Semantic Segmentation Evaluation
Annotation Tools:
https://github.com/AKSHAYUBHAT/ImageSegmentation
https://github.com/kyamagu/js-segment-annotator
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/
Results:
http://rodrigob.github.io/are_we_there_yet/build/semantic_labeling_datasets_results.html
https://github.com/kjw0612/awesome-deep-vision#semantic-segmentation
Semantic Segmentation Code:
Simultaneous detection and segmentation
http://www.eecs.berkeley.edu/Research/Projects/CS/vision/shape/sds/
https://github.com/bharath272/sds_eccv2014
A MatConvNet-based implementation of the Fully-Convolutional Networks for image segmentation
'Fully Convolutional Models for Semantic Segmentation', Jonathan Long, Evan Shelhamer and Trevor Darrell, CVPR, 2015
https://github.com/vlfeat/matconvnet-fcn
https://github.com/shelhamer/fcn.berkeleyvision.org
TextonBoost: Joint Appearance, Shape and Context Modeling for Multi-Class Object Recognition and Segmentation
http://jamie.shotton.org/work/code.html
https://github.com/amueller/textonboost
Semantic Segmentation for Aerial Imagery using Convolutional Neural Network
https://github.com/mitmul/ssai
"Learning Deconvolution Network for Semantic Segmentation"
https://github.com/HyeonwooNoh/DeconvNet
"Decoupled Deep Neural Network for Semi-supervised Semantic Segmentation"
https://github.com/HyeonwooNoh/DecoupledNet
"Convolutional (Patch) Networks for Semantic Segmentation"
https://github.com/cvjena/cn24
"Learning to Propose Objects"
http://vladlen.info/publications/learning-to-propose-objects/
https://github.com/philkr/lpo
"Efficient Inference in Fully Connected CRFs with Gaussian Edge Potentials"
http://vladlen.info/publications/efficient-inference-in-fully-connected-crfs-with-gaussian-edge-potentials/
"Nonparametric Scene Parsing via Label Transfer"
http://people.csail.mit.edu/celiu/LabelTransfer/code.html
"Conditional Random Fields as Recurrent Neural Networks"
https://github.com/torrvision/crfasrnn
Graphical Models / Conditional Random Field Toolbox
http://users.cecs.anu.edu.au/~jdomke/JGMT/
SegNet
http://mi.eng.cam.ac.uk/projects/segnet/tutorial.html
https://github.com/alexgkendall/caffe-segnet
DeepLab
https://bitbucket.org/deeplab/deeplab-public/
http://ccvl.stat.ucla.edu/software/deeplab/deeplab/
https://github.com/cdmh/deeplab-public
https://github.com/cvlab-epfl/densecrf
Efficient Inference in Fully Connected CRFs with Gaussian Edge Potentials
http://www.philkr.net/home/densecrf
https://github.com/amiltonwong/segmentation/blob/master/segmentation.ipynb
https://github.com/jliemansifry/super-simple-semantic-segmentation
mxnet fcn-xs
https://github.com/tornadomeet/mxnet/tree/seg/example/fcn-xs
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
http://cvlab.postech.ac.kr/research/deconvnet/
Pixelwise segmentation
https://github.com/naibaf7/caffe_neural_tool
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