/ConvNets

research and implementations of CNNs and their applications

MIT LicenseMIT


CNN Architectures

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  • [LeNet(1998)] [paper]
  • [LeNet-5 (2010)] [paper]
  • [AlexNet (2012)] [paper]
  • [ZFNet(2013)] [paper]
  • [VGGNet (2014)] [paper]
  • [GoogleNet/Inception(2014)] [paper]
  • [FCN(2014)] [paper]
  • [RCNN(2014)] [paper]
  • [Deeply-supervised networks(2014)] [paper]
  • [ResNet(2015)] [paper]
  • [Ladder network(2015)] [paper]
  • [YOLO(2015)] [Paper]
  • [FractalNet (2016)] [paper]
  • [PolyNet/Inception-Residual(2016)] [paper]
  • [DenseNet(2016)] [paper] [code]
  • [SegNet(2016)] [paper]
  • [fast region based CNN(2016)] [paper]
  • [Look up based CNN(2016)] [paper]
  • [Deep network with stochastic depth(2016)] [paper]
  • [ResNeXt(2016)] [paper]
  • [SqueezeNet(2016)] [paper] [code]
  • [CapsNet(2017)] [paper]
  • [MobileNets(2017)] [paper]
  • [Xception(2017)] [paper]
  • [IRCNN(2017)][paper]
  • [ViP CNN(2017)] [paper]
  • [Squeeze-and-Excitation Networks(2017)][Paper] [code]
  • [MobileFaceNets(2018)] [paper]
  • [DCNet and DCNet++(2018)] [paper]

Applications

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Object Detection

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  • [R-CNN(2013)] [paper]
  • [Overfeat(2014)] [paper]
  • [Multibox(2014)] [paper]
  • [SPPNet(2014)] [paper]
  • [MR-CNN(2015)] [paper]
  • [Deepbox(2015)] [paper]
  • [AttentionNet(2015)] [paper]
  • [Fast R-CNN(2015)] [paper]
  • [DeepProposal(2015)] [paper]
  • [RPN(2015)] [[paper]]
  • [Faster R-CNN(2015)] [paper]
  • [YOLOv1(2016)] [paper]
  • [GCNN(2016)] [paper]
  • [AZNet(2016)] [paper]
  • [ION(2016)] [paper]
  • [HyperNet(2016)] [paper]
  • [OHEM(2016)] [paper]
  • [CRAFT(2016)] [paper]
  • [MultipathNet(2016)] [paper]
  • [SSD(2016)] [paper]
  • [GBDNet(2016)] [paper]
  • [CPF(2016)] [[paper]]
  • [MS-CNN(2016)] [paper]
  • [R-FCN(2016)] [paper]
  • [PVANET(2016)] [paper]
  • [DeepIDNet(2016)] [paper]
  • [NoC(2016)] [paper]
  • [DSSD(2017)] [paper]
  • [TDM(2017)] [paper]
  • [Feature Pyramid Net(2017)] [paper]
  • [YOLOv2(2017)] [paper]
  • [RON(2017)] [paper]
  • [DCN(2017)] [[paper]]
  • [DeNet(2017)] [paper]
  • [CoupleNet(2017)] [paper]
  • [RetinaNet(2017)] [paper]
  • [Mask R-CNN(2017)] [paper]
  • [DSOD(2017)] [paper]
  • [SMN(2017)] [paper]
  • [YOLOv3(2018)] [paper]
  • [SIN(2018)] [paper]
  • [STDN(2018)] [paper]
  • [RefineDet(2018)] [paper]
  • [RFBNet(2018)] [paper]

  • [Light-Head R-CNN(2017)] [paper]
  • [Cascade R-CNN(2017)] [paper]
  • [YOLT(2018)] [paper]
  • [FSSD(2018)] [paper]
  • [ESSD] [paper]
  • [MDSSD(2018)] [paper]
  • [Pelee(2018)] [paper]
  • [Fire SSD(2018)] [paper]
  • [MegNet(2018)] [paper]
  • [DetNet(2018)] [paper]
  • [SSOD(2018)] [paper]
  • [CornerNet(2018)] [paper]
  • [3D Object Detection(2018)] [paper]
  • [ZSD(Zero-Shot Object Detection)(2018)] [paper]
  • [OSD(One-Shot object Detection)(2018)] [paper]
  • [Weakly Supervised Object Detection(2018)] [paper]
  • [Softer-NMS (2018)] [paper]
  • [VideoCapsuleNet(2018)] [paper]
  • [YOLO3D(2018)] [paper]

Semantic Segmentation

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References



cnn

1542368857926

img72

cnn_4

cnn_5

cnn_6


cnn_11

cnn21 cnn22 cnn23


obj_det


Maintainer

Gopala KR / @gopala-kr