Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.
Awesome Lists | Featured Topics | Projects
https://github.com/mrgloom/awesome-semantic-segmentation

:metal: awesome-semantic-segmentation
https://github.com/mrgloom/awesome-semantic-segmentation
List: awesome-semantic-segmentation
benchmark deeplearning evaluation semantic-segmentation
Last synced: 20 days ago
JSON representation
:metal: awesome-semantic-segmentation
Host: GitHub
URL: https://github.com/mrgloom/awesome-semantic-segmentation
Owner: mrgloom
Created: 2015-10-03T10:07:34.000Z (about 9 years ago)
Default Branch: master
Last Pushed: 2021-05-08T13:40:11.000Z (over 3 years ago)
Last Synced: 2024-05-19T22:00:57.584Z (6 months ago)
Topics: benchmark, deeplearning, evaluation, semantic-segmentation
Homepage:
Size: 283 KB
Stars: 10,352
Watchers: 443
Forks: 2,489
Open Issues: 16
Metadata Files:
- Readme: README.md
Awesome Lists containing this project

awesomeai - Semantic Segmentation
awesome-ai-awesomeness - Semantic Segmentation
awesome-computer-vision-papers - awesome-semantic-segmentation
awesome-ai-awesomeness - Semantic Segmentation
awesome-of-awesome-ml - awesome-semantic-segmentation (by mrgloom)
Awesome-Paper-List - Semantic Segmentation
awesome-ai-list-guide - awesome-semantic-segmentation - semantic-segmentation (CV)
Awesome-Computer-Vision - 12
Awesome-lists - awesome-semantic-segmentation - semantic-segmentation. (Algorithms, big data and machine learning)
SecondaryAwesomeCollection - mrgloom/awesome-semantic-segmentation
ultimate-awesome - awesome-semantic-segmentation - :metal: awesome-semantic-segmentation. (Other Lists / PowerShell Lists)
StarryDivineSky - mrgloom/awesome-semantic-segmentation
README

        [![Awesome](https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)](https://github.com/sindresorhus/awesome)

# Awesome Semantic Segmentation

## Networks by architecture

### Semantic segmentation

- U-Net [https://arxiv.org/pdf/1505.04597.pdf] [2015]

	+ 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)

	+ 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)

	+ 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)

	+ 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)

	+ 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)

	+ 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)

	+ 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)

	+ 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)

	+ 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)

	+ 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)

	+ 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)

	+ 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)

	+ 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)

	+ 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)

	+ 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)

	+ https://lmb.informatik.uni-freiburg.de/people/ronneber/u-net/ [Caffe + Matlab]

- SegNet [https://arxiv.org/pdf/1511.00561.pdf] [2016]

	+ https://github.com/alexgkendall/caffe-segnet [Caffe]

	+ https://github.com/developmentseed/caffe/tree/segnet-multi-gpu [Caffe]

	+ https://github.com/preddy5/segnet [Keras]

	+ https://github.com/imlab-uiip/keras-segnet [Keras]

	+ https://github.com/andreaazzini/segnet [Tensorflow]

	+ https://github.com/fedor-chervinskii/segnet-torch [Torch]

	+ https://github.com/0bserver07/Keras-SegNet-Basic [Keras]

	+ https://github.com/tkuanlun350/Tensorflow-SegNet [Tensorflow]

	+ https://github.com/divamgupta/image-segmentation-keras [Keras]

	+ https://github.com/ZijunDeng/pytorch-semantic-segmentation [PyTorch]

	+ https://github.com/chainer/chainercv/tree/master/examples/segnet [Chainer]

	+ https://github.com/ykamikawa/keras-SegNet [Keras]

	+ https://github.com/ykamikawa/tf-keras-SegNet [Keras]

	+ https://github.com/yassouali/pytorch_segmentation [PyTorch][![GitHub stars](https://img.shields.io/github/stars/yassouali/pytorch_segmentation)](https://github.com/yassouali/pytorch_segmentation)

- DeepLab [https://arxiv.org/pdf/1606.00915.pdf] [2017]

	+ https://bitbucket.org/deeplab/deeplab-public/ [Caffe]

	+ https://bitbucket.org/aquariusjay/deeplab-public-ver2 [Caffe]

	+ https://github.com/TheLegendAli/DeepLab-Context [Caffe]

	+ https://github.com/msracver/Deformable-ConvNets/tree/master/deeplab [MXNet]

	+ https://github.com/DrSleep/tensorflow-deeplab-resnet [Tensorflow]

	+ https://github.com/muyang0320/tensorflow-deeplab-resnet-crf [TensorFlow]

	+ https://github.com/isht7/pytorch-deeplab-resnet [PyTorch]

	+ https://github.com/bermanmaxim/jaccardSegment [PyTorch]

	+ https://github.com/martinkersner/train-DeepLab [Caffe]

	+ https://github.com/chenxi116/TF-deeplab [Tensorflow]

	+ https://github.com/bonlime/keras-deeplab-v3-plus [Keras]

	+ https://github.com/tensorflow/models/tree/master/research/deeplab [Tensorflow]

	+ https://github.com/speedinghzl/pytorch-segmentation-toolbox [PyTorch]

	+ https://github.com/kazuto1011/deeplab-pytorch [PyTorch]

	+ https://github.com/youansheng/torchcv [PyTorch]

	+ https://github.com/yassouali/pytorch_segmentation [PyTorch][![GitHub stars](https://img.shields.io/github/stars/yassouali/pytorch_segmentation)](https://github.com/yassouali/pytorch_segmentation)

	+ https://github.com/hualin95/Deeplab-v3plus [PyTorch]

- FCN [https://arxiv.org/pdf/1605.06211.pdf] [2016]

	+ https://github.com/vlfeat/matconvnet-fcn [MatConvNet]

	+ https://github.com/shelhamer/fcn.berkeleyvision.org [Caffe]

	+ https://github.com/MarvinTeichmann/tensorflow-fcn [Tensorflow]

	+ https://github.com/aurora95/Keras-FCN [Keras]

	+ https://github.com/mzaradzki/neuralnets/tree/master/vgg_segmentation_keras [Keras]

	+ https://github.com/k3nt0w/FCN_via_keras [Keras]

	+ https://github.com/shekkizh/FCN.tensorflow [Tensorflow]

	+ https://github.com/seewalker/tf-pixelwise [Tensorflow]

	+ https://github.com/divamgupta/image-segmentation-keras [Keras]

	+ https://github.com/ZijunDeng/pytorch-semantic-segmentation [PyTorch]

	+ https://github.com/wkentaro/pytorch-fcn [PyTorch]

	+ https://github.com/wkentaro/fcn [Chainer]

	+ https://github.com/apache/incubator-mxnet/tree/master/example/fcn-xs [MxNet]

	+ https://github.com/muyang0320/tf-fcn [Tensorflow]

	+ https://github.com/ycszen/pytorch-seg [PyTorch]

	+ https://github.com/Kaixhin/FCN-semantic-segmentation [PyTorch]

	+ https://github.com/petrama/VGGSegmentation [Tensorflow]

	+ https://github.com/simonguist/testing-fcn-for-cityscapes [Caffe]

	+ https://github.com/hellochick/semantic-segmentation-tensorflow [Tensorflow]

	+ https://github.com/pierluigiferrari/fcn8s_tensorflow [Tensorflow]

	+ https://github.com/theduynguyen/Keras-FCN [Keras]

	+ https://github.com/JihongJu/keras-fcn [Keras]

	+ https://github.com/yassouali/pytorch_segmentation [PyTorch][![GitHub stars](https://img.shields.io/github/stars/yassouali/pytorch_segmentation)](https://github.com/yassouali/pytorch_segmentation)

- ENet [https://arxiv.org/pdf/1606.02147.pdf] [2016]

 	+ https://github.com/TimoSaemann/ENet [Caffe]

	+ https://github.com/e-lab/ENet-training [Torch]

	+ https://github.com/PavlosMelissinos/enet-keras [Keras]

	+ https://github.com/fregu856/segmentation [Tensorflow]

	+ https://github.com/kwotsin/TensorFlow-ENet [Tensorflow]

	+ https://github.com/davidtvs/PyTorch-ENet [PyTorch]

	+ https://github.com/yassouali/pytorch_segmentation [PyTorch][![GitHub stars](https://img.shields.io/github/stars/yassouali/pytorch_segmentation)](https://github.com/yassouali/pytorch_segmentation)

- LinkNet [https://arxiv.org/pdf/1707.03718.pdf] [2017]

	+ https://github.com/e-lab/LinkNet [Torch]

	+ https://github.com/qubvel/segmentation_models [Keras]

- DenseNet [https://arxiv.org/pdf/1611.09326.pdf] [2017]

	+ https://github.com/SimJeg/FC-DenseNet [Lasagne]

	+ https://github.com/HasnainRaz/FC-DenseNet-TensorFlow [Tensorflow]

	+ https://github.com/0bserver07/One-Hundred-Layers-Tiramisu [Keras]

- DilatedNet [https://arxiv.org/pdf/1511.07122.pdf] [2016]

	+ https://github.com/nicolov/segmentation_keras [Keras]

	+ https://github.com/fyu/dilation [Caffe]

	+ https://github.com/fyu/drn#semantic-image-segmentataion [PyTorch]

	+ https://github.com/hangzhaomit/semantic-segmentation-pytorch [PyTorch]

- PixelNet [https://arxiv.org/pdf/1609.06694.pdf] [2016]

	+ https://github.com/aayushbansal/PixelNet [Caffe]

- ICNet [https://arxiv.org/pdf/1704.08545.pdf] [2017]

	+ https://github.com/hszhao/ICNet [Caffe]

	+ https://github.com/aitorzip/Keras-ICNet [Keras]

	+ https://github.com/hellochick/ICNet-tensorflow [Tensorflow]

	+ https://github.com/oandrienko/fast-semantic-segmentation [Tensorflow]

	+ https://github.com/supervisely/supervisely/tree/master/plugins/nn/icnet [PyTorch]

- ERFNet [http://www.robesafe.uah.es/personal/eduardo.romera/pdfs/Romera17iv.pdf] [?]

	+ https://github.com/Eromera/erfnet [Torch]

	+ https://github.com/Eromera/erfnet_pytorch [PyTorch]

- RefineNet [https://arxiv.org/pdf/1611.06612.pdf] [2016]

	+ https://github.com/guosheng/refinenet [MatConvNet]

- PSPNet [https://arxiv.org/pdf/1612.01105.pdf,https://hszhao.github.io/projects/pspnet/] [2017]

	+ https://github.com/hszhao/PSPNet [Caffe]

	+ https://github.com/ZijunDeng/pytorch-semantic-segmentation [PyTorch]

	+ https://github.com/mitmul/chainer-pspnet [Chainer]

	+ https://github.com/Vladkryvoruchko/PSPNet-Keras-tensorflow [Keras/Tensorflow]

	+ https://github.com/pudae/tensorflow-pspnet [Tensorflow]

	+ https://github.com/hellochick/PSPNet-tensorflow [Tensorflow]

	+ https://github.com/hellochick/semantic-segmentation-tensorflow [Tensorflow]

	+ https://github.com/qubvel/segmentation_models [Keras]

	+ https://github.com/oandrienko/fast-semantic-segmentation [Tensorflow]

	+ https://github.com/speedinghzl/pytorch-segmentation-toolbox [PyTorch]

	+ https://github.com/youansheng/torchcv [PyTorch]

	+ https://github.com/yassouali/pytorch_segmentation [PyTorch][![GitHub stars](https://img.shields.io/github/stars/yassouali/pytorch_segmentation)](https://github.com/yassouali/pytorch_segmentation)

	+ 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)

	+ https://github.com/kazuto1011/pspnet-pytorch [PyTorch]

- DeconvNet [https://arxiv.org/pdf/1505.04366.pdf] [2015]

	+ http://cvlab.postech.ac.kr/research/deconvnet/ [Caffe]

	+ https://github.com/HyeonwooNoh/DeconvNet [Caffe]

	+ https://github.com/fabianbormann/Tensorflow-DeconvNet-Segmentation [Tensorflow]

- FRRN [https://arxiv.org/pdf/1611.08323.pdf] [2016]

	+ https://github.com/TobyPDE/FRRN [Lasagne]

- GCN [https://arxiv.org/pdf/1703.02719.pdf] [2017]

	+ https://github.com/ZijunDeng/pytorch-semantic-segmentation [PyTorch]

	+ https://github.com/ycszen/pytorch-seg [PyTorch]

	+ https://github.com/yassouali/pytorch_segmentation [PyTorch][![GitHub stars](https://img.shields.io/github/stars/yassouali/pytorch_segmentation)](https://github.com/yassouali/pytorch_segmentation)

- LRR [https://arxiv.org/pdf/1605.02264.pdf] [2016]

	+ https://github.com/golnazghiasi/LRR [Matconvnet]

- DUC, HDC [https://arxiv.org/pdf/1702.08502.pdf] [2017]

	+ https://github.com/ZijunDeng/pytorch-semantic-segmentation [PyTorch]

	+ https://github.com/ycszen/pytorch-seg [PyTorch]

	+ https://github.com/yassouali/pytorch_segmentation [PyTorch][![GitHub stars](https://img.shields.io/github/stars/yassouali/pytorch_segmentation)](https://github.com/yassouali/pytorch_segmentation)

- MultiNet [https://arxiv.org/pdf/1612.07695.pdf] [2016]

	+ https://github.com/MarvinTeichmann/MultiNet

	+ https://github.com/MarvinTeichmann/KittiSeg

- Segaware [https://arxiv.org/pdf/1708.04607.pdf] [2017]

	+ https://github.com/aharley/segaware [Caffe]

- Semantic Segmentation using Adversarial Networks [https://arxiv.org/pdf/1611.08408.pdf] [2016]

	+ https://github.com/oyam/Semantic-Segmentation-using-Adversarial-Networks [Chainer]

- PixelDCN [https://arxiv.org/pdf/1705.06820.pdf] [2017]

	+ https://github.com/HongyangGao/PixelDCN [Tensorflow]

- ShuffleSeg [https://arxiv.org/pdf/1803.03816.pdf] [2018]

	+ https://github.com/MSiam/TFSegmentation [TensorFlow]

- AdaptSegNet [https://arxiv.org/pdf/1802.10349.pdf] [2018]

	+ https://github.com/wasidennis/AdaptSegNet [PyTorch]

- TuSimple-DUC [https://arxiv.org/pdf/1702.08502.pdf] [2018]

	+ https://github.com/TuSimple/TuSimple-DUC [MxNet]

- FPN [http://presentations.cocodataset.org/COCO17-Stuff-FAIR.pdf] [2017]

	+ https://github.com/qubvel/segmentation_models [Keras]

- R2U-Net [https://arxiv.org/ftp/arxiv/papers/1802/1802.06955.pdf] [2018]

	+ https://github.com/LeeJunHyun/Image_Segmentation#r2u-net [PyTorch]

- Attention U-Net [https://arxiv.org/pdf/1804.03999.pdf] [2018]

	+ https://github.com/LeeJunHyun/Image_Segmentation#attention-u-net [PyTorch]

	+ https://github.com/ozan-oktay/Attention-Gated-Networks [PyTorch]

- DANet [https://arxiv.org/pdf/1809.02983.pdf] [2018]

	+ https://github.com/junfu1115/DANet [PyTorch]

- ShelfNet [https://arxiv.org/pdf/1811.11254.pdf] [2018]

	+ https://github.com/juntang-zhuang/ShelfNet [PyTorch]

- LadderNet [https://arxiv.org/pdf/1810.07810.pdf] [2018]

	+ https://github.com/juntang-zhuang/LadderNet [PyTorch]

- BiSeNet [https://arxiv.org/pdf/1808.00897.pdf] [2018]

	+ https://github.com/ooooverflow/BiSeNet [PyTorch]

	+ https://github.com/ycszen/TorchSeg [PyTorch]

	+ https://github.com/zllrunning/face-parsing.PyTorch [PyTorch]

- ESPNet [https://arxiv.org/pdf/1803.06815.pdf] [2018]

	+ https://github.com/sacmehta/ESPNet [PyTorch]

- DFN [https://arxiv.org/pdf/1804.09337.pdf] [2018]

	+ https://github.com/ycszen/TorchSeg [PyTorch]

- CCNet [https://arxiv.org/pdf/1811.11721.pdf] [2018]

	+ https://github.com/speedinghzl/CCNet [PyTorch]

- DenseASPP [http://openaccess.thecvf.com/content_cvpr_2018/papers/Yang_DenseASPP_for_Semantic_CVPR_2018_paper.pdf] [2018]

	+ https://github.com/youansheng/torchcv [PyTorch]

- Fast-SCNN [https://arxiv.org/pdf/1902.04502.pdf] [2019]

	+ https://github.com/DeepVoltaire/Fast-SCNN [PyTorch]

- HRNet [https://arxiv.org/pdf/1904.04514.pdf] [2019]

	+ https://github.com/HRNet/HRNet-Semantic-Segmentation [PyTorch]

- PSANet [https://hszhao.github.io/papers/eccv18_psanet.pdf] [2018]

	+ https://github.com/hszhao/PSANet [Caffe]

- UPSNet [https://arxiv.org/pdf/1901.03784.pdf] [2019]

	+ https://github.com/uber-research/UPSNet [PyTorch]

- ConvCRF [https://arxiv.org/pdf/1805.04777.pdf] [2018]

	+ https://github.com/MarvinTeichmann/ConvCRF [PyTorch]

- Multi-scale Guided Attention for Medical Image Segmentation [https://arxiv.org/pdf/1906.02849.pdf] [2019]

	+ https://github.com/sinAshish/Multi-Scale-Attention [PyTorch]

- DFANet [https://arxiv.org/pdf/1904.02216.pdf] [2019]

	+ https://github.com/huaifeng1993/DFANet [PyTorch]

- ExtremeC3Net [https://arxiv.org/pdf/1908.03093.pdf] [2019]

	+ https://github.com/HYOJINPARK/ExtPortraitSeg [PyTorch]

- EncNet [https://arxiv.org/pdf/1803.08904.pdf] [2018]

	+ https://github.com/zhanghang1989/PyTorch-Encoding [PyTorch]

- Unet++ [https://arxiv.org/pdf/1807.10165.pdf] [2018]

	+ https://github.com/MrGiovanni/UNetPlusPlus [Keras]

	+ https://github.com/4uiiurz1/pytorch-nested-unet [PyTorch]

- FastFCN [https://arxiv.org/pdf/1903.11816.pdf] [2019]

	+ https://github.com/wuhuikai/FastFCN [PyTorch]

- PortraitNet [https://www.yongliangyang.net/docs/mobilePotrait_c&g19.pdf] [2019]

	+ https://github.com/dong-x16/PortraitNet [PyTorch]

- GSCNN [https://arxiv.org/pdf/1907.05740.pdf] [2019]

	+ https://github.com/nv-tlabs/gscnn [PyTorch]

	

### Instance aware segmentation

- FCIS [https://arxiv.org/pdf/1611.07709.pdf]

	+ https://github.com/msracver/FCIS [MxNet]

- MNC [https://arxiv.org/pdf/1512.04412.pdf]

	+ https://github.com/daijifeng001/MNC [Caffe]

- DeepMask [https://arxiv.org/pdf/1506.06204.pdf]

	+ https://github.com/facebookresearch/deepmask [Torch]

- SharpMask [https://arxiv.org/pdf/1603.08695.pdf]

	+ https://github.com/facebookresearch/deepmask [Torch]

- Mask-RCNN [https://arxiv.org/pdf/1703.06870.pdf]

	+ https://github.com/CharlesShang/FastMaskRCNN [Tensorflow]

	+ https://github.com/jasjeetIM/Mask-RCNN [Caffe]

	+ https://github.com/TuSimple/mx-maskrcnn [MxNet]

	+ https://github.com/matterport/Mask_RCNN [Keras]

	+ https://github.com/facebookresearch/maskrcnn-benchmark [PyTorch]

	+ https://github.com/open-mmlab/mmdetection [PyTorch]

	+ https://github.com/ZFTurbo/Keras-Mask-RCNN-for-Open-Images-2019-Instance-Segmentation [Keras]

- RIS [https://arxiv.org/pdf/1511.08250.pdf]

  + https://github.com/bernard24/RIS [Torch]

- FastMask [https://arxiv.org/pdf/1612.08843.pdf]

  + https://github.com/voidrank/FastMask [Caffe]

- BlitzNet [https://arxiv.org/pdf/1708.02813.pdf]

  + https://github.com/dvornikita/blitznet [Tensorflow]

- PANet [https://arxiv.org/pdf/1803.01534.pdf] [2018]

  + https://github.com/ShuLiu1993/PANet [Caffe]

- PAN [https://arxiv.org/pdf/1805.10180.pdf] [2018]

  + https://github.com/JaveyWang/Pyramid-Attention-Networks-pytorch [PyTorch]

- TernausNetV2 [https://arxiv.org/pdf/1806.00844.pdf] [2018]

	+ https://github.com/ternaus/TernausNetV2 [PyTorch]

- MS R-CNN [https://arxiv.org/pdf/1903.00241.pdf] [2019]

	+ https://github.com/zjhuang22/maskscoring_rcnn [PyTorch]

- AdaptIS [https://arxiv.org/pdf/1909.07829.pdf] [2019]

	+ https://github.com/saic-vul/adaptis [MxNet][PyTorch]

- Pose2Seg [https://arxiv.org/pdf/1803.10683.pdf] [2019]

	+ https://github.com/liruilong940607/Pose2Seg [PyTorch]

- YOLACT [https://arxiv.org/pdf/1904.02689.pdf] [2019]

	+ https://github.com/dbolya/yolact [PyTorch]

- CenterMask [https://arxiv.org/pdf/1911.06667.pdf] [2019]

	+ https://github.com/youngwanLEE/CenterMask [PyTorch]

	+ https://github.com/youngwanLEE/centermask2 [PyTorch]

- InstaBoost [https://arxiv.org/pdf/1908.07801.pdf] [2019]

	+ https://github.com/GothicAi/Instaboost [PyTorch]

- SOLO [https://arxiv.org/pdf/1912.04488.pdf] [2019]

	+ https://github.com/WXinlong/SOLO [PyTorch]

- SOLOv2 [https://arxiv.org/pdf/2003.10152.pdf] [2020]

	+ https://github.com/WXinlong/SOLO [PyTorch]

- D2Det [https://openaccess.thecvf.com/content_CVPR_2020/papers/Cao_D2Det_Towards_High_Quality_Object_Detection_and_Instance_Segmentation_CVPR_2020_paper.pdf] [2020]

	+https://github.com/JialeCao001/D2Det [PyTorch]

	

### Weakly-supervised segmentation

- SEC [https://arxiv.org/pdf/1603.06098.pdf]

  + https://github.com/kolesman/SEC [Caffe]

## RNN

- ReNet [https://arxiv.org/pdf/1505.00393.pdf]

  + https://github.com/fvisin/reseg [Lasagne]

- ReSeg [https://arxiv.org/pdf/1511.07053.pdf]

  + https://github.com/Wizaron/reseg-pytorch [PyTorch]

  + https://github.com/fvisin/reseg [Lasagne]

- RIS [https://arxiv.org/pdf/1511.08250.pdf]

  + https://github.com/bernard24/RIS [Torch]

- CRF-RNN [http://www.robots.ox.ac.uk/%7Eszheng/papers/CRFasRNN.pdf]

  + https://github.com/martinkersner/train-CRF-RNN [Caffe]

  + https://github.com/torrvision/crfasrnn [Caffe]

  + https://github.com/NP-coder/CLPS1520Project [Tensorflow]

  + https://github.com/renmengye/rec-attend-public [Tensorflow]

  + https://github.com/sadeepj/crfasrnn_keras [Keras]

## GANS

- pix2pix [https://arxiv.org/pdf/1611.07004.pdf] [2018]

  + https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix [Pytorch]

  + https://github.com/affinelayer/pix2pix-tensorflow [Tensorflow]

- pix2pixHD [https://arxiv.org/pdf/1711.11585.pdf] [2018]

  + https://github.com/NVIDIA/pix2pixHD

- Probalistic Unet [https://arxiv.org/pdf/1806.05034.pdf] [2018]

  + https://github.com/SimonKohl/probabilistic_unet

## Graphical Models (CRF, MRF)

  + https://github.com/cvlab-epfl/densecrf

  + http://vladlen.info/publications/efficient-inference-in-fully-connected-crfs-with-gaussian-edge-potentials/

  + http://www.philkr.net/home/densecrf

  + http://graphics.stanford.edu/projects/densecrf/

  + https://github.com/amiltonwong/segmentation/blob/master/segmentation.ipynb

  + https://github.com/jliemansifry/super-simple-semantic-segmentation

  + 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://github.com/tpeng/python-crfsuite

  + https://github.com/chokkan/crfsuite

  + https://sites.google.com/site/zeppethefake/semantic-segmentation-crf-baseline

  + https://github.com/lucasb-eyer/pydensecrf

## Datasets:

  + [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)

  + [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/)

  + [Inria Aerial Image Labeling](https://project.inria.fr/aerialimagelabeling/)

## Benchmarks

  + https://github.com/openseg-group/openseg.pytorch [PyTorch]

  + https://github.com/open-mmlab/mmsegmentation [PyTorch]

  + https://github.com/ZijunDeng/pytorch-semantic-segmentation [PyTorch]

  + https://github.com/meetshah1995/pytorch-semseg [PyTorch]

  + https://github.com/GeorgeSeif/Semantic-Segmentation-Suite [Tensorflow]

  + https://github.com/MSiam/TFSegmentation [Tensorflow]

  + https://github.com/CSAILVision/sceneparsing [Caffe+Matlab]

  + https://github.com/BloodAxe/segmentation-networks-benchmark [PyTorch]

  + https://github.com/warmspringwinds/pytorch-segmentation-detection [PyTorch]

  + https://github.com/ycszen/TorchSeg [PyTorch]

  + https://github.com/qubvel/segmentation_models [Keras]

  + https://github.com/qubvel/segmentation_models.pytorch [PyTorch]

  + https://github.com/Tramac/awesome-semantic-segmentation-pytorch [PyTorch]

  + https://github.com/hszhao/semseg [PyTorch]

  + https://github.com/yassouali/pytorch_segmentation [PyTorch]

  + https://github.com/divamgupta/image-segmentation-keras [Keras]

  + https://github.com/CSAILVision/semantic-segmentation-pytorch [PyTorch]

  + https://github.com/thuyngch/Human-Segmentation-PyTorch [PyTorch]

  + https://github.com/PaddlePaddle/PaddleSeg [PaddlePaddle]

## Evaluation code

  + [Cityscapes dataset] https://github.com/phillipi/pix2pix/tree/master/scripts/eval_cityscapes

## Starter code

  + https://github.com/mrgloom/keras-semantic-segmentation-example

## Annotation Tools:

  + https://github.com/AKSHAYUBHAT/ImageSegmentation

  + https://github.com/kyamagu/js-segment-annotator

  + https://github.com/CSAILVision/LabelMeAnnotationTool

  + https://github.com/seanbell/opensurfaces-segmentation-ui

  + https://github.com/lzx1413/labelImgPlus

  + https://github.com/wkentaro/labelme

  + https://github.com/labelbox/labelbox

  + https://github.com/Deep-Magic/COCO-Style-Dataset-Generator-GUI

  + https://github.com/Labelbox/Labelbox

  + https://github.com/opencv/cvat

  + https://github.com/saic-vul/fbrs_interactive_segmentation

## Results:

  + [MSRC-21](http://rodrigob.github.io/are_we_there_yet/build/semantic_labeling_datasets_results.html)

  + [Cityscapes](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

## Metrics

  + https://github.com/martinkersner/py_img_seg_eval

## Losses

  + https://github.com/JunMa11/SegLoss 

  + http://www.cs.umanitoba.ca/~ywang/papers/isvc16.pdf

  + https://arxiv.org/pdf/1705.08790.pdf

  + https://arxiv.org/pdf/1707.03237.pdf

  + http://www.bmva.org/bmvc/2013/Papers/paper0032/paper0032.pdf

    

## Other lists

  + https://paperswithcode.com/task/semantic-segmentation

  + https://github.com/tangzhenyu/SemanticSegmentation_DL

  + https://github.com/nightrome/really-awesome-semantic-segmentation

  + https://github.com/JackieZhangdx/InstanceSegmentationList

  + https://github.com/damminhtien/awesome-semantic-segmentation

## Medical image segmentation:

- DIGITS

  + 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/

  + https://github.com/dmlc/mxnet/issues/1514

  + https://github.com/orobix/retina-unet

  + https://github.com/fvisin/reseg

  + https://github.com/yulequan/melanoma-recognition

  + http://www.andrewjanowczyk.com/use-case-1-nuclei-segmentation/

  + https://github.com/junyanz/MCILBoost

  + https://github.com/imlab-uiip/lung-segmentation-2d

  + https://github.com/scottykwok/cervix-roi-segmentation-by-unet

  + https://github.com/WeidiXie/cell_counting_v2

  + https://github.com/yandexdataschool/YSDA_deeplearning17/blob/master/Seminar6/Seminar%206%20-%20segmentation.ipynb

  

- Cascaded-FCN

  + https://github.com/IBBM/Cascaded-FCN

  

- Keras

  + https://github.com/jocicmarko/ultrasound-nerve-segmentation

  + https://github.com/EdwardTyantov/ultrasound-nerve-segmentation

  + https://github.com/intact-project/ild-cnn

  + https://github.com/scottykwok/cervix-roi-segmentation-by-unet

  + https://github.com/lishen/end2end-all-conv

  

- Tensorflow

  + https://github.com/imatge-upc/liverseg-2017-nipsws

  + 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

  

- Papers:

  + https://www2.warwick.ac.uk/fac/sci/dcs/people/research/csrkbb/tmi2016_ks.pdf

  + Sliding window approach

	  - http://people.idsia.ch/~juergen/nips2012.pdf

  + https://github.com/albarqouni/Deep-Learning-for-Medical-Applications#segmentation

	

 - Data:

   - https://luna16.grand-challenge.org/

   - https://camelyon16.grand-challenge.org/

   - https://github.com/beamandrew/medical-data

## Satellite images segmentation

  + https://github.com/mshivaprakash/sat-seg-thesis

  + https://github.com/KGPML/Hyperspectral

  + https://github.com/lopuhin/kaggle-dstl

  + https://github.com/mitmul/ssai

  + https://github.com/mitmul/ssai-cnn

  + https://github.com/azavea/raster-vision

  + https://github.com/nshaud/DeepNetsForEO

  + https://github.com/trailbehind/DeepOSM

  + https://github.com/mapbox/robosat

  + https://github.com/datapink/robosat.pink

 - Data:

  	+ https://github.com/RSIA-LIESMARS-WHU/RSOD-Dataset-

	+ SpaceNet[https://spacenetchallenge.github.io/]

	+ https://github.com/chrieke/awesome-satellite-imagery-datasets

## Video segmentation

  + https://github.com/shelhamer/clockwork-fcn

  + https://github.com/JingchunCheng/Seg-with-SPN

## Autonomous driving

  + https://github.com/MarvinTeichmann/MultiNet

  + https://github.com/MarvinTeichmann/KittiSeg

  + https://github.com/vxy10/p5_VehicleDetection_Unet [Keras]

  + https://github.com/ndrplz/self-driving-car

  + https://github.com/mvirgo/MLND-Capstone

  + https://github.com/zhujun98/semantic_segmentation/tree/master/fcn8s_road

  + 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/abbypa/NNProject_DeepMask

- TensorFlow

	+ https://github.com/warmspringwinds/tf-image-segmentation

	

- Caffe

	+ https://github.com/xiaolonw/nips14_loc_seg_testonly

	+ https://github.com/naibaf7/caffe_neural_tool

	

- torch

	+ https://github.com/erogol/seg-torch

	+ https://github.com/phillipi/pix2pix

	

- MXNet

	+ 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/

  + https://github.com/bharath272/sds_eccv2014

  

- Decoupled Deep Neural Network for Semi-supervised Semantic Segmentation

  + https://github.com/HyeonwooNoh/DecoupledNet

  

- Learning to Propose Objects

  + http://vladlen.info/publications/learning-to-propose-objects/ 

  + https://github.com/philkr/lpo

  

- Nonparametric Scene Parsing via Label Transfer

  + http://people.csail.mit.edu/celiu/LabelTransfer/code.html

  

- Other

  + https://github.com/cvjena/cn24

  + http://lmb.informatik.uni-freiburg.de/resources/software.php

  + https://github.com/NVIDIA/DIGITS/tree/master/examples/semantic-segmentation

  + http://jamie.shotton.org/work/code.html 

  + https://github.com/amueller/textonboost

  

## To look at

  + https://github.com/fchollet/keras/issues/6538

  + https://github.com/warmspringwinds/tensorflow_notes

  + https://github.com/kjw0612/awesome-deep-vision#semantic-segmentation

  + https://github.com/desimone/segmentation-models

  + https://github.com/nightrome/really-awesome-semantic-segmentation

  + https://github.com/kjw0612/awesome-deep-vision#semantic-segmentation

  + http://www.it-caesar.com/list-of-contemporary-semantic-segmentation-datasets/

  + https://github.com/MichaelXin/Awesome-Caffe#23-image-segmentation

  + https://github.com/warmspringwinds/pytorch-segmentation-detection

  + https://github.com/neuropoly/axondeepseg

  + https://github.com/petrochenko-pavel-a/segmentation_training_pipeline

## Blog posts, other:

  + https://handong1587.github.io/deep_learning/2015/10/09/segmentation.html

  + http://www.andrewjanowczyk.com/efficient-pixel-wise-deep-learning-on-large-images/

  + 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/semantic-segmentation

  + http://blog.qure.ai/notes/semantic-segmentation-deep-learning-review

  + https://medium.com/@barvinograd1/instance-embedding-instance-segmentation-without-proposals-31946a7c53e1