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awesome-semantic-segmentation
awesome-semantic-segmentation - list of awesome things around semantic segmentation :tada:
https://github.com/damminhtien/awesome-semantic-segmentation
Last synced: about 13 hours ago
JSON representation
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Review list of Semantic Segmentation
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Variants
- towardds
- towardds
- towardds
- towardds
- nanonet
- University of Gour Banga,India
- mc.ai
- towardds
- jeremyjordan.me
- arxiv - summary-recent-progress-in-semantic-image-segmentation-d7b93ee1b705)) :star: :star: :star: :star:
- medium-towardds
- slide
- arxiv
- towardds
- towardds
- towardds
- towardds
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- towardds
- towardds
- towardds
- towardds
- towardds
- towardds
- towardds
- towardds
- University of Gour Banga,India
- towardds
- arxiv
- towardds
- towardds
- towardds
- towardds
- towardds
- towardds
- towardds
- towardds
- towardds
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- towardds
- towardds
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- towardds
- towardds
- towardds
- towardds
- towardds
- towardds
- towardds
- towardds
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Related techniques
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Variants
- Unpooling
- Unpooling
- Atrous/ Dilated Convolution
- Transpose Convolution
- Unpooling
- CRF
- Unpooling
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- Unpooling
- Unpooling
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State-Of-The-Art (SOTA) methods of Semantic Segmentation
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- Rethinking Pre-training and Self-training
- Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation - deeplab-v3-plus), [Pytorch](https://github.com/jfzhang95/pytorch-deeplab-xception), [Demo](https://colab.sandbox.google.com/github/tensorflow/models/blob/master/research/deeplab/deeplab_demo.ipynb) |
- Rethinking Atrous Convolution for Semantic Image Segmentation - deeplab-v3) |
- Learning a Discriminative Feature Network for Semantic Segmentation
- Pyramid Scene Parsing Network - segmentation-detection), [Pytorch](https://github.com/kazuto1011/pspnet-pytorch) |
- Wider or Deeper: Revisiting the ResNet Model for Visual Recognition
- RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentation - keras) |
- Large Kernel Matters -- Improve Semantic Segmentation by Global Convolutional Network
- Conditional Random Fields as Recurrent Neural Networks
- ParseNet: Looking Wider to See Better - parsenet) |
- Multi-Scale Context Aggregation by Dilated Convolutions
- Fully Convolutional Networks for Semantic Segmentation
- Rethinking Pre-training and Self-training
- Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation - deeplab-v3-plus), [Pytorch](https://github.com/jfzhang95/pytorch-deeplab-xception), [Demo](https://colab.sandbox.google.com/github/tensorflow/models/blob/master/research/deeplab/deeplab_demo.ipynb) |
- Rethinking Atrous Convolution for Semantic Image Segmentation - deeplab-v3) |
- Learning a Discriminative Feature Network for Semantic Segmentation
- Pyramid Scene Parsing Network - segmentation-detection), [Pytorch](https://github.com/kazuto1011/pspnet-pytorch) |
- Wider or Deeper: Revisiting the ResNet Model for Visual Recognition
- Conditional Random Fields as Recurrent Neural Networks
- ParseNet: Looking Wider to See Better - parsenet) |
- Multi-Scale Context Aggregation by Dilated Convolutions
- Fully Convolutional Networks for Semantic Segmentation
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Variants
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Case studies
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Variants
- 1st place - science-bowl-2018/discussion/61170), [3rd](https://www.kaggle.com/c/data-science-bowl-2018/discussion/56393), [4th](https://www.kaggle.com/c/data-science-bowl-2018/discussion/55118), [5th](https://www.kaggle.com/c/data-science-bowl-2018/discussion/56326), [10th](https://www.kaggle.com/c/data-science-bowl-2018/discussion/56238)
- 4th place - ship-detection/discussion/71782)
- 1st place
- 1st place
- 1st place - wheat-detection/discussion/175961), [3rd](https://www.kaggle.com/c/global-wheat-detection/discussion/179055)
- 1st place - steel-defect-detection/discussion/114716), [7th](https://www.kaggle.com/c/severstal-steel-defect-detection/discussion/114215)
- 1st place - protein-atlas-image-classification/discussion/77731)
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Datasets
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Programming Languages
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