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Glossary

Acronyms and Abbreviations

Abbreviation	Description
API	Application Programming Interface
AVX	Advanced Vector Extensions
clDNN	Compute Library for Deep Neural Networks
CLI	Command Line Interface
CNN	Convolutional Neural Network
CPU	Central Processing Unit
CV	Computer Vision
DL	Deep Learning
DLL	Dynamic Link Library
DNN	Deep Neural Networks
ELU	Exponential Linear rectification Unit
FCN	Fully Convolutional Network
FP	Floating Point
GCC	GNU Compiler Collection
GPU	Graphics Processing Unit
HD	High Definition
IR	Intermediate Representation
JIT	Just In Time
JTAG	Joint Test Action Group
LPR	License-Plate Recognition
LRN	Local Response Normalization
mAP	Mean Average Precision
Intel(R) OneDNN	Intel(R) OneAPI Deep Neural Network Library
MO	Model Optimizer
MVN	Mean Variance Normalization
NCDHW	Number of images, Channels, Depth, Height, Width
NCHW	Number of images, Channels, Height, Width
NHWC	Number of images, Height, Width, Channels
NMS	Non-Maximum Suppression
NN	Neural Network
NST	Neural Style Transfer
OD	Object Detection
OS	Operating System
PCI	Peripheral Component Interconnect
PReLU	Parametric Rectified Linear Unit
PSROI	Position Sensitive Region Of Interest
RCNN, R-CNN	Region-based Convolutional Neural Network
ReLU	Rectified Linear Unit
ROI	Region Of Interest
SDK	Software Development Kit
SSD	Single Shot multibox Detector
SSE	Streaming SIMD Extensions
USB	Universal Serial Bus
VGG	Visual Geometry Group
VOC	Visual Object Classes
WINAPI	Windows Application Programming Interface

Terms

Glossary of terms used in the OpenVINO™

Term	Description
Batch	Number of images to analyze during one call of infer. Maximum batch size is a property of the model and it is set before compiling of the model by the device. In NHWC, NCHW and NCDHW image data layout representation, the N refers to the number of images in the batch
Tensor	Memory container used for storing inputs, outputs of the model, weights and biases of the operations
Device (Affinitity)	A preferred Intel(R) hardware device to run the inference (CPU, GPU, GNA, etc.)
Extensibility mechanism, Custom layers	The mechanism that provides you with capabilities to extend the OpenVINO™ Runtime and Model Optimizer so that they can work with models containing operations that are not yet supported
ov::Model	A class of the Model that OpenVINO™ Runtime reads from IR or converts from ONNX, PaddlePaddle formats. Consists of model structure, weights and biases
ov::CompiledModel	An instance of the compiled model which allows the OpenVINO™ Runtime to request (several) infer requests and perform inference synchronously or asynchronously
ov::InferRequest	A class that represents the end point of inference on the model compiled by the device and represented by a compiled model. Inputs are set here, outputs should be requested from this interface as well
ov::ProfilingInfo	Represents basic inference profiling information per operation
OpenVINO™ Runtime	A C++ library with a set of classes that you can use in your application to infer input tensors and get the results
OpenVINO™ API	The basic default API for all supported devices, which allows you to load a model from Intermediate Representation or convert from ONNX, PaddlePaddle file formars, set input and output formats and execute the model on various devices
OpenVINO™ Core	OpenVINO™ Core is a software component that manages inference on certain Intel(R) hardware devices: CPU, GPU, MYRIAD, GNA, etc.
ov::Layout	Image data layout refers to the representation of images batch. Layout shows a sequence of 4D or 5D tensor data in memory. A typical NCHW format represents pixel in horizontal direction, rows by vertical dimension, planes by channel and images into batch. See also Layout API Overview
ov::element::Type	Represents data element type. For example, f32 is 32-bit floating point, f16 is 16-bit floating point.

See Also

• Available Operations Sets

• Terminology

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