openvino/docs/glossary.md

Glossary

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

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® 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 #################################################

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, GNA, 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, GNA, 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::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 [Inference Modes](@ref openvino_docs_Runtime_Inference_Modes_Overview) and [Inference Devices](@ref openvino_docs_OV_UG_Working_with_devices).

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

See Also #################################################

• :doc:Available Operations Sets <openvino_docs_ops_opset>
• :doc:Terminology <openvino_docs_OV_UG_supported_plugins_Supported_Devices>

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