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 -))

Classes
class	cv::dnn::AbsLayer
class	cv::dnn::AccumLayer
class	cv::dnn::AcoshLayer
class	cv::dnn::AcosLayer
class	cv::dnn::ActivationLayer
class	cv::dnn::ActivationLayerInt8
class	cv::dnn::ArgLayer
	ArgMax/ArgMin layer. More...
class	cv::dnn::AsinhLayer
class	cv::dnn::AsinLayer
class	cv::dnn::AtanhLayer
class	cv::dnn::AtanLayer
class	cv::dnn::AttentionLayer
class	cv::dnn::BaseConvolutionLayer
class	cv::dnn::BatchNormLayer
class	cv::dnn::BatchNormLayerInt8
class	cv::dnn::BlankLayer
class	cv::dnn::BNLLLayer
class	cv::dnn::CeilLayer
class	cv::dnn::CeluLayer
class	cv::dnn::ChannelsPReLULayer
class	cv::dnn::CompareLayer
class	cv::dnn::ConcatLayer
class	cv::dnn::ConstLayer
class	cv::dnn::ConvolutionLayer
class	cv::dnn::ConvolutionLayerInt8
class	cv::dnn::CorrelationLayer
class	cv::dnn::CoshLayer
class	cv::dnn::CosLayer
class	cv::dnn::CropAndResizeLayer
class	cv::dnn::CropLayer
class	cv::dnn::CumSumLayer
class	cv::dnn::DataAugmentationLayer
class	cv::dnn::DeconvolutionLayer
class	cv::dnn::DequantizeLayer
class	cv::dnn::DetectionOutputLayer
	Detection output layer. More...
class	cv::dnn::EinsumLayer
	This function performs array summation based on the Einstein summation convention. The function allows for concise expressions of various mathematical operations using subscripts. More...
class	cv::dnn::EltwiseLayer
	Element wise operation on inputs. More...
class	cv::dnn::EltwiseLayerInt8
class	cv::dnn::ELULayer
class	cv::dnn::ErfLayer
class	cv::dnn::ExpandLayer
class	cv::dnn::ExpLayer
class	cv::dnn::FlattenLayer
class	cv::dnn::FloorLayer
class	cv::dnn::FlowWarpLayer
class	cv::dnn::GatherElementsLayer
	GatherElements layer GatherElements takes two inputs data and indices of the same rank r >= 1 and an optional attribute axis and works such that: output[i][j][k] = data[index[i][j][k]][j][k] if axis = 0 and r = 3 output[i][j][k] = data[i][index[i][j][k]][k] if axis = 1 and r = 3 output[i][j][k] = data[i][j][index[i][j][k]] if axis = 2 and r = 3. More...
class	cv::dnn::GatherLayer
	Gather layer. More...
class	cv::dnn::GeluApproximationLayer
class	cv::dnn::GeluLayer
class	cv::dnn::GemmLayer
class	cv::dnn::GroupNormLayer
class	cv::dnn::GRULayer
	GRU recurrent one-layer. More...
class	cv::dnn::HardSigmoidLayer
class	cv::dnn::HardSwishLayer
class	cv::dnn::InnerProductLayer
class	cv::dnn::InnerProductLayerInt8
class	cv::dnn::InstanceNormLayer
class	cv::dnn::InterpLayer
	Bilinear resize layer from https://github.com/cdmh/deeplab-public-ver2. More...
class	cv::dnn::LayerNormLayer
class	cv::dnn::LogLayer
class	cv::dnn::LRNLayer
class	cv::dnn::LSTMLayer
	LSTM recurrent layer. More...
class	cv::dnn::MatMulLayer
class	cv::dnn::MaxUnpoolLayer
class	cv::dnn::MishLayer
class	cv::dnn::MVNLayer
class	cv::dnn::NaryEltwiseLayer
class	cv::dnn::NormalizeBBoxLayer
	\( L_p \) - normalization layer. More...
class	cv::dnn::NotLayer
class	cv::dnn::PaddingLayer
	Adds extra values for specific axes. More...
class	cv::dnn::PermuteLayer
class	cv::dnn::PoolingLayer
class	cv::dnn::PoolingLayerInt8
class	cv::dnn::PowerLayer
class	cv::dnn::PriorBoxLayer
class	cv::dnn::ProposalLayer
class	cv::dnn::QuantizeLayer
class	cv::dnn::ReciprocalLayer
class	cv::dnn::ReduceLayer
class	cv::dnn::RegionLayer
class	cv::dnn::ReLU6Layer
class	cv::dnn::ReLULayer
class	cv::dnn::ReorgLayer
class	cv::dnn::RequantizeLayer
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::RoundLayer
class	cv::dnn::ScaleLayer
class	cv::dnn::ScaleLayerInt8
class	cv::dnn::ScatterLayer
class	cv::dnn::ScatterNDLayer
class	cv::dnn::SeluLayer
class	cv::dnn::ShiftLayer
class	cv::dnn::ShiftLayerInt8
class	cv::dnn::ShrinkLayer
class	cv::dnn::ShuffleChannelLayer
class	cv::dnn::SigmoidLayer
class	cv::dnn::SignLayer
class	cv::dnn::SinhLayer
class	cv::dnn::SinLayer
class	cv::dnn::SliceLayer
class	cv::dnn::SoftmaxLayer
class	cv::dnn::SoftmaxLayerInt8
class	cv::dnn::SoftplusLayer
class	cv::dnn::SoftsignLayer
class	cv::dnn::SplitLayer
class	cv::dnn::SqrtLayer
class	cv::dnn::SwishLayer
class	cv::dnn::TanHLayer
class	cv::dnn::TanLayer
class	cv::dnn::ThresholdedReluLayer
class	cv::dnn::TileLayer