132 lines
6.5 KiB
ReStructuredText
132 lines
6.5 KiB
ReStructuredText
.. {#openvino_docs_OV_Glossary}
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Glossary
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========
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.. meta::
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:description: Check the list of acronyms, abbreviations and terms used in
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Intel® Distribution of OpenVINO™ toolkit.
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Acronyms and Abbreviations
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#################################################
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================== ===========================================================================
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Abbreviation Description
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================== ===========================================================================
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API Application Programming Interface
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AVX Advanced Vector Extensions
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clDNN Compute Library for Deep Neural Networks
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CLI Command Line Interface
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CNN Convolutional Neural Network
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CPU Central Processing Unit
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CV Computer Vision
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DL Deep Learning
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DLL Dynamic Link Library
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DNN Deep Neural Networks
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ELU Exponential Linear rectification Unit
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FCN Fully Convolutional Network
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FP Floating Point
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GCC GNU Compiler Collection
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GPU Graphics Processing Unit
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HD High Definition
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IR Intermediate Representation
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JIT Just In Time
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JTAG Joint Test Action Group
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LPR License-Plate Recognition
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LRN Local Response Normalization
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mAP Mean Average Precision
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Intel® OneDNN Intel® OneAPI Deep Neural Network Library
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`mo` Command-line tool for model conversion, CLI for ``tools.mo.convert_model`` (legacy)
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MVN Mean Variance Normalization
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NCDHW Number of images, Channels, Depth, Height, Width
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NCHW Number of images, Channels, Height, Width
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NHWC Number of images, Height, Width, Channels
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NMS Non-Maximum Suppression
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NN Neural Network
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NST Neural Style Transfer
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OD Object Detection
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OS Operating System
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`ovc` OpenVINO Model Converter, command line tool for model conversion
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PCI Peripheral Component Interconnect
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PReLU Parametric Rectified Linear Unit
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PSROI Position Sensitive Region Of Interest
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RCNN, R-CNN Region-based Convolutional Neural Network
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ReLU Rectified Linear Unit
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ROI Region Of Interest
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SDK Software Development Kit
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SSD Single Shot multibox Detector
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SSE Streaming SIMD Extensions
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USB Universal Serial Bus
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VGG Visual Geometry Group
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VOC Visual Object Classes
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WINAPI Windows Application Programming Interface
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================== ===========================================================================
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Terms
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Glossary of terms used in OpenVINO™
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| *Batch*
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| 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.
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| *Device Affinity*
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| A preferred hardware device to run inference (CPU, GPU, NPU, etc.).
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| *Extensibility mechanism, Custom layers*
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| 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.
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| *layer / operation*
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| In OpenVINO, both terms are treated synonymously. To avoid confusion, "layer" is being pushed out and "operation" is the currently accepted term.
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| *Model conversion API*
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| 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.
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| *OpenVINO™ Core*
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| OpenVINO™ Core is a software component that manages inference on certain Intel(R) hardware devices: CPU, GPU, NPU, etc.
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| *OpenVINO™ API*
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| 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.
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| *OpenVINO™ Runtime*
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| A C++ library with a set of classes that you can use in your application to infer input tensors and get the results.
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| *ov::Model*
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| 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.
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| *ov::CompiledModel*
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| An instance of the compiled model which allows the OpenVINO™ Runtime to request (several) infer requests and perform inference synchronously or asynchronously.
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| *ov::InferRequest*
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| 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.
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| *ov::ProfilingInfo*
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| Represents basic inference profiling information per operation.
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| *ov::Layout*
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| 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).
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| *ov::element::Type*
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| Represents data element type. For example, f32 is 32-bit floating point, f16 is 16-bit floating point.
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| *plugin / Inference Device / Inference Mode*
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| OpenVINO makes hardware available for inference based on several core components.
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They used to be called "plugins" in earlier versions of documentation and you may
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still find this term in some articles. Because of their role in the software,
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they are now referred to as Devices and Modes ("virtual" devices). For a detailed
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description of the concept, refer to
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:doc:`Inference Devices and Modes <openvino_docs_Runtime_Inference_Modes_Overview>`.
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| *Tensor*
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| A memory container used for storing inputs and outputs of the model, as well as
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weights and biases of the operations.
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See Also
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#################################################
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* :doc:`Available Operations Sets <openvino_docs_ops_opset>`
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