + https://github.com/albarqouni/Deep-Learning-for-Medical-Applications#segmentation 439 | 440 | - Data: 441 | - https://luna16.grand-challenge.org/ 442 | - https://camelyon16.grand-challenge.org/ 443 | - https://github.com/beamandrew/medical-data 444 | 445 | ## Satellite images segmentation 446 | 447 | + https://github.com/mshivaprakash/sat-seg-thesis 448 | + https://github.com/KGPML/Hyperspectral 449 | + https://github.com/lopuhin/kaggle-dstl 450 | + https://github.com/mitmul/ssai 451 | + https://github.com/mitmul/ssai-cnn 452 | + https://github.com/azavea/raster-vision 453 | + https://github.com/nshaud/DeepNetsForEO 454 | + https://github.com/trailbehind/DeepOSM 455 | + https://github.com/mapbox/robosat 456 | + https://github.com/datapink/robosat.pink 457 | 458 | - Data: 459 | + https://github.com/RSIA-LIESMARS-WHU/RSOD-Dataset- 460 | + SpaceNet[https://spacenetchallenge.github.io/] 461 | + https://github.com/chrieke/awesome-satellite-imagery-datasets 462 | 463 | ## Video segmentation 464 | 465 | + https://github.com/shelhamer/clockwork-fcn 466 | + https://github.com/JingchunCheng/Seg-with-SPN 467 | 468 | ## Autonomous driving 469 | 470 | + https://github.com/MarvinTeichmann/MultiNet 471 | + https://github.com/MarvinTeichmann/KittiSeg 472 | + https://github.com/vxy10/p5_VehicleDetection_Unet [Keras] 473 | + https://github.com/ndrplz/self-driving-car 474 | + https://github.com/mvirgo/MLND-Capstone 475 | + https://github.com/zhujun98/semantic_segmentation/tree/master/fcn8s_road 476 | + https://github.com/MaybeShewill-CV/lanenet-lane-detection 477 | 478 | ### Other 479 | 480 | ## Networks by framework (Older list) 481 | - Keras 482 | + https://github.com/gakarak/FCN_MSCOCO_Food_Segmentation 483 | + https://github.com/abbypa/NNProject_DeepMask 484 | 485 | - TensorFlow 486 | + https://github.com/warmspringwinds/tf-image-segmentation 487 | 488 | - Caffe 489 | + https://github.com/xiaolonw/nips14_loc_seg_testonly 490 | + https://github.com/naibaf7/caffe_neural_tool 491 | 492 | - torch 493 | + https://github.com/erogol/seg-torch 494 | + https://github.com/phillipi/pix2pix 495 | 496 | - MXNet 497 | + https://github.com/itijyou/ademxapp 498 | 499 | ## Papers and Code (Older list) 500 | 501 | - Simultaneous detection and segmentation 502 | 503 | + http://www.eecs.berkeley.edu/Research/Projects/CS/vision/shape/sds/ 504 | + https://github.com/bharath272/sds_eccv2014 505 | 506 | - Decoupled Deep Neural Network for Semi-supervised Semantic Segmentation 507 | 508 | + https://github.com/HyeonwooNoh/DecoupledNet 509 | 510 | - Learning to Propose Objects 511 | 512 | + http://vladlen.info/publications/learning-to-propose-objects/ 513 | + https://github.com/philkr/lpo 514 | 515 | - Nonparametric Scene Parsing via Label Transfer 516 | 517 | + http://people.csail.mit.edu/celiu/LabelTransfer/code.html 518 | 519 | - Other 520 | + https://github.com/cvjena/cn24 521 | + http://lmb.informatik.uni-freiburg.de/resources/software.php 522 | + https://github.com/NVIDIA/DIGITS/tree/master/examples/semantic-segmentation 523 | + http://jamie.shotton.org/work/code.html 524 | + https://github.com/amueller/textonboost 525 | 526 | ## To look at 527 | + https://github.com/fchollet/keras/issues/6538 528 | + https://github.com/warmspringwinds/tensorflow_notes 529 | + https://github.com/kjw0612/awesome-deep-vision#semantic-segmentation 530 | + https://github.com/desimone/segmentation-models 531 | + https://github.com/nightrome/really-awesome-semantic-segmentation 532 | + https://github.com/kjw0612/awesome-deep-vision#semantic-segmentation 533 | + http://www.it-caesar.com/list-of-contemporary-semantic-segmentation-datasets/ 534 | + https://github.com/MichaelXin/Awesome-Caffe#23-image-segmentation 535 | + https://github.com/warmspringwinds/pytorch-segmentation-detection 536 | + https://github.com/neuropoly/axondeepseg 537 | + https://github.com/petrochenko-pavel-a/segmentation_training_pipeline 538 | 539 | 540 | ## Blog posts, other: 541 | 542 | + https://handong1587.github.io/deep_learning/2015/10/09/segmentation.html 543 | + http://www.andrewjanowczyk.com/efficient-pixel-wise-deep-learning-on-large-images/ 544 | + https://devblogs.nvidia.com/parallelforall/image-segmentation-using-digits-5/ 545 | + https://github.com/NVIDIA/DIGITS/tree/master/examples/binary-segmentation 546 | + https://github.com/NVIDIA/DIGITS/tree/master/examples/semantic-segmentation 547 | + http://blog.qure.ai/notes/semantic-segmentation-deep-learning-review 548 | + https://medium.com/@barvinograd1/instance-embedding-instance-segmentation-without-proposals-31946a7c53e1 549 | 550 | --------------------------------------------------------------------------------