- U-Net [https://arxiv.org/pdf/1505.04597.pdf] [2015]
- https://github.com/zhixuhao/unet [Keras]
- https://github.com/jocicmarko/ultrasound-nerve-segmentation [Keras]
- https://github.com/EdwardTyantov/ultrasound-nerve-segmentation [Keras]
- https://github.com/ZFTurbo/ZF_UNET_224_Pretrained_Model [Keras]
- https://github.com/yihui-he/u-net [Keras]
- https://github.com/jakeret/tf_unet [Tensorflow]
- https://github.com/divamgupta/image-segmentation-keras [Keras]
- https://github.com/ZijunDeng/pytorch-semantic-segmentation [PyTorch]
- https://github.com/akirasosa/mobile-semantic-segmentation [Keras]
- https://github.com/orobix/retina-unet [Keras]
- https://github.com/qureai/ultrasound-nerve-segmentation-using-torchnet [Torch]
- https://github.com/ternaus/TernausNet [PyTorch]
- https://github.com/qubvel/segmentation_models [Keras]
- https://github.com/LeeJunHyun/Image_Segmentation#u-net [PyTorch]
- https://github.com/yassouali/pytorch_segmentation [PyTorch]
- https://lmb.informatik.uni-freiburg.de/people/ronneber/u-net/ [Caffe + Matlab]
- https://github.com/zhixuhao/unet [Keras]
- 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]
- DeepLab [https://arxiv.org/pdf/1606.00915.pdf] [2017]
- https://bitbucket.org/deeplab/deeplab-public/ [Caffe]
- https://github.com/cdmh/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]
- 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]
- 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]
- LinkNet [https://arxiv.org/pdf/1707.03718.pdf] [2017]
- DenseNet [https://arxiv.org/pdf/1611.09326.pdf] [2017]
- DilatedNet [https://arxiv.org/pdf/1511.07122.pdf] [2016]
- PixelNet [https://arxiv.org/pdf/1609.06694.pdf] [2016]
- 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]
- 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]
- https://github.com/holyseven/PSPNet-TF-Reproduce [Tensorflow]
- DeconvNet [https://arxiv.org/pdf/1505.04366.pdf] [2015]
- FRRN [https://arxiv.org/pdf/1611.08323.pdf] [2016]
- https://github.com/TobyPDE/FRRN [Lasagne]
- GCN [https://arxiv.org/pdf/1703.02719.pdf] [2017]
- 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]
- MultiNet [https://arxiv.org/pdf/1612.07695.pdf] [2016]
- Segaware [https://arxiv.org/pdf/1708.04607.pdf] [2017]
- Semantic Segmentation using Adversarial Networks [https://arxiv.org/pdf/1611.08408.pdf] [2016]
- 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]
- FPN [http://presentations.cocodataset.org/COCO17-Stuff-FAIR.pdf] [2017]
- R2U-Net [https://arxiv.org/ftp/arxiv/papers/1802/1802.06955.pdf] [2018]
- Attention U-Net [https://arxiv.org/pdf/1804.03999.pdf] [2018]
- 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]
- BiSeNet [https://arxiv.org/pdf/1808.00897.pdf] [2018]
- https://github.com/ooooverflow/BiSeNet [PyTorch]
- https://github.com/ycszen/TorchSeg [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]
- 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]
- FCIS [https://arxiv.org/pdf/1611.07709.pdf]
- https://github.com/msracver/FCIS [MxNet]
- MNC [https://arxiv.org/pdf/1512.04412.pdf]
- DeepMask [https://arxiv.org/pdf/1506.06204.pdf]
- SharpMask [https://arxiv.org/pdf/1603.08695.pdf]
- 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]
- RIS [https://arxiv.org/pdf/1511.08250.pdf]
- https://github.com/bernard24/RIS [Torch]
- FastMask [https://arxiv.org/pdf/1612.08843.pdf]
- BlitzNet [https://arxiv.org/pdf/1708.02813.pdf]
- https://github.com/dvornikita/blitznet [Tensorflow]
- PANet [https://arxiv.org/pdf/1803.01534.pdf] [2018]
- 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]
- 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]
- pix2pix [https://arxiv.org/pdf/1611.07004.pdf] [2018]
- pix2pixHD [https://arxiv.org/pdf/1711.11585.pdf] [2018]
- Probalistic Unet [https://arxiv.org/pdf/1806.05034.pdf] [2018]
- 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
- 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
- INRIA Annotations for Graz-02 (IG02)
- Pratheepan Dataset
- Clothing Co-Parsing (CCP) Dataset
- Inria Aerial Image
- ApolloScape
- UrbanMapper3D
- RoadDetector
- Cityscapes
- CamVid
- Inria Aerial Image Labeling
- 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]
- [Cityscapes dataset] https://github.com/phillipi/pix2pix/tree/master/scripts/eval_cityscapes
- 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
- 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
- https://github.com/tangzhenyu/SemanticSegmentation_DL
- https://github.com/nightrome/really-awesome-semantic-segmentation
- https://github.com/JackieZhangdx/InstanceSegmentationList
-
DIGITS
-
U-Net: Convolutional Networks for Biomedical Image Segmentation
- http://lmb.informatik.uni-freiburg.de/people/ronneber/u-net/
- apache/mxnet#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
-
Keras
-
Tensorflow
-
Using Convolutional Neural Networks (CNN) for Semantic Segmentation of Breast Cancer Lesions (BRCA)
-
Papers:
-
Data:
- 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/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
-
Keras
-
TensorFlow
-
Caffe
-
torch
-
MXNet
-
Simultaneous detection and segmentation
-
Decoupled Deep Neural Network for Semi-supervised Semantic Segmentation
-
Learning to Propose Objects
-
Nonparametric Scene Parsing via Label Transfer
-
Other
- keras-team/keras#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
- 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