Classes
class	cv::dnn::AbsLayer

class	cv::dnn::ActivationLayer

class	cv::dnn::BaseConvolutionLayer

class	cv::dnn::BatchNormLayer

class	cv::dnn::BlankLayer

class	cv::dnn::BNLLLayer

class	cv::dnn::ChannelsPReLULayer

class	cv::dnn::ConcatLayer

class	cv::dnn::ConvolutionLayer

class	cv::dnn::CropAndResizeLayer

class	cv::dnn::CropLayer

class	cv::dnn::DeconvolutionLayer

class	cv::dnn::DetectionOutputLayer

class	cv::dnn::EltwiseLayer

class	cv::dnn::ELULayer

class	cv::dnn::FlattenLayer

class	cv::dnn::InnerProductLayer

class	cv::dnn::InterpLayer
	Bilinear resize layer from https://github.com/cdmh/deeplab-public. More...

class	cv::dnn::LRNLayer

class	cv::dnn::LSTMLayer
	LSTM recurrent layer. More...

class	cv::dnn::MaxUnpoolLayer

class	cv::dnn::MVNLayer

class	cv::dnn::NormalizeBBoxLayer
	\( L_p \) - normalization layer. More...

class	cv::dnn::PaddingLayer
	Adds extra values for specific axes. More...

class	cv::dnn::PermuteLayer

class	cv::dnn::PoolingLayer

class	cv::dnn::PowerLayer

class	cv::dnn::PriorBoxLayer

class	cv::dnn::ProposalLayer

class	cv::dnn::RegionLayer

class	cv::dnn::ReLU6Layer

class	cv::dnn::ReLULayer

class	cv::dnn::ReorgLayer

class	cv::dnn::ReshapeLayer

class	cv::dnn::ResizeLayer
	Resize input 4-dimensional blob by nearest neighbor or bilinear strategy. More...

class	cv::dnn::RNNLayer
	Classical recurrent layer. More...

class	cv::dnn::ScaleLayer

class	cv::dnn::ShiftLayer

class	cv::dnn::ShuffleChannelLayer

class	cv::dnn::SigmoidLayer

class	cv::dnn::SliceLayer

class	cv::dnn::SoftmaxLayer

class	cv::dnn::SplitLayer

class	cv::dnn::TanHLayer

Detailed Description

This subsection of dnn module contains information about built-in layers and their descriptions.

Classes listed here, in fact, provides C++ API for creating instances of built-in layers. In addition to this way of layers instantiation, there is a more common factory API (see Utilities for New Layers Registration), it allows to create layers dynamically (by name) and register new ones. You can use both API, but factory API is less convenient for native C++ programming and basically designed for use inside importers (see readNetFromCaffe(), readNetFromTorch(), readNetFromTensorflow()).

Built-in layers partially reproduce functionality of corresponding Caffe and Torch7 layers. In particular, the following layers and Caffe importer were tested to reproduce Caffe functionality:

Convolution
Deconvolution
Pooling
InnerProduct
TanH, ReLU, Sigmoid, BNLL, Power, AbsVal
Softmax
Reshape, Flatten, Slice, Split
LRN
MVN
Dropout (since it does nothing on forward pass -))