Summary
OpenVINO is an open-source software toolkit for optimizing and deploying deep learning models. It enables programmers to develop scalable and efficient AI solutions with relatively few lines of code. It supports several popular model formats[2] and categories, such as large language models, computer vision, and generative AI.
 | |
| Developer(s) | Intel Corporation |
|---|---|
| Initial release | May 16, 2018 |
| Stable release | 2024.0
/ March 2024.[1] |
| Repository | github |
| Written in | C++ |
| Operating system | Cross-platform |
| License | Apache License 2.0 |
| Website | docs |
| As of | March 2024 |
Actively developed by Intel, it prioritizes high-performance inference on Intel hardware but also supports ARM/ARM64 processors[2] and encourages contributors to add new devices to the portfolio.
Based in C++, it offers the following APIs: C/C++, Python, and Node.js (an early preview).
OpenVINO is cross-platform and free for use under Apache License 2.0.[3]
Overview edit
The high level pipeline of OpenVINO consists of two parts: generate IR (Intermediate Representation) files via Model Optimizer using your trained model or public one and execute inference on Inference Engine on specified devices.
OpenVINO has different sample types: classification, object detection, style transfer, speech recognition, etc. It is possible to try inference on public models. There are a variety of models for tasks, such as:
- classification
- segmentation
- object detection
- face recognition
- human pose estimation
- monocular depth estimation
- image inpainting
- style transfer
- action recognition
- colorization
OpenVINO model format edit
OpenVINO IR[4] is the default format used to run inference. It is saved as a set of two files, *.bin and *.xml, containing weights and topology, respectively. It is obtained by converting a model from one of the supported frameworks, using the application's API or a dedicated converter.
Models of the supported formats may also be used for inference directly, without prior conversion to OpenVINO IR. Such an approach is more convenient but offers fewer optimization options and lower performance, since the conversion is performed automatically before inference.
The supported model formats are:[5]
- PyTorch
- TensorFlow
- TensorFlow Lite
- ONNX (including formats that may be serialized to ONNX)
- PaddlePaddle
OS support edit
OpenVINO runs on the following desktop operation systems: Windows, Linux and MacOS.[6]
See also edit
References edit
- ^ "Release Notes for Intel Distribution of OpenVINO toolkit 2024.0". March 2024.
- ^ a b "OpenVINO Compatibility and Support". OpenVINO Documentation. 24 January 2024.
- ^ "License". OpenVINO repository. 16 October 2018.
- ^ "OpenVINO IR". www.docs.openvino.ai. 2 February 2024.
- ^ "OpenVINO Model Preparation". OpenVINO Documentation. 24 January 2024.
- ^ "System Requirements". OpenVINO Documentation. February 2024.
- Agrawal, Vasu (2019). Ground Up Design of a Multi-modal Object Detection System (PDF) (MSc). Carnegie Mellon University Pittsburgh, PA. Archived (PDF) from the original on 26 January 2020.
- Driaba, Alexander; Gordeev, Aleksei; Klyachin, Vladimir (2019). "Recognition of Various Objects from a Certain Categorical Set in Real Time Using Deep Convolutional Neural Networks" (PDF). Institute of Mathematics and Informational Technologies Volgograd State University. Archived (PDF) from the original on 26 January 2020. Retrieved 26 January 2020.
{{cite journal}}: Cite journal requires|journal=(help) - Nanjappa, Ashwin (31 May 2019). Caffe2 Quick Start Guide: Modular and scalable deep learning made easy. Packt. pp. 91–98. ISBN 978-1789137750.