| ►NInferenceEngine | Inference Engine API |
| ►NBuilder | Neural network builder API |
| CValidatorRegisterBase | This class registers layer validators |
| CValidatorsHolder | This structure implements a holder for validators |
| CBatchNormalizationLayer | This class represents a Batch Normalization Layer |
| CBinaryConvolutionLayer | This class represents a standard binary convolution layer |
| CBlob | This class represents a universal container in the Inference Engine |
| CBlockingDesc | This class describes blocking layouts |
| CBroadcastLayer | This class represents a standard Broadcast layer Broadcast modifies input tensor dimensions according parameters |
| CClampLayer | This class represents a Clamp activation layer Clamps all tensor elements into the range [min_value, max_value] |
| CCNNLayer | This is a base abstraction Layer - all DNN Layers inherit from this class |
| CCNNNetReader | This is a wrapper class used to build and parse a network from the given IR. All the methods here can throw exceptions |
| CCNNNetwork | This class contains all the information about the Neural Network and the related binary information |
| CCompoundBlob | This class represents a blob that contains other blobs |
| CConcatLayer | This class represents concatenation layer Takes as input several data elements and merges them to one using the supplied axis |
| CConnection | This class is the main object to describe the Inference Engine connection |
| CConvolutionLayer | This class represents a standard 3D Convolution Layer |
| CCore | This class represents Inference Engine Core entity. It can throw exceptions safely for the application, where it is properly handled |
| CCropLayer | This class represents a standard crop layer |
| CData | This class represents the main Data representation node |
| CDataConfig | This structure describes data configuration |
| CDeconvolutionLayer | This class represents a standard deconvolution layer |
| CDeformableConvolutionLayer | This class represents a standard deformable convolution layer |
| CDepthToSpaceLayer | This class represents a standard Depth To Space layer Depth To Space picks from input tensor according parameters |
| CEltwiseLayer | This class represents an element wise operation layer |
| CExecutableNetwork | Wrapper over IExecutableNetwork |
| CExtension | This class is a C++ helper to work with objects created using extensions |
| CFillLayer | This class represents a standard Fill layer RFill modifies input tensor according parameters |
| CFullyConnectedLayer | This class represents a fully connected layer |
| CGatherLayer | This class represents a standard Gather layer Gather slices from Dictionary according to Indexes |
| CGemmLayer | This class represents a general matrix multiplication operation layer Formula is: dst := alpha*src1*src2 + beta*src3 |
| CGeneralError | This class represents StatusCode::GENERIC_ERROR exception |
| CGRNLayer | This class represents standard GRN Layer |
| CGRUCell | GRU Cell layer |
| CIAllocator | Allocator concept to be used for memory management and is used as part of the Blob |
| CICNNNetReader | This class is the main interface to build and parse a network from a given IR |
| CICNNNetwork | This is the main interface to describe the NN topology |
| CICNNNetworkStats | This is the interface to describe the NN topology scoring statistics |
| CIErrorListener | This class represents a custom error listener. Plugin consumers can provide it via InferenceEngine::SetLogCallback |
| CIExecutableNetwork | This is an interface of an executable network |
| CIExtension | This class is the main extension interface |
| CIInferRequest | This is an interface of asynchronous infer request |
| CILayer | This class is the main interface to describe the Inference Engine layer. All methods here are constant and do not throw exceptions |
| CILayerExecImpl | This class provides interface for the implementation with the custom execution code |
| CILayerImpl | This class provides interface for extension implementations |
| CILayerImplFactory | This class provides interface for extension factories |
| CIMemoryState | Manages data for reset operations |
| CINetwork | This class is the main interface to describe the Inference Engine network |
| CINetwotkIterator | |
| CInferenceEngine | This class is a C++ API wrapper for IInferencePlugin. It can throw exceptions safely for the application, where it is properly handled |
| CInferenceEngineProfileInfo | Represents basic inference profiling information per layer. If the layer is executed using tiling, the sum time per each tile is indicated as the total execution time. Due to parallel execution, the total execution time for all layers might be greater than the total inference time |
| CInferNotStarted | This class represents StatusCode::INFER_NOT_STARTED exception |
| CInferRequest | This class is a wrapper of IInferRequest to provide setters/getters of input/output which operates with BlobMaps. It can throw exceptions safely for the application, where it is properly handled |
| CInputInfo | This class contains information about each input of the network |
| CIShapeInferExtension | This class is the reader extension interface to provide implementation for shape propagation |
| CIShapeInferImpl | This class provides interface for the implementation with the custom execution code |
| CLayerConfig | This structure describes Layer configuration |
| CLayerParams | This is an internal common Layer parameter parsing arguments |
| CLockedMemory | This class represents locked memory for read/write memory |
| CLockedMemory< const T > | This class is for read-only segments |
| CLockedMemory< void > | This class is for <void*> data and allows casting to any pointers |
| CLSTMCell | LSTM Cell layer |
| CMathLayer | This class represents a standard Math layers Math modifies input tensor dimensions according parameters |
| CMemoryBlob | This class implements a container object that represents a tensor in memory (host and remote/accelerated) |
| CMemoryState | C++ exception based error reporting wrapper of API class IMemoryState |
| CMVNLayer | This class represents standard MVN Layer |
| CNetworkNodeStats | This class implements a container which stores statistics for a layer |
| CNetworkNotLoaded | This class represents StatusCode::NETWORK_NOT_LOADED exception |
| CNonMaxSuppressionLayer | This class represents a standard NonMaxSuppression layer |
| CNormLayer | This class represents a Linear Response Normalization (LRN) Layer |
| CNotAllocated | This class represents StatusCode::NOT_ALLOCATED exception |
| CNotFound | This class represents StatusCode::NOT_FOUND exception |
| CNotImplemented | This class represents StatusCode::NOT_IMPLEMENTED exception |
| CNV12Blob | Represents a blob that contains two planes (Y and UV) in NV12 color format |
| COneHotLayer | This class represents a OneHot layer Converts input into OneHot representation |
| COutOfBounds | This class represents StatusCode::OUT_OF_BOUNDS exception |
| CPadLayer | This class represents a standard Pad layer Adds paddings to input tensor |
| CParameter | This class represents an object to work with different parameters |
| CParameterMismatch | This class represents StatusCode::PARAMETER_MISMATCH exception |
| CPoolingLayer | This class represents a standard pooling layer |
| CPowerLayer | This class represents a standard Power Layer Formula is: output = (offset + scale * input) ^ power |
| CPrecision | This class holds precision value and provides precision related operations |
| CPrecisionTrait | Particular precision traits |
| CPReLULayer | This class represents a Layer which performs Scale and Shift |
| CPreProcessChannel | This structure stores info about pre-processing of network inputs (scale, mean image, ...) |
| CPreProcessInfo | This class stores pre-process information for the input |
| CPrimitiveInfo | Structure with information about Primitive |
| CPropertyVector | |
| CQuantizeLayer | This class represents a quantization operation layer Element-wise linear quantization of floating point input values into a descrete set of floating point values |
| CQueryNetworkResult | Responce structure encapsulating information about supported layer |
| CRangeLayer | This class represents a standard RangeLayer layer RangeLayer modifies input tensor dimensions according parameters |
| CReduceLayer | This class represents a standard Reduce layers Reduce modifies input tensor according parameters |
| CReLU6Layer | This class represents a ReLU6 activation layer Clamps all tensor elements into the range [0, 6.0] |
| CReLULayer | This class represents a Rectified Linear activation layer |
| CRequestBusy | This class represents StatusCode::REQUEST_BUSY exception |
| CReshapeLayer | This class represents a standard reshape layer |
| CResponseDesc | Represents detailed information for an error |
| CResultNotReady | This class represents StatusCode::RESULT_NOT_READY exception |
| CReverseSequenceLayer | This class represents a standard Reverse Sequence layer Reverse Sequence modifies input tensor according parameters |
| CRNNCell | RNN Cell layer |
| CRNNCellBase | Base class for recurrent cell layers |
| CRNNSequenceLayer | Sequence of recurrent cells |
| CROI | This structure describes ROI data |
| CScaleShiftLayer | This class represents a Layer which performs Scale and Shift |
| CScatterLayer | This class represents a standard Scatter layer |
| CSelectLayer | This class represents a SelectLayer layer SelectLayer layer takes elements from the second (“then”) or the third (“else”) input based on condition mask (“cond”) provided in the first input. The “cond” tensor is broadcasted to “then” and “else” tensors. The output tensor shape is equal to broadcasted shape of “cond”, “then” and “else” |
| CShapeInferExtension | This class is a C++ helper to work with objects created using extensions |
| CShuffleChannelsLayer | This class represents a standard Shuffle Channels layer Shuffle Channels picks from input tensor according parameters |
| CSoftMaxLayer | This class represents standard softmax Layer |
| CSpaceToDepthLayer | This class represents a standard Space To Depth layer Depth To Space picks from input tensor according parameters |
| CSparseFillEmptyRowsLayer | This class represents SparseFillEmptyRows layer SparseFillEmptyRows fills empty rows in a sparse tensor |
| CSparseSegmentReduceLayer | This class represents SparseSegmentMean(SqrtN, Sum) layers SparseSegmentMean(SqrtN, Sum) layer reduces data along sparse segments of a tensor |
| CSplitLayer | This class represents a layer that evenly splits the input into the supplied outputs |
| CStridedSliceLayer | This class represents a standard Strided Slice layer Strided Slice picks from input tensor according parameters |
| CTBlob | Represents real host memory allocated for a Tensor/Blob per C type |
| CTensorDesc | This class defines Tensor description |
| CTensorInfo | This structure describes tensor information |
| ►CTensorIterator | This class represents TensorIterator layer |
| CBody | |
| CPortMap | |
| CTileLayer | This class represents a standard Tile Layer |
| CTopKLayer | This class represents a standard TopK layer TopK picks top K values from input tensor according parameters |
| CUnexpected | This class represents StatusCode::UNEXPECTED exception |
| CUniqueLayer | This class represents Unique layer. The Unique operation searches for unique elements in 1-D input |
| CUserValue | The method holds the user values to enable binding of data per graph node |
| CVersion | Represents version information that describes plugins and the inference engine runtime library |
| CWeightableLayer | This class represents a layer with Weights and/or Biases (e.g. Convolution/Fully Connected, etc.) |
| CNetworkNotRead | This class represents StatusCode::NETWORK_NOT_READ exception |
| CNullStream |