101ef50f4e
* add doxygen doc build configurations * fix layouts * change the dl-streamer link Co-authored-by: Nikolay Tyukaev <ntyukaev_lo@jenkins.inn.intel.com>
67 lines
5.1 KiB
Markdown
67 lines
5.1 KiB
Markdown
# OpenVINO™ Toolkit Documentation {#index}
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## Introduction to OpenVINO™ Toolkit
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OpenVINO™ toolkit quickly deploys applications and solutions that emulate human vision. Based on Convolutional Neural Networks (CNNs), the toolkit extends computer vision (CV) workloads across Intel® hardware, maximizing performance. The OpenVINO™ toolkit includes the Deep Learning Deployment Toolkit (DLDT).
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OpenVINO™ toolkit:
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- Enables CNN-based deep learning inference on the edge
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- Supports heterogeneous execution across an Intel® CPU, Intel® Integrated Graphics, Intel® FPGA, Intel® Neural Compute Stick 2 and Intel® Vision Accelerator Design with Intel® Movidius™ VPUs
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- Speeds time-to-market via an easy-to-use library of computer vision functions and pre-optimized kernels
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- Includes optimized calls for computer vision standards, including OpenCV\* and OpenCL™
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## Toolkit Components
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OpenVINO™ toolkit includes the following components:
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- Deep Learning Deployment Toolkit (DLDT)
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- [Deep Learning Model Optimizer](MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md) - A cross-platform command-line tool for importing models and
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preparing them for optimal execution with the Inference Engine. The Model Optimizer imports, converts, and optimizes models, which were trained in popular frameworks, such as Caffe*,
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TensorFlow*, MXNet*, Kaldi*, and ONNX*.
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- [Deep Learning Inference Engine](IE_DG/inference_engine_intro.md) - A unified API to allow high performance inference on many hardware types
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including the following:
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- Intel® CPU
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- Intel® Integrated Graphics
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- Intel® Neural Compute Stick 2
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- Intel® Vision Accelerator Design with Intel® Movidius™ vision processing unit (VPU)
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- [Samples](IE_DG/Samples_Overview.md) - A set of simple console applications demonstrating how to use the Inference Engine in your applications
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- [Tools](IE_DG/Tools_Overview.md) - A set of simple console tools to work with your models
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- [Open Model Zoo](@ref omz_models_intel_index)
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- [Demos](@ref omz_demos_README) - Console applications that demonstrate how you can use the Inference Engine in your applications to solve specific use cases
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- [Tools](IE_DG/Tools_Overview.md) - Additional tools to download models and check accuracy
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- [Documentation for Pretrained Models](@ref omz_models_intel_index) - Documentation for pretrained models is available in the [Open Model Zoo repository](https://github.com/opencv/open_model_zoo)
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- [Post-Training Optimization tool](@ref pot_README) - A tool to calibrate a model and then execute it in the INT8 precision
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- [Deep Learning Workbench](@ref workbench_docs_Workbench_DG_Introduction) - A web-based graphical environment that allows you to easily use various sophisticated OpenVINO™ toolkit components
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- Deep Learning Streamer (DL Streamer) – Streaming analytics framework, based on GStreamer, for constructing graphs of media analytics components. DL Streamer can be installed by the Intel® Distribution of OpenVINO™ toolkit installer. Its open source version is available on [GitHub](https://github.com/opencv/gst-video-analytics). For the DL Streamer documentation, see:
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- [DL Streamer Samples](IE_DG/Tools_Overview.md)
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- [API Reference](https://openvinotoolkit.github.io/dlstreamer_gst/)
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- [Elements](https://github.com/opencv/gst-video-analytics/wiki/Elements)
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- [Tutorial](https://github.com/opencv/gst-video-analytics/wiki/DL%20Streamer%20Tutorial)
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- [OpenCV](https://docs.opencv.org/master/) - OpenCV* community version compiled for Intel® hardware
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- Drivers and runtimes for OpenCL™ version 2.1
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- [Intel® Media SDK](https://software.intel.com/en-us/media-sdk)
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## Documentation Set Contents
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OpenVINO™ toolkit documentation set includes the following documents:
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- [Install the Intel® Distribution of OpenVINO™ Toolkit for Linux*](install_guides/installing-openvino-linux.md)
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- [Install the Intel® Distribution of OpenVINO™ Toolkit for Linux with FPGA Support](install_guides/installing-openvino-linux-fpga.md)
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- [Install the Intel® Distribution of OpenVINO™ Toolkit for Windows*](install_guides/installing-openvino-windows.md)
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- [Install the Intel® Distribution of OpenVINO™ Toolkit for macOS*](install_guides/installing-openvino-macos.md)
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- [Install the Intel® Distribution of OpenVINO™ Toolkit for Raspbian*](install_guides/installing-openvino-raspbian.md)
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- [Install OpenVINO™ Deep Learning Workbench](@ref workbench_docs_Workbench_DG_Install_Workbench)
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- [Introduction to Deep Learning Deployment Toolkit](IE_DG/Introduction.md)
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- [Model Optimizer Developer Guide](MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md)
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- [Inference Engine Developer Guide](IE_DG/Deep_Learning_Inference_Engine_DevGuide.md)
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- [Post-Training Optimization Tool](@ref pot_README)
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- [Inference Engine Samples](IE_DG/Samples_Overview.md)
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- [Demo Applications](@ref omz_demos_README)
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- [Tools](IE_DG/Tools_Overview.md)
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- [Pretrained Models](@ref omz_models_intel_index)
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- [Known Issues](IE_DG/Known_Issues_Limitations.md)
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- [Legal Information](@ref omz_demos_README)
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> **Typical Next Step:** [Introduction to Deep Learning Deployment Toolkit](IE_DG/Introduction.md)
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