[Target detection] Summary of target detection training methods

Note connection: Summary of training methods-continue to see the ability to update and improve. The
following are probably the things and frameworks to be updated. Now it is a pit. When I become a little stronger, I can slowly refine and refine the content of the specific things
. The frame diagram of common training methods for convolutional neural networks-from the screenshot in the original YOLOv4

1. Activation function
   1.1
   What is ReLU :
   Features:
   1.2
   What is leaky-ReLU :
   Features:
   1.3
   What is parametric-ReLU :
   Features:
   1.4 What
   is ReLU6 :
   Features:
   1.5 What
   is SELU :
   Features:
   1.6
   What is Swish :
   Features:
   1.7 Mish
   What:
   Features:
2. Bounding box regression loss
   2.1
   What is MSE :
   Features:
   2.2 What
   is IoU :
   Features:
   2.3 What
   is GIoU :
   Features:
   2.4 What
   is CIoU :
   Features:
   2.5 What
   is DIoU :
   Features:
3. Data enhancement
   3.1 What
   is CutOut :
   Features:
   3.2 What
   is MixUp :
   Features:
   3.3 What
   is CutMix :
   Features:
4. Regularization
   4.1 What
   is DropOut :
   Features:
   4.2 What
   is DropPath :
   Features:
   4.3
   What is Spatial DropOut :
   Features:
   4.4 What
   is DropBlock :
   Features:
5. Normalized
   5.1 Batch Normalization (BN)
   What is:
   Features:
   5.2 Cross-GPU Batch Normalization (CGBN or SyncBN)
   What is:
   Features:
   5.3 the Filter the Response Normalization (FRN)
   What is:
   Features:
   5.4 Cross-the Iteration Batch Normalization (CBN )
   What:
   features:
6. Remaining connected
   6.1 Residual connections
   What is:
   Features:
   6.2 Weighted
   What is:
   Features:
   6.3 residual Connections
   What is:
   Features:
   6.4 Multi-the INPUT Weighted residual
   Connections
   What is:
   Features:
   6.5 Cross Stage partial Connections (CSP)
   What is:
   Features: