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models

README.md

PyTorch-Models

Pytorch implementation of cnn network

Classical network

  • AlexNet

  • VGG

  • ResNet

  • ResNext

  • InceptionV1

  • InceptionV2 and InceptionV3

  • InceptionV4 and Inception-ResNet

  • GoogleNet

  • EfficienNet

  • MNasNet

  • DPN

Attention network

  • SE Attention
  • External Attention
  • Self Attention
  • SK Attention
  • CBAM Attention
  • BAM Attention
  • ECA Attention
  • DANet Attention
  • Pyramid Split Attention(PSA)
  • EMSA Attention
  • A2Attention
  • Non-Local Attention
  • CoAtNet
  • CoordAttention
  • HaloAttention
  • MobileViTAttention
  • MUSEAttention
  • OutlookAttention
  • ParNetAttention
  • ParallelPolarizedSelfAttention
  • residual_attention
  • S2Attention
  • SpatialGroupEnhance Attention
  • ShuffleAttention
  • GFNet Attention
  • TripletAttention
  • UFOAttention
  • VIPAttention

Lightweight network

  • MobileNets:
  • MobileNetV2：
  • MobileNetV3：
  • ShuffleNet：
  • ShuffleNet V2:
  • SqueezeNet
  • Xception
  • MixNet
  • GhostNet

GAN

  • Auxiliary Classifier GAN
  • Adversarial Autoencoder
  • BEGAN
  • BicycleGAN
  • Boundary-Seeking GAN
  • Cluster GAN
  • Conditional GAN
  • Context-Conditional GAN
  • Context Encoder
  • Coupled GAN
  • CycleGAN
  • Deep Convolutional GAN
  • DiscoGAN
  • DRAGAN
  • DualGAN
  • Energy-Based GAN
  • Enhanced Super-Resolution GAN
  • Least Squares GAN
  • Enhanced Super-Resolution GAN
  • GAN
  • InfoGAN
  • Pix2Pix
  • Relativistic GAN
  • Semi-Supervised GAN
  • StarGAN
  • Wasserstein GAN
  • Wasserstein GAN GP
  • Wasserstein GAN DIV

ObjectDetection-network

  • SSD:
  • YOLO:
  • YOLOv2:
  • YOLOv3:
  • FCOS:
  • FPN:
  • RetinaNet
  • Objects as Points:
  • FSAF:
  • CenterNet
  • FoveaBox

Semantic Segmentation

  • FCN

  • Fast-SCNN

  • LEDNet:

  • LRNNet

  • FisheyeMODNet:

Instance Segmentation

  • PolarMask

FaceDetectorAndRecognition

  • FaceBoxes
  • LFFD
  • VarGFaceNet

HumanPoseEstimation

  • Stacked Hourglass Networks
  • Simple Baselines
  • LPN

Write at the end

At present, the work organized by this project is indeed not comprehensive enough. As the amount of reading increases, we will continue to improve this project. Welcome everyone star to support. If there are incorrect statements or incorrect code implementations in the article, you are welcome to point out~