ffd63f9758
* Updated glossary * Removed references to OpenVX * Moved migration_ov_2_0 to OpenVINO User guide * Replaced IE with OV runtime
79 lines
7.8 KiB
Markdown
79 lines
7.8 KiB
Markdown
# Glossary {#openvino_docs_OV_Glossary}
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## Acronyms and Abbreviations
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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(R) MKL-DNN | Intel(R) Math Kernel Library Deep Neural Networks |
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| MO | Model Optimizer |
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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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| 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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## Terms
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Glossary of terms used in the OpenVINO™
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| Term | Description |
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| :--- |:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
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| 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 |
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| Tensor | Memory container used for storing inputs, outputs of the model, weights and biases of the operations |
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| Device (Affinitity) | A preferred Intel(R) hardware device to run the inference (CPU, GPU, GNA, etc.) |
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| 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 |
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| <code>ov::Model</code> | A class of the Model that OpenVINO™ Runtime reads from IR or converts from ONNX, PaddlePaddle formats. Consists of model structure, weights and biases |
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| <code>ov::CompiledModel</code> | 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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| <code>ov::InferRequest</code> | 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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| <code>ov::ProfilingInfo</code> | Represents basic inference profiling information per operation |
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| 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 |
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| 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 |
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| OpenVINO™ <code>Core</code> | OpenVINO™ Core is a software component that manages inference on certain Intel(R) hardware devices: CPU, GPU, MYRIAD, GNA, etc. |
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| <code>ov::Layout</code> | 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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| <code>ov::element::Type</code> | Represents data element type. For example, f32 is 32-bit floating point, f16 is 16-bit floating point. |
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## See Also
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* [Available Operations Sets](ops/opset.md)
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* [Terminology](OV_Runtime_UG/supported_plugins/Supported_Devices.md)
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