Package | Description |
---|---|
org.bytedeco.opencv.global | |
org.bytedeco.opencv.opencv_dnn | |
org.bytedeco.opencv.opencv_mcc |
Class and Description |
---|
_Range |
Image2BlobParams
\brief Processing params of image to blob.
|
Net
\brief This class allows to create and manipulate comprehensive artificial neural networks.
|
Class and Description |
---|
AbsLayer |
AccumLayer |
AcoshLayer |
AcosLayer |
ActivationLayer |
ActivationLayerInt8 |
ArgLayer
\brief ArgMax/ArgMin layer
\note returns indices as floats, which means the supported range is [-2^24; 2^24]
|
AsinhLayer |
AsinLayer |
AtanhLayer |
AtanLayer |
AttentionLayer |
BackendNode
\brief Derivatives of this class encapsulates functions of certain backends.
|
BackendWrapper
\brief Derivatives of this class wraps cv::Mat for different backends and targets.
|
BaseConvolutionLayer |
BatchNormLayer |
BatchNormLayerInt8 |
BNLLLayer |
CeilLayer |
CeluLayer |
ClassificationModel
\brief This class represents high-level API for classification models.
|
CompareLayer |
ConcatLayer |
ConstLayer
Constant layer produces the same data blob at an every forward pass.
|
ConvolutionLayer |
ConvolutionLayerInt8 |
CorrelationLayer |
CoshLayer |
CosLayer |
CumSumLayer |
DataAugmentationLayer |
DequantizeLayer |
DetectionModel
\brief This class represents high-level API for object detection networks.
|
DetectionOutputLayer
\brief Detection output layer.
|
Dict
\brief This class implements name-value dictionary, values are instances of DictValue.
|
DictValue
\addtogroup dnn
\{
|
EinsumLayer
\brief This function performs array summation based
on the Einstein summation convention.
|
EltwiseLayer
\brief Element wise operation on inputs
|
EltwiseLayerInt8 |
ELULayer |
ErfLayer |
ExpandLayer |
ExpLayer |
FlattenLayer |
FloorLayer |
FlowWarpLayer |
GatherElementsLayer
\brief 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
Gather, on the other hand, takes a data tensor of rank r >= 1, and indices tensor of rank q, and works such that:
it gathers the enteries along axis dimension of the input data indexed by indices and concatenates them in an output tensor of rank q + (r - 1)
e.g.
|
GatherLayer
\brief Gather layer
|
GeluApproximationLayer |
GeluLayer |
GemmLayer |
GRULayer
\brief GRU recurrent one-layer
Accepts input sequence and computes the final hidden state for each element in the batch.
|
HardSigmoidLayer |
HardSwishLayer |
Image2BlobParams
\brief Processing params of image to blob.
|
InnerProductLayer
InnerProduct , MatMul and Gemm operations are all implemented by Fully Connected Layer. |
InnerProductLayerInt8 |
InstanceNormLayer |
IntFloatPair |
Layer
\brief This interface class allows to build new Layers - are building blocks of networks.
|
LayerFactory.Constructor
Each Layer class must provide this function to the factory
|
LayerNormLayer |
LayerParams
\brief This class provides all data needed to initialize layer.
|
LogLayer |
LRNLayer |
LSTMLayer
LSTM recurrent layer
|
MatMulLayer |
MatPointerVector |
MatPointerVector.Iterator |
MatShapeVector |
MatShapeVector.Iterator |
MatShapeVectorVector |
MatShapeVectorVector.Iterator |
MaxUnpoolLayer |
MishLayer |
Model
\brief This class is presented high-level API for neural networks.
|
Model.Impl |
MVNLayer |
NaryEltwiseLayer |
Net
\brief This class allows to create and manipulate comprehensive artificial neural networks.
|
Net.Impl |
NormalizeBBoxLayer
\brief
L_p - normalization layer. |
NotLayer |
PaddingLayer
\brief Adds extra values for specific axes.
|
PermuteLayer |
PoolingLayer |
PoolingLayerInt8 |
PowerLayer |
PriorBoxLayer |
ProposalLayer |
QuantizeLayer |
RangeVectorVector |
ReciprocalLayer |
ReduceLayer |
RegionLayer |
ReLU6Layer |
ReLULayer |
ReorgLayer |
RequantizeLayer |
ReshapeLayer |
ResizeLayer
\brief Resize input 4-dimensional blob by nearest neighbor or bilinear strategy.
|
RNNLayer
\brief Classical recurrent layer
|
RoundLayer |
ScaleLayer |
ScaleLayerInt8 |
ScatterLayer |
ScatterNDLayer |
SeluLayer |
ShiftLayerInt8 |
ShrinkLayer |
ShuffleChannelLayer
Permute channels of 4-dimensional input blob.
|
SigmoidLayer |
SignLayer |
SinhLayer |
SinLayer |
SliceLayer
Slice layer has several modes:
1.
|
SoftmaxLayer |
SoftmaxLayerInt8 |
SoftplusLayer |
SoftsignLayer |
SplitLayer |
SqrtLayer |
SwishLayer |
TanHLayer |
TanLayer |
TextDetectionModel
\brief Base class for text detection networks
|
TextDetectionModel_DB
\brief This class represents high-level API for text detection DL networks compatible with DB model.
|
TextDetectionModel_EAST
\brief This class represents high-level API for text detection DL networks compatible with EAST model.
|
TextRecognitionModel
\brief This class represents high-level API for text recognition networks.
|
ThresholdedReluLayer |
TileLayer |
Class and Description |
---|
Net
\brief This class allows to create and manipulate comprehensive artificial neural networks.
|
Copyright © 2024. All rights reserved.