/Real-Time-Semantic-Segmentation

lightweight and efficient cnn for semantic segmentation, my blog address:

Primary LanguageOpenEdge ABLMIT LicenseMIT

Real-time network for mobile devices

Google

Learning Transferable Architectures for Scalable Image Recognition NASNet

MnasNet: Platform-Aware Neural Architecture Search for Mobile MnasNet

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications MobileNets

MobileNetV2: Inverted Residuals and Linear Bottlenecks MobileNetV2 MobileNetV2-pytorch)

Searching for Efficient Multi-Scale Architectures for Dense Image Prediction

Megvill

ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices ShuffleNet

ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design ShuffleNet V2 Shufflenet-v2-Pytorch

Microsoft Research

Interleaved Group Convolutions for Deep Neural Networks IGCV

IGCV2: Interleaved Structured Sparse Convolutional Neural Networks IGCV2

IGCV3: Interleaved Low-Rank Group Convolutions for Efficient Deep Neural Networks IGCV3

Accelerating Deep Neural Networks with Spatial Bottleneck Modules

Others

CondenseNet: An Efficient DenseNet using Learned Group Convolutions CondenseNet

ChamNet: Towards Efficient Network Design through Platform-Aware Model AdaptationChamNet

Papers for real-time semantic segmentation

  1. A Comparative Study of Real-time Semantic Segmentation for Autonomous Driving
  2. Analysis of efficient CNN design techniques for semantic segmentation
  3. Real-time Semantic Image Segmentation via Spatial Sparsity arxiv2017
  4. ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation ENet
  5. ICNet for Real-Time Semantic Segmentation on High-Resolution Images ICNet
  6. Speeding up Semantic Segmentation for Autonomous Driving SQNet
  7. Efficient ConvNet for Real-time Semantic Segmentation EConvNet
  8. ERFNet: Efficient Residual Factorized ConvNet for Real-time Semantic Segmentation ERFNet
  9. Efficient Dense Modules of Asymmetric Convolution for Real-Time Semantic SegmentationEDANet
  10. ESPNet: Efficient Spatial Pyramid of Dilated Convolutions for Semantic Segmentation ESPNet
  11. ESPNetv2: A Light-weight, Power Efficient, and General Purpose Convolutional Neural Network ESPNetv2 code
  12. Concentrated-Comprehensive Convolutions for lightweight semantic segmentation CCC-ERFNet
  13. BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation BiSeNet
  14. Light-Weight RefineNet for Real-Time Semantic SegmentationLight-Weight RefineNet code
  15. ShelfNet for Real-time Semantic Segmentation arxiv2018 code
  16. LadderNet: MULTI-PATH NETWORKS BASED ON U-NET FOR MEDICAL IMAGE SEGMENTATION LadderNet
  17. ShuffleSeg: REAL-TIME SEMANTIC SEGMENTATION NETWORK ShuffleSeg
  18. RTSeg: REAL-TIME SEMANTIC SEGMENTATION COMPARATIVE STUDY RTSeg
  19. ContextNet: Exploring Context and Detail for Semantic Segmentation in Real-time ContextNet
  20. CGNet: A Light-weight Context Guided Network for Semantic Segmentation arxiv2018 code
  21. Design of Real-time Semantic Segmentation Decoder for Automated Driving arxiv2019
  22. DSNet: DSNet for Real-Time Driving Scene Semantic Segmentation DSNet
  23. Fast-SCNN: Fast Semantic Segmentation Network arxiv2019
  24. An efficient solution for semantic segmentation: ShuffleNet V2 with atrous separable convolutions arxiv2019

Some useful links & repo

  1. Awesome-model-compression-and-acceleration

  2. awesome-model-compression-and-acceleration

  3. real-time-network

  4. ConvNets

  5. awesome-computer-vision-models

  6. fast_portrait_segmentation

  7. Light_CNN

  8. read--paper--list

  9. awesome--computer--vision--models

  10. Semantic-Segmentation-Paper-Record

  11. Segmentation-Paper-Reading-Notes

  12. CV-News

  13. research-method

  14. segmentation-paper-reading-notes

  15. CV-Papers-Datasets

  16. useful-computer-vision-phd-resources

  17. AI_Projects

  18. awesome-embedded-ai

  19. Segmentation.X

  20. SemanticSegmentation_DL

  21. CV_resources

  22. 无人驾驶资源收集

  23. 无人驾驶相关论文速递

  24. AI papers

  25. Paper-Notes