| 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.
|
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