algorithm_introduction

• Image Classification Task Introduction
  • Catalogue
  • 1. Dataset Introduction
  • 2. Image Classification Process
  • 3. Main Algorithms Introduction
• Metric Learning
  • Catalogue
  • 1.Introduction
  • 2.Applications
  • 3.Algorithms
• Knowledge Distillation
  • Content
  • 1. Introduction of model compression methods
  • 2. Application of knowledge distillation
  • 3. Overview of knowledge distillation methods
  • 4. Reference
• Algorithms of Model Pruning and Quantization
  • Catlogue
  • 1. PACT
  • 2. FPGM
• ImageNet Model zoo overview
  • Catalogue
  • 1. Model library overview diagram
  • 2. SSLD pretrained models
  • 3. PP-LCNet series ^[28]
  • 4. ResNet series ^[1]
  • 5. Mobile series ^{[3][4][5][6][23]}
  • 6. SEResNeXt and Res2Net series ^[7][8][9]
  • 7. DPN and DenseNet series ^[14][15]
  • 8. HRNet series ^[13]
  • 9. Inception series ^{[10][11][12][26]}
  • 10. EfficientNet and ResNeXt101_wsl series ^[16][17]
  • 11. ResNeSt and RegNet series ^[24][25]
  • 12. ViT and DeiT series ^[31][32]
  • 13. RepVGG series ^[36]
  • 14. MixNet series ^[29]
  • 15. ReXNet series ^[30]
  • 16. SwinTransformer series ^[27]
  • 17. LeViT series ^[33]
  • 18. Twins series ^[34]
  • 19. HarDNet series ^[37]
  • 20. DLA series ^[38]
  • 21. RedNet series ^[39]
  • 22. TNT series ^[35]
  • 23. CSWinTransformer series ^[40]
  • 24. PVTV2 series ^[41]
  • 25. MobileViT series ^[42]
  • 26. Other models
  • Reference
• Data Augmentation
  • Catalogue
  • 1. Introduction to data augmentation
  • 2. Common data augmentation methods
  • Reference