awesome-semantic-segmentation
by mrgloom
awesome-semantic-segmentation
AI summary
Segmentation models
A curated list of semantic segmentation models and their implementations in various deep learning frameworks.
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What's in the list
445 links in 174 sections, with live GitHub stats.activeno commit in 2y
Networks by architecture / Semantic segmentation
- https://arxiv.org/pdf/1505.04597.pdf]
U-Net [ [2015]
Networks by architecture / Semantic segmentation / https://arxiv.org/pdf/1505.04597.pdf]
https://github.com/jakeret/tf_unet
[Tensorflow]
- https://lmb.informatik.uni-freiburg.de/people/ronneber/u-net/
[Caffe + Matlab]
Networks by architecture / Semantic segmentation
- https://arxiv.org/pdf/1511.00561.pdf]
SegNet [ [2016]
Networks by architecture / Semantic segmentation / https://arxiv.org/pdf/1511.00561.pdf]
https://github.com/andreaazzini/segnet
[Tensorflow]
https://github.com/tkuanlun350/Tensorflow-SegNet
[Tensorflow]
Networks by architecture / Semantic segmentation
- https://arxiv.org/pdf/1606.00915.pdf]
DeepLab [ [2017]
Networks by architecture / Semantic segmentation / https://arxiv.org/pdf/1606.00915.pdf]
https://github.com/chenxi116/TF-deeplab
[Tensorflow]
Networks by architecture / Semantic segmentation
- https://arxiv.org/pdf/1605.06211.pdf]
FCN [ [2016]
Networks by architecture / Semantic segmentation / https://arxiv.org/pdf/1605.06211.pdf]
https://github.com/vlfeat/matconvnet-fcn
[MatConvNet]
https://github.com/shekkizh/FCN.tensorflow
[Tensorflow]
https://github.com/seewalker/tf-pixelwise
[Tensorflow]
https://github.com/wkentaro/fcn
[Chainer]
https://github.com/muyang0320/tf-fcn
[Tensorflow]
https://github.com/petrama/VGGSegmentation
[Tensorflow]
Networks by architecture / Semantic segmentation
- https://arxiv.org/pdf/1606.02147.pdf]
ENet [ [2016]
Networks by architecture / Semantic segmentation / https://arxiv.org/pdf/1606.02147.pdf]
https://github.com/fregu856/segmentation
[Tensorflow]
https://github.com/kwotsin/TensorFlow-ENet
[Tensorflow]
Networks by architecture / Semantic segmentation
- https://arxiv.org/pdf/1707.03718.pdf]
LinkNet [ [2017]
Networks by architecture / Semantic segmentation / https://arxiv.org/pdf/1707.03718.pdf]
Networks by architecture / Semantic segmentation
- https://arxiv.org/pdf/1611.09326.pdf]
DenseNet [ [2017]
Networks by architecture / Semantic segmentation / https://arxiv.org/pdf/1611.09326.pdf]
Networks by architecture / Semantic segmentation
- https://arxiv.org/pdf/1511.07122.pdf]
DilatedNet [ [2016]
Networks by architecture / Semantic segmentation / https://arxiv.org/pdf/1511.07122.pdf]
Networks by architecture / Semantic segmentation
- https://arxiv.org/pdf/1609.06694.pdf]
PixelNet [ [2016]
Networks by architecture / Semantic segmentation / https://arxiv.org/pdf/1609.06694.pdf]
Networks by architecture / Semantic segmentation
- https://arxiv.org/pdf/1704.08545.pdf]
ICNet [ [2017]
Networks by architecture / Semantic segmentation / https://arxiv.org/pdf/1704.08545.pdf]
https://github.com/hellochick/ICNet-tensorflow
[Tensorflow]
Networks by architecture / Semantic segmentation
Networks by architecture / Semantic segmentation / http://www.robesafe.uah.es/personal/eduardo.romera/pdfs/Romera17iv.pdf]
Networks by architecture / Semantic segmentation
- https://arxiv.org/pdf/1611.06612.pdf]
RefineNet [ [2016]
Networks by architecture / Semantic segmentation / https://arxiv.org/pdf/1611.06612.pdf]
https://github.com/guosheng/refinenet
[MatConvNet]
Networks by architecture / Semantic segmentation
Networks by architecture / Semantic segmentation / https://arxiv.org/pdf/1612.01105.pdf,https://hszhao.github.io/projects/pspnet/]
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/holyseven/PSPNet-TF-Reproduce
[Tensorflow]
Networks by architecture / Semantic segmentation
- https://arxiv.org/pdf/1505.04366.pdf]
DeconvNet [ [2015]
Networks by architecture / Semantic segmentation / https://arxiv.org/pdf/1505.04366.pdf]
Networks by architecture / Semantic segmentation
- https://arxiv.org/pdf/1611.08323.pdf]
FRRN [ [2016]
Networks by architecture / Semantic segmentation / https://arxiv.org/pdf/1611.08323.pdf]
https://github.com/TobyPDE/FRRN
[Lasagne]
Networks by architecture / Semantic segmentation
- https://arxiv.org/pdf/1703.02719.pdf]
GCN [ [2017]
Networks by architecture / Semantic segmentation / https://arxiv.org/pdf/1703.02719.pdf]
Networks by architecture / Semantic segmentation
- https://arxiv.org/pdf/1605.02264.pdf]
LRR [ [2016]
Networks by architecture / Semantic segmentation / https://arxiv.org/pdf/1605.02264.pdf]
https://github.com/golnazghiasi/LRR
[Matconvnet]
Networks by architecture / Semantic segmentation
- https://arxiv.org/pdf/1702.08502.pdf]
DUC, HDC [ [2017]
Networks by architecture / Semantic segmentation / https://arxiv.org/pdf/1702.08502.pdf]
Networks by architecture / Semantic segmentation
- https://arxiv.org/pdf/1612.07695.pdf]
MultiNet [ [2016]
Networks by architecture / Semantic segmentation / https://arxiv.org/pdf/1612.07695.pdf]
Networks by architecture / Semantic segmentation
- https://arxiv.org/pdf/1708.04607.pdf]
Segaware [ [2017]
Networks by architecture / Semantic segmentation / https://arxiv.org/pdf/1708.04607.pdf]
Networks by architecture / Semantic segmentation
- https://arxiv.org/pdf/1611.08408.pdf]
Semantic Segmentation using Adversarial Networks [ [2016]
Networks by architecture / Semantic segmentation / https://arxiv.org/pdf/1611.08408.pdf]
Networks by architecture / Semantic segmentation
- https://arxiv.org/pdf/1705.06820.pdf]
PixelDCN [ [2017]
Networks by architecture / Semantic segmentation / https://arxiv.org/pdf/1705.06820.pdf]
https://github.com/HongyangGao/PixelDCN
[Tensorflow]
Networks by architecture / Semantic segmentation
- https://arxiv.org/pdf/1803.03816.pdf]
ShuffleSeg [ [2018]
Networks by architecture / Semantic segmentation / https://arxiv.org/pdf/1803.03816.pdf]
https://github.com/MSiam/TFSegmentation
[TensorFlow]
Networks by architecture / Semantic segmentation
- https://arxiv.org/pdf/1802.10349.pdf]
AdaptSegNet [ [2018]
Networks by architecture / Semantic segmentation / https://arxiv.org/pdf/1802.10349.pdf]
Networks by architecture / Semantic segmentation
- https://arxiv.org/pdf/1702.08502.pdf]
TuSimple-DUC [ [2018]
Networks by architecture / Semantic segmentation / https://arxiv.org/pdf/1702.08502.pdf]
Networks by architecture / Semantic segmentation
Networks by architecture / Semantic segmentation / http://presentations.cocodataset.org/COCO17-Stuff-FAIR.pdf]
Networks by architecture / Semantic segmentation
- https://arxiv.org/ftp/arxiv/papers/1802/1802.06955.pdf]
R2U-Net [ [2018]
Networks by architecture / Semantic segmentation / https://arxiv.org/ftp/arxiv/papers/1802/1802.06955.pdf]
Networks by architecture / Semantic segmentation
- https://arxiv.org/pdf/1804.03999.pdf]
Attention U-Net [ [2018]
Networks by architecture / Semantic segmentation / https://arxiv.org/pdf/1804.03999.pdf]
Networks by architecture / Semantic segmentation
- https://arxiv.org/pdf/1809.02983.pdf]
DANet [ [2018]
Networks by architecture / Semantic segmentation / https://arxiv.org/pdf/1809.02983.pdf]
https://github.com/junfu1115/DANet
[PyTorch]
Networks by architecture / Semantic segmentation
- https://arxiv.org/pdf/1811.11254.pdf]
ShelfNet [ [2018]
Networks by architecture / Semantic segmentation / https://arxiv.org/pdf/1811.11254.pdf]
Networks by architecture / Semantic segmentation
- https://arxiv.org/pdf/1810.07810.pdf]
LadderNet [ [2018]
Networks by architecture / Semantic segmentation / https://arxiv.org/pdf/1810.07810.pdf]
Networks by architecture / Semantic segmentation
- https://arxiv.org/pdf/1808.00897.pdf]
BiSeNet [ [2018]
Networks by architecture / Semantic segmentation / https://arxiv.org/pdf/1808.00897.pdf]
https://github.com/ycszen/TorchSeg
[PyTorch]
Networks by architecture / Semantic segmentation
- https://arxiv.org/pdf/1803.06815.pdf]
ESPNet [ [2018]
Networks by architecture / Semantic segmentation / https://arxiv.org/pdf/1803.06815.pdf]
https://github.com/sacmehta/ESPNet
[PyTorch]
Networks by architecture / Semantic segmentation
- https://arxiv.org/pdf/1804.09337.pdf]
DFN [ [2018]
Networks by architecture / Semantic segmentation / https://arxiv.org/pdf/1804.09337.pdf]
https://github.com/ycszen/TorchSeg
[PyTorch]
Networks by architecture / Semantic segmentation
- https://arxiv.org/pdf/1811.11721.pdf]
CCNet [ [2018]
Networks by architecture / Semantic segmentation / https://arxiv.org/pdf/1811.11721.pdf]
https://github.com/speedinghzl/CCNet
[PyTorch]
Networks by architecture / Semantic segmentation
Networks by architecture / Semantic segmentation / http://openaccess.thecvf.com/content_cvpr_2018/papers/Yang_DenseASPP_for_Semantic_CVPR_2018_paper.pdf]
Networks by architecture / Semantic segmentation
- https://arxiv.org/pdf/1902.04502.pdf]
Fast-SCNN [ [2019]
Networks by architecture / Semantic segmentation / https://arxiv.org/pdf/1902.04502.pdf]
Networks by architecture / Semantic segmentation
- https://arxiv.org/pdf/1904.04514.pdf]
HRNet [ [2019]
Networks by architecture / Semantic segmentation / https://arxiv.org/pdf/1904.04514.pdf]
Networks by architecture / Semantic segmentation
- https://hszhao.github.io/papers/eccv18_psanet.pdf]
PSANet [ [2018]
Networks by architecture / Semantic segmentation / https://hszhao.github.io/papers/eccv18_psanet.pdf]
Networks by architecture / Semantic segmentation
- https://arxiv.org/pdf/1901.03784.pdf]
UPSNet [ [2019]
Networks by architecture / Semantic segmentation / https://arxiv.org/pdf/1901.03784.pdf]
Networks by architecture / Semantic segmentation
- https://arxiv.org/pdf/1805.04777.pdf]
ConvCRF [ [2018]
Networks by architecture / Semantic segmentation / https://arxiv.org/pdf/1805.04777.pdf]
Networks by architecture / Semantic segmentation
- https://arxiv.org/pdf/1906.02849.pdf]
Multi-scale Guided Attention for Medical Image Segmentation [ [2019]
Networks by architecture / Semantic segmentation / https://arxiv.org/pdf/1906.02849.pdf]
Networks by architecture / Semantic segmentation
- https://arxiv.org/pdf/1904.02216.pdf]
DFANet [ [2019]
Networks by architecture / Semantic segmentation / https://arxiv.org/pdf/1904.02216.pdf]
Networks by architecture / Semantic segmentation
- https://arxiv.org/pdf/1908.03093.pdf]
ExtremeC3Net [ [2019]
Networks by architecture / Semantic segmentation / https://arxiv.org/pdf/1908.03093.pdf]
Networks by architecture / Semantic segmentation
- https://arxiv.org/pdf/1803.08904.pdf]
EncNet [ [2018]
Networks by architecture / Semantic segmentation / https://arxiv.org/pdf/1803.08904.pdf]
Networks by architecture / Semantic segmentation
- https://arxiv.org/pdf/1807.10165.pdf]
Unet++ [ [2018]
Networks by architecture / Semantic segmentation / https://arxiv.org/pdf/1807.10165.pdf]
Networks by architecture / Semantic segmentation
- https://arxiv.org/pdf/1903.11816.pdf]
FastFCN [ [2019]
Networks by architecture / Semantic segmentation / https://arxiv.org/pdf/1903.11816.pdf]
https://github.com/wuhuikai/FastFCN
[PyTorch]
Networks by architecture / Semantic segmentation
- https://www.yongliangyang.net/docs/mobilePotrait_c&g19.pdf]
PortraitNet [ [2019]
Networks by architecture / Semantic segmentation / https://www.yongliangyang.net/docs/mobilePotrait_c&g19.pdf]
Networks by architecture / Semantic segmentation
- https://arxiv.org/pdf/1907.05740.pdf]
GSCNN [ [2019]
Networks by architecture / Semantic segmentation / https://arxiv.org/pdf/1907.05740.pdf]
https://github.com/nv-tlabs/gscnn
[PyTorch]
Networks by architecture / Instance aware segmentation
Networks by architecture / Instance aware segmentation / https://arxiv.org/pdf/1611.07709.pdf]
Networks by architecture / Instance aware segmentation
Networks by architecture / Instance aware segmentation / https://arxiv.org/pdf/1512.04412.pdf]
Networks by architecture / Instance aware segmentation
- https://arxiv.org/pdf/1506.06204.pdf]
DeepMask [
Networks by architecture / Instance aware segmentation / https://arxiv.org/pdf/1506.06204.pdf]
Networks by architecture / Instance aware segmentation
- https://arxiv.org/pdf/1603.08695.pdf]
SharpMask [
Networks by architecture / Instance aware segmentation / https://arxiv.org/pdf/1603.08695.pdf]
Networks by architecture / Instance aware segmentation
- https://arxiv.org/pdf/1703.06870.pdf]
Mask-RCNN [
Networks by architecture / Instance aware segmentation / https://arxiv.org/pdf/1703.06870.pdf]
https://github.com/CharlesShang/FastMaskRCNN
[Tensorflow]
Networks by architecture / Instance aware segmentation
Networks by architecture / Instance aware segmentation / https://arxiv.org/pdf/1511.08250.pdf]
Networks by architecture / Instance aware segmentation
- https://arxiv.org/pdf/1612.08843.pdf]
FastMask [
Networks by architecture / Instance aware segmentation / https://arxiv.org/pdf/1612.08843.pdf]
Networks by architecture / Instance aware segmentation
- https://arxiv.org/pdf/1708.02813.pdf]
BlitzNet [
Networks by architecture / Instance aware segmentation / https://arxiv.org/pdf/1708.02813.pdf]
https://github.com/dvornikita/blitznet
[Tensorflow]
Networks by architecture / Instance aware segmentation
- https://arxiv.org/pdf/1803.01534.pdf]
PANet [ [2018]
Networks by architecture / Instance aware segmentation / https://arxiv.org/pdf/1803.01534.pdf]
Networks by architecture / Instance aware segmentation
- https://arxiv.org/pdf/1805.10180.pdf]
PAN [ [2018]
Networks by architecture / Instance aware segmentation / https://arxiv.org/pdf/1805.10180.pdf]
Networks by architecture / Instance aware segmentation
- https://arxiv.org/pdf/1806.00844.pdf]
TernausNetV2 [ [2018]
Networks by architecture / Instance aware segmentation / https://arxiv.org/pdf/1806.00844.pdf]
Networks by architecture / Instance aware segmentation
- https://arxiv.org/pdf/1903.00241.pdf]
MS R-CNN [ [2019]
Networks by architecture / Instance aware segmentation / https://arxiv.org/pdf/1903.00241.pdf]
Networks by architecture / Instance aware segmentation
- https://arxiv.org/pdf/1909.07829.pdf]
AdaptIS [ [2019]
Networks by architecture / Instance aware segmentation / https://arxiv.org/pdf/1909.07829.pdf]
https://github.com/saic-vul/adaptis
[MxNet][PyTorch]
Networks by architecture / Instance aware segmentation
- https://arxiv.org/pdf/1803.10683.pdf]
Pose2Seg [ [2019]
Networks by architecture / Instance aware segmentation / https://arxiv.org/pdf/1803.10683.pdf]
Networks by architecture / Instance aware segmentation
- https://arxiv.org/pdf/1904.02689.pdf]
YOLACT [ [2019]
Networks by architecture / Instance aware segmentation / https://arxiv.org/pdf/1904.02689.pdf]
https://github.com/dbolya/yolact
[PyTorch]
Networks by architecture / Instance aware segmentation
- https://arxiv.org/pdf/1911.06667.pdf]
CenterMask [ [2019]
Networks by architecture / Instance aware segmentation / https://arxiv.org/pdf/1911.06667.pdf]
Networks by architecture / Instance aware segmentation
- https://arxiv.org/pdf/1908.07801.pdf]
InstaBoost [ [2019]
Networks by architecture / Instance aware segmentation / https://arxiv.org/pdf/1908.07801.pdf]
Networks by architecture / Instance aware segmentation
- https://arxiv.org/pdf/1912.04488.pdf]
SOLO [ [2019]
Networks by architecture / Instance aware segmentation / https://arxiv.org/pdf/1912.04488.pdf]
https://github.com/WXinlong/SOLO
[PyTorch]
Networks by architecture / Instance aware segmentation
- https://arxiv.org/pdf/2003.10152.pdf]
SOLOv2 [ [2020]
Networks by architecture / Instance aware segmentation / https://arxiv.org/pdf/2003.10152.pdf]
https://github.com/WXinlong/SOLO
[PyTorch]
Networks by architecture / Instance aware segmentation
Networks by architecture / Weakly-supervised segmentation
Networks by architecture / Weakly-supervised segmentation / https://arxiv.org/pdf/1603.06098.pdf]
RNN
RNN / https://arxiv.org/pdf/1505.00393.pdf]
https://github.com/fvisin/reseg
[Lasagne]
RNN
RNN / https://arxiv.org/pdf/1511.07053.pdf]
https://github.com/fvisin/reseg
[Lasagne]
RNN
RNN / https://arxiv.org/pdf/1511.08250.pdf]
RNN
RNN / http://www.robots.ox.ac.uk/%7Eszheng/papers/CRFasRNN.pdf]
https://github.com/NP-coder/CLPS1520Project
[Tensorflow]
https://github.com/renmengye/rec-attend-public
[Tensorflow]
GANS
- https://arxiv.org/pdf/1611.07004.pdf]
pix2pix [ [2018]
GANS / https://arxiv.org/pdf/1611.07004.pdf]
GANS
- https://arxiv.org/pdf/1711.11585.pdf]
pix2pixHD [ [2018]
GANS / https://arxiv.org/pdf/1711.11585.pdf]
GANS
- https://arxiv.org/pdf/1806.05034.pdf]
Probalistic Unet [ [2018]
GANS / https://arxiv.org/pdf/1806.05034.pdf]
Graphical Models (CRF, MRF)
Datasets:
Benchmarks
https://github.com/MSiam/TFSegmentation
[Tensorflow]
https://github.com/CSAILVision/sceneparsing
[Caffe+Matlab]
https://github.com/ycszen/TorchSeg
[PyTorch]
https://github.com/hszhao/semseg
[PyTorch]
https://github.com/PaddlePaddle/PaddleSeg
[PaddlePaddle]
Evaluation code
https://github.com/phillipi/pix2pix/tree/master/scripts/eval_cityscapes
[Cityscapes dataset]
Starter code
Annotation Tools:
Results:
Metrics
Losses
Other lists
Medical image segmentation:
Medical image segmentation: / Sliding window approach
Medical image segmentation:
Satellite images segmentation
Satellite images segmentation / Data:
Video segmentation
Autonomous driving
Networks by framework (Older list)
Papers and Code (Older list)
To look at
Blog posts, other:
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Featured in 5 awesome lists
Each link jumps to the spot where the list mentions awesome-semantic-segmentation.
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