转：Awesome Image/Video segmentation

淩亂°似流年 2022-01-09 11:13 260阅读 0赞

#

Semantic segmentation

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

  • 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]
    • /images/20220109/bb9483d0911a4f399db55befde17e4a0.png [Keras]
    • /images/20220109/e997a99e1ceb42f2a4ded045e01deee2.png [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]

  • 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]

  • 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]
    • /images/20220109/bb9483d0911a4f399db55befde17e4a0.png [Keras]
    • /images/20220109/e997a99e1ceb42f2a4ded045e01deee2.png [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]

  • 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]

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

  • DenseNet [https://arxiv.org/pdf/1608.06993.pdf] [2018]

    • https://github.com/flyyufelix/DenseNet-Keras [Keras]

  • Tiramisu [https://arxiv.org/pdf/1611.09326.pdf] [2017]

    • https://github.com/0bserver07/One-Hundred-Layers-Tiramisu [Keras]
    • https://github.com/SimJeg/FC-DenseNet [Lasagne]

  • 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/ai-tor/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]
    • /images/20220109/e997a99e1ceb42f2a4ded045e01deee2.png [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]

  • 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]

  • 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]

    • 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]

  • 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]

    • 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]

  • 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]

#

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]

  • 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]

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

#

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

  • 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
  • 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

#

Benchmarks

#

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

#

Results:

  • MSRC-21
  • Cityscapes
  • VOC2012
  • https://paperswithcode.com/task/semantic-segmentation

#

Metrics

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

#

Losses

  • 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://github.com/tangzhenyu/SemanticSegmentation_DL
  • https://github.com/nightrome/really-awesome-semantic-segmentation
  • https://github.com/JackieZhangdx/InstanceSegmentationList

#

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
    • /images/20220109/5c6c11b514f24108993e1be3a58136c2.png
    • 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

  • 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

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

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