:orphan:

Glossary
========


.. meta::
   :description: Check the list of acronyms, abbreviations and terms used in
                 Intel® Distribution of OpenVINO™ toolkit.


Acronyms and Abbreviations
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==================  ===========================================================================
 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® OneDNN       Intel® OneAPI Deep Neural Network Library
 `mo`                Command-line tool for model conversion, CLI for ``tools.mo.convert_model`` (legacy)
 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
 `ovc`               OpenVINO Model Converter, command line tool for model conversion
 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
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Glossary of terms used in OpenVINO™


| *Batch*
|   Number of images to analyze during one call of infer. Maximum batch size is a property of the model set before its compilation. In NHWC, NCHW, and NCDHW image data layout representations, the 'N' refers to the number of images in the batch.

| *Device Affinity*
|   A preferred hardware device to run inference (CPU, GPU, NPU, etc.).

| *Extensibility mechanism, Custom layers*
|   The mechanism that provides you with capabilities to extend the OpenVINO™ Runtime and model conversion API so that they can work with models containing operations that are not yet supported.

| *layer / operation*
|   In OpenVINO, both terms are treated synonymously. To avoid confusion, "layer" is being pushed out and "operation" is the currently accepted term.

| *Model conversion API*
|   The Conversion API is used to import and convert models trained in popular frameworks to a format usable by other OpenVINO components. Model conversion API is represented by a Python ``openvino.convert_model()`` method  and ``ovc`` command-line tool.

| *OpenVINO™ Core*
|   OpenVINO™ Core is a software component that manages inference on certain Intel(R) hardware devices: CPU, GPU, NPU, etc.

| *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, TensorFlow, TensorFlow Lite file formats, set input and output formats and execute the model on various devices.

| *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.

| *ov::Model*
|   A class of the Model that OpenVINO™ Runtime reads from IR or converts from ONNX, PaddlePaddle, TensorFlow, TensorFlow Lite 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.

| *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_Runtime_UG/layout_overview.md).

| *ov::element::Type*
|   Represents data element type. For example, f32 is 32-bit floating point, f16 is 16-bit floating point.

| *plugin / Inference Device / Inference Mode*
|   OpenVINO makes hardware available for inference based on several core components.
    They used to be called "plugins" in earlier versions of documentation and you may
    still find this term in some articles. Because of their role in the software,
    they are now referred to as Devices and Modes ("virtual" devices). For a detailed
    description of the concept, refer to
    :doc:`Inference Devices and Modes <../../openvino-workflow/running-inference/inference-devices-and-modes>`.

| *Tensor*
|   A memory container used for storing inputs and outputs of the model, as well as
    weights and biases of the operations.


See Also
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* :doc:`Available Operations Sets <../../documentation/openvino-ir-format/operation-sets/available-opsets>`
