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docs/HOWTO/Custom_Layers_Guide.md
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@ -21,11 +21,11 @@ The original format will be a supported framework such as TensorFlow, Caffe, or
## Custom Layer Overview
The [Model Optimizer](https://docs.openvinotoolkit.org/2019_R1.1/_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide.html) searches the list of known layers for each layer contained in the input model topology before building the model's internal representation, optimizing the model, and producing the Intermediate Representation files.
The [Model Optimizer](../MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md) searches the list of known layers for each layer contained in the input model topology before building the model's internal representation, optimizing the model, and producing the Intermediate Representation files.
The [Inference Engine](https://docs.openvinotoolkit.org/2019_R1.1/_docs_IE_DG_Deep_Learning_Inference_Engine_DevGuide.html) loads the layers from the input model IR files into the specified device plugin, which will search a list of known layer implementations for the device. If your topology contains layers that are not in the list of known layers for the device, the Inference Engine considers the layer to be unsupported and reports an error. To see the layers that are supported by each device plugin for the Inference Engine, refer to the [Supported Devices](https://docs.openvinotoolkit.org/2019_R1.1/_docs_IE_DG_supported_plugins_Supported_Devices.html) documentation.
The [Inference Engine](../IE_DG/Deep_Learning_Inference_Engine_DevGuide.md) loads the layers from the input model IR files into the specified device plugin, which will search a list of known layer implementations for the device. If your topology contains layers that are not in the list of known layers for the device, the Inference Engine considers the layer to be unsupported and reports an error. To see the layers that are supported by each device plugin for the Inference Engine, refer to the [Supported Devices](../IE_DG/supported_plugins/Supported_Devices.md) documentation.
<br>
**Note:** If a device doesn't support a particular layer, an alternative to creating a new custom layer is to target an additional device using the HETERO plugin. The [Heterogeneous Plugin](https://docs.openvinotoolkit.org/2019_R1.1/_docs_IE_DG_supported_plugins_HETERO.html) may be used to run an inference model on multiple devices allowing the unsupported layers on one device to "fallback" to run on another device (e.g., CPU) that does support those layers.
> **NOTE:** If a device doesn't support a particular layer, an alternative to creating a new custom layer is to target an additional device using the HETERO plugin. The [Heterogeneous Plugin](../IE_DG/supported_plugins/HETERO.md) may be used to run an inference model on multiple devices allowing the unsupported layers on one device to "fallback" to run on another device (e.g., CPU) that does support those layers.
## Custom Layer Implementation Workflow
@ -40,7 +40,7 @@ The following figure shows the basic processing steps for the Model Optimizer hi
The Model Optimizer first extracts information from the input model which includes the topology of the model layers along with parameters, input and output format, etc., for each layer. The model is then optimized from the various known characteristics of the layers, interconnects, and data flow which partly comes from the layer operation providing details including the shape of the output for each layer. Finally, the optimized model is output to the model IR files needed by the Inference Engine to run the model.
The Model Optimizer starts with a library of known extractors and operations for each [supported model framework](https://docs.openvinotoolkit.org/2019_R1.1/_docs_MO_DG_prepare_model_Supported_Frameworks_Layers.html) which must be extended to use each unknown custom layer. The custom layer extensions needed by the Model Optimizer are:
The Model Optimizer starts with a library of known extractors and operations for each [supported model framework](../MO_DG/prepare_model/Supported_Frameworks_Layers.md) which must be extended to use each unknown custom layer. The custom layer extensions needed by the Model Optimizer are:
- Custom Layer Extractor
- Responsible for identifying the custom layer operation and extracting the parameters for each instance of the custom layer. The layer parameters are stored per instance and used by the layer operation before finally appearing in the output IR. Typically the input layer parameters are unchanged, which is the case covered by this tutorial.
@ -182,10 +182,10 @@ There are two options to convert your MXNet* model that contains custom layers:
2. If you have sub-graphs that should not be expressed with the analogous sub-graph in the Intermediate Representation, but another sub-graph should appear in the model, the Model Optimizer provides such an option. In MXNet the function is actively used for ssd models provides an opportunity to for the necessary subgraph sequences and replace them. To read more, see [Sub-graph Replacement in the Model Optimizer](../MO_DG/prepare_model/customize_model_optimizer/Subgraph_Replacement_Model_Optimizer.md).
## Kaldi\* Models with Custom Layers <a name="Kaldi-models-with-custom-layers"></a>
For information on converting your Kaldi* model containing custom layers see [Converting a Kaldi Model in the Model Optimizer Developer Guide](https://docs.openvinotoolkit.org/latest/_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_Kaldi.html).
For information on converting your Kaldi* model containing custom layers see [Converting a Kaldi Model in the Model Optimizer Developer Guide](../MO_DG/prepare_model/convert_model/Convert_Model_From_Kaldi.md).
## ONNX\* Models with Custom Layers <a name="ONNX-models-with-custom-layers"></a>
For information on converting your ONNX* model containing custom layers see [Converting an ONNX Model in the Model Optimizer Developer Guide](https://docs.openvinotoolkit.org/latest/_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_ONNX.html).
For information on converting your ONNX* model containing custom layers see [Converting an ONNX Model in the Model Optimizer Developer Guide](../MO_DG/prepare_model/convert_model/Convert_Model_From_ONNX.md).
## Step-by-Step Custom Layers Tutorial
For a step-by-step walk-through creating and executing a custom layer, see [Custom Layer Implementation Tutorial for Linux and Windows.](https://github.com/david-drew/OpenVINO-Custom-Layers/tree/master/2019.r2.0)
@ -194,10 +194,10 @@ For a step-by-step walk-through creating and executing a custom layer, see [Cust
- Intel® Distribution of OpenVINO™ toolkit home page: [https://software.intel.com/en-us/openvino-toolkit](https://software.intel.com/en-us/openvino-toolkit)
- OpenVINO™ toolkit online documentation: [https://docs.openvinotoolkit.org](https://docs.openvinotoolkit.org)
- [Model Optimizer Developer Guide](https://docs.openvinotoolkit.org/latest/_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide.html)
- [Kernel Extensivility in the Inference Engine Developer Guide](https://docs.openvinotoolkit.org/latest/_docs_IE_DG_Integrate_your_kernels_into_IE.html)
- [Inference Engine Samples Overview](https://docs.openvinotoolkit.org/latest/_docs_IE_DG_Samples_Overview.html)
- [Overview of OpenVINO™ Toolkit Pre-Trained Models](https://docs.openvinotoolkit.org/latest/_intel_models_index.html)
- [Model Optimizer Developer Guide](../MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md)
- [Kernel Extensivility in the Inference Engine Developer Guide](../IE_DG/Integrate_your_kernels_into_IE.md)
- [Inference Engine Samples Overview](../IE_DG/Samples_Overview.md)
- [Overview of OpenVINO™ Toolkit Pre-Trained Models](@ref omz_models_intel_index)
- [Inference Engine Tutorials](https://github.com/intel-iot-devkit/inference-tutorials-generic)
- For IoT Libraries and Code Samples see the [Intel® IoT Developer Kit](https://github.com/intel-iot-devkit).

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@ -51,9 +51,9 @@ weights respectively.
- Intel® Distribution of OpenVINO™ toolkit home page: [https://software.intel.com/en-us/openvino-toolkit](https://software.intel.com/en-us/openvino-toolkit)
- OpenVINO™ toolkit online documentation: [https://docs.openvinotoolkit.org](https://docs.openvinotoolkit.org)
- Model Optimizer Developer Guide: [Model Optimizer Developer Guide](https://docs.openvinotoolkit.org/latest/_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide.html)
- Inference Engine Developer Guide: [Inference Engine Developer Guide](https://docs.openvinotoolkit.org/latest/_docs_IE_DG_Deep_Learning_Inference_Engine_DevGuide.html)
- For more information on Sample Applications, see the [Inference Engine Samples Overview](https://docs.openvinotoolkit.org/latest/_docs_IE_DG_Samples_Overview.html)
- Model Optimizer Developer Guide: [Model Optimizer Developer Guide](../MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md)
- Inference Engine Developer Guide: [Inference Engine Developer Guide](Deep_Learning_Inference_Engine_DevGuide.md)
- For more information on Sample Applications, see the [Inference Engine Samples Overview](Samples_Overview.html)
- For information on a set of pre-trained models, see the [Overview of OpenVINO™ Toolkit Pre-Trained Models](@ref omz_models_intel_index)
- For information on Inference Engine Tutorials, see the [Inference Tutorials](https://github.com/intel-iot-devkit/inference-tutorials-generic)
- For IoT Libraries and Code Samples see the [Intel® IoT Developer Kit](https://github.com/intel-iot-devkit).

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@ -5,7 +5,7 @@ Currently, there are two groups of optimization methods that can influence on th
- **Quantization**. The rest of this document is dedicated to the representation of quantized models.
## Representation of quantized models
The OpenVINO Toolkit represents all the quantized models using the so-called [FakeQuantize](https://docs.openvinotoolkit.org/latest/_docs_MO_DG_prepare_model_convert_model_Legacy_IR_Layers_Catalog_Spec.html#FakeQuantize) operation. This operation is very expressive and allows mapping values from arbitrary input and output ranges. The whole idea behind that is quite simple: we project (discretize) the input values to the low-precision data type using affine transformation (with clamp and rounding) and then reproject discrete values back to the original range and data type. It can be considered as an emulation of the quantization process which happens at runtime.
The OpenVINO Toolkit represents all the quantized models using the so-called FakeQuantize operation (see the description in [this document](../MO_DG/prepare_model/convert_model/Legacy_IR_Layers_Catalog_Spec.md)). This operation is very expressive and allows mapping values from arbitrary input and output ranges. The whole idea behind that is quite simple: we project (discretize) the input values to the low-precision data type using affine transformation (with clamp and rounding) and then reproject discrete values back to the original range and data type. It can be considered as an emulation of the quantization process which happens at runtime.
In order to be able to execute a particular DL operation in low-precision all its inputs should be quantized i.e. should have FakeQuantize between operation and data blobs. The figure below shows an example of quantized Convolution which contains two FakeQuantize nodes: one for weights and one for activations (bias is quantized using the same parameters).
![quantized_convolution]
<div align="center">Figure 1. Example of quantized Convolution operation.</div>

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@ -9,7 +9,7 @@ For more details about low-precision model representation please refer to this [
During the model load each plugin can interpret quantization rules expressed in *FakeQuantize* operations:
- Independently based on the definition of *FakeQuantize* operation.
- Using a special library of low-precision transformations (LPT) which applies common rules for generic operations,
such as Convolution, Fully-Connected, Eltwise, etc., and translates "fake-quantized" models into the models with low-precision operations. For more information about low-precision flow please refer to the following [document](https://docs.openvinotoolkit.org/latest/_docs_IE_DG_Int8Inference.html).
such as Convolution, Fully-Connected, Eltwise, etc., and translates "fake-quantized" models into the models with low-precision operations. For more information about low-precision flow please refer to the following [document](../IE_DG/Int8Inference.md).
Here we provide only a high-level overview of the interpretation rules of FakeQuantize.
At runtime each FakeQuantize can be split into two independent operations: **Quantize** and **Dequantize**.

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@ -365,7 +365,7 @@ Keep in mind that there is no space between and inside the brackets for input sh
#### 58. What does the message "Please provide input layer names for input layer shapes" mean? <a name="question-58"></a>
When specifying input shapes for several layers, you must provide names for inputs, whose shapes will be overwritten. For usage examples, see [Converting a Caffe\* Model](https://docs.openvinotoolkit.org/latest/_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_Caffe.html). Additional information for `--input_shape` is in FAQ [#57](#question-57).
When specifying input shapes for several layers, you must provide names for inputs, whose shapes will be overwritten. For usage examples, see [Converting a Caffe* Model](convert_model/Convert_Model_From_Caffe.md. Additional information for `--input_shape` is in FAQ [#57](#question-57).
#### 59. What does the message "Values cannot be parsed" mean? <a name="question-59"></a>

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@ -216,7 +216,7 @@ Testing by Intel done on: see test date for each HW platform below.
| BIOS Release | September 21, 2018 | September 21, 2018 | December 03, 2019 |
| Test Date | July 8, 2020 | July 8, 2020 | July 8, 2020 |
Please follow this link for more detailed configuration descriptions: [Configuration Details](https://docs.openvinotoolkit.org/resources/benchmark_files/system_configurations_2020.4.html)
Please follow this link for more detailed configuration descriptions: [Configuration Details](https://docs.openvinotoolkit.org/resources/benchmark_files/system_configurations_2021.1.html)
\htmlonly
<style>

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docs/benchmarks/performance_benchmarks_faq.md
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@ -1,6 +1,6 @@
# Performance Information Frequently Asked Questions {#openvino_docs_performance_benchmarks_faq}
The following questions and answers are related to performance benchmarks published on the [Performance Information](https://docs.openvinotoolkit.org/latest/_docs_performance_benchmarks.html) documentation site.
The following questions and answers are related to [performance benchmarks](./performance_benchmarks.md) published on the documentation site.
#### 1. How often do performance benchmarks get updated?
New performance benchmarks are typically published on every `major.minor` release of the Intel® Distribution of OpenVINO™ toolkit.
@ -15,7 +15,7 @@ The models used in the performance benchmarks were chosen based on general adopt
CF means Caffe*, while TF means TensorFlow*.
#### 5. How can I run the benchmark results on my own?
All of the performance benchmarks were generated using the open-sourced tool within the Intel® Distribution of OpenVINO™ toolkit called `benchmark_app`, which is available in both [C++](https://docs.openvinotoolkit.org/latest/_inference_engine_samples_benchmark_app_README.html) and [Python](https://docs.openvinotoolkit.org/latest/_inference_engine_tools_benchmark_tool_README.html).
All of the performance benchmarks were generated using the open-sourced tool within the Intel® Distribution of OpenVINO™ toolkit called `benchmark_app`, which is available in both [C++](../../inference-engine/samples/benchmark_app/README.md) and [Python](../../inference-engine/tools/benchmark_tool/README.md).
#### 6. What image sizes are used for the classification network models?
The image size used in the inference depends on the network being benchmarked. The following table shows the list of input sizes for each network model.
@ -34,13 +34,13 @@ The image size used in the inference depends on the network being benchmarked. T
| [ssd300-CF](https://github.com/opencv/open_model_zoo/tree/master/models/public/ssd300) | SSD (VGG-16)_VOC-2007_Caffe | object detection | 300x300 |
#### 7. Where can I purchase the specific hardware used in the benchmarking?
Intel partners with various vendors all over the world. Visit the [Intel® AI: In Production Partners & Solutions Catalog](https://www.intel.com/content/www/us/en/internet-of-things/ai-in-production/partners-solutions-catalog.html) for a list of Equipment Makers and the [Supported Devices](https://docs.openvinotoolkit.org/latest/_docs_IE_DG_supported_plugins_Supported_Devices.html) documentation. You can also remotely test and run models before purchasing any hardware by using [Intel® DevCloud for the Edge](http://devcloud.intel.com/edge/).
Intel partners with various vendors all over the world. Visit the [Intel® AI: In Production Partners & Solutions Catalog](https://www.intel.com/content/www/us/en/internet-of-things/ai-in-production/partners-solutions-catalog.html) for a list of Equipment Makers and the [Supported Devices](../IE_DG/supported_plugins/Supported_Devices.md) documentation. You can also remotely test and run models before purchasing any hardware by using [Intel® DevCloud for the Edge](http://devcloud.intel.com/edge/).
#### 8. How can I optimize my models for better performance or accuracy?
We published a set of guidelines and recommendations to optimize your models available in an [introductory](https://docs.openvinotoolkit.org/latest/_docs_IE_DG_Intro_to_Performance.html) guide and an [advanced](https://docs.openvinotoolkit.org/latest/_docs_optimization_guide_dldt_optimization_guide.html) guide. For further support, please join the conversation in the [Community Forum](https://software.intel.com/en-us/forums/intel-distribution-of-openvino-toolkit).
We published a set of guidelines and recommendations to optimize your models available in an [introductory](../IE_DG/Intro_to_Performance.md) guide and an [advanced](../optimization_guide/dldt_optimization_guide.md) guide. For further support, please join the conversation in the [Community Forum](https://software.intel.com/en-us/forums/intel-distribution-of-openvino-toolkit).
#### 9. Why are INT8 optimized models used for benchmarking on CPUs with no VNNI support?
The benefit of low-precision optimization using the OpenVINO™ toolkit model optimizer extends beyond processors supporting VNNI through Intel® DL Boost. The reduced bit width of INT8 compared to FP32 allows Intel® CPU to process the data faster and thus offers better throughput on any converted model agnostic of the intrinsically supported low-precision optimizations within Intel® hardware. Please refer to [INT8 vs. FP32 Comparison on Select Networks and Platforms](https://docs.openvinotoolkit.org/latest/_docs_performance_int8_vs_fp32.html) for comparison on boost factors for different network models and a selection of Intel® CPU architectures, including AVX-2 with Intel® Core™ i7-8700T, and AVX-512 (VNNI) with Intel® Xeon® 5218T and Intel® Xeon® 8270.
The benefit of low-precision optimization using the OpenVINO™ toolkit model optimizer extends beyond processors supporting VNNI through Intel® DL Boost. The reduced bit width of INT8 compared to FP32 allows Intel® CPU to process the data faster and thus offers better throughput on any converted model agnostic of the intrinsically supported low-precision optimizations within Intel® hardware. Please refer to [INT8 vs. FP32 Comparison on Select Networks and Platforms](./performance_int8_vs_fp32.html) for comparison on boost factors for different network models and a selection of Intel® CPU architectures, including AVX-2 with Intel® Core™ i7-8700T, and AVX-512 (VNNI) with Intel® Xeon® 5218T and Intel® Xeon® 8270.
#### 10. Previous releases included benchmarks on googlenet-v1. Why is there no longer benchmarks on this neural network model?
We replaced googlenet-v1 to [resnet-18-pytorch](https://github.com/opencv/open_model_zoo/blob/master/models/public/resnet-18-pytorch/resnet-18-pytorch.md) due to changes in developer usage. The public model resnet-18 is used by many developers as an Image Classification model. This pre-optimized model was also trained on the ImageNet database, similar to googlenet-v1. Both googlenet-v1 and resnet-18 will remain part of the Open Model Zoo. Developers are encouraged to utilize resnet-18-pytorch for Image Classification use cases.

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@ -18,7 +18,9 @@
<tab type="usergroup" title="Install Intel® Distribution of OpenVINO™ toolkit for Windows* with FPGA support" url="@ref openvino_docs_install_guides_installing_openvino_windows_fpga">
<tab type="user" title="Configuration Guide for OpenVINO and the Intel® Vision Accelerator Design with an Intel® Arria 10 FPGA SG2 (IEIs Mustang-F100-A10) on Windows*" url="@ref openvino_docs_install_guides_VisionAcceleratorFPGA_Configure_Windows"/>
</tab>
<tab type="user" title="Get Started with OpenVINO™ toolkit on Windows*" url="@ref openvino_docs_get_started_get_started_windows"/>
<tab type="user" title="Install Intel® Distribution of OpenVINO™ toolkit for macOS*" url="@ref openvino_docs_install_guides_installing_openvino_macos"/>
<tab type="user" title="Get Started with OpenVINO™ toolkit on macOS*" url="@ref openvino_docs_get_started_get_started_macos"/>
<tab type="user" title="Install Intel® Distribution of OpenVINO™ toolkit for Raspbian* OS" url="@ref openvino_docs_install_guides_installing_openvino_raspbian"/>
<tab type="user" title="Intel® Movidius™ VPUs Setup Guide for Use with Intel® Distribution of OpenVINO™ toolkit" url="@ref openvino_docs_install_guides_movidius_setup_guide"/>
<tab type="user" title="Intel® Movidius™ VPUs Programming Guide for Use with Intel® Distribution of OpenVINO™ toolkit" url="@ref openvino_docs_install_guides_movidius_programming_guide"/>

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@ -15,13 +15,13 @@ The toolkit consists of three primary components:
In addition, demo scripts, code samples and demo applications are provided to help you get up and running with the toolkit:
* **Demo Scripts** - Shell scripts that automatically perform the workflow steps to demonstrate running inference pipelines for different scenarios.
* [**Code Samples**](https://docs.openvinotoolkit.org/latest/_docs_IE_DG_Samples_Overview.html) - Small console applications that show you how to:
* **[Code Samples](../IE_DG/Samples_Overview.md)** - Small console applications that show you how to:
* Utilize specific OpenVINO capabilities in an application
* Perform specific tasks, such as loading a model, running inference, querying specific device capabilities, and more.
* [**Demo Applications**](https://docs.openvinotoolkit.org/latest/_demos_README.html) - Console applications that provide robust application templates to help you implement specific deep learning scenarios. These applications involve increasingly complex processing pipelines that gather analysis data from several models that run inference simultaneously, such as detecting a person in a video stream along with detecting the person's physical attributes, such as age, gender, and emotional state.
* **[Demo Applications](@ref omz_demos_README)** - Console applications that provide robust application templates to help you implement specific deep learning scenarios. These applications involve increasingly complex processing pipelines that gather analysis data from several models that run inference simultaneously, such as detecting a person in a video stream along with detecting the person's physical attributes, such as age, gender, and emotional state.
## <a name="openvino-installation"></a>Intel® Distribution of OpenVINO™ toolkit Installation and Deployment Tools Directory Structure
This guide assumes you completed all Intel® Distribution of OpenVINO™ toolkit installation and configuration steps. If you have not yet installed and configured the toolkit, see [Install Intel® Distribution of OpenVINO™ toolkit for Linux*](https://docs.openvinotoolkit.org/latest/_docs_install_guides_installing_openvino_linux.html).
This guide assumes you completed all Intel® Distribution of OpenVINO™ toolkit installation and configuration steps. If you have not yet installed and configured the toolkit, see [Install Intel® Distribution of OpenVINO™ toolkit for Linux*](../install_guides/installing-openvino-linux.md).
By default, the installation directory is `/opt/intel/openvino`, but the installation gave you the option to use the directory of your choice. If you installed the Intel® Distribution of OpenVINO™ toolkit to a directory other than the default, replace `/opt/intel` with the directory in which you installed the software.
@ -70,7 +70,9 @@ The demo scripts can run inference on any [supported target device](https://soft
./<script_name> -d [CPU, GPU, MYRIAD, HDDL]
```
Before running the demo applications on Intel® Processor Graphics or on an Intel® Neural Compute Stick 2 device, you must complete the [Steps for Intel® Processor Graphics (GPU)](https://docs.openvinotoolkit.org/2020.1/_docs_install_guides_installing_openvino_linux.html#additional-GPU-steps) or [Steps for Intel® Neural Compute Stick 2](https://docs.openvinotoolkit.org/2020.1/_docs_install_guides_installing_openvino_linux.html#additional-NCS-steps).
Before running the demo applications on Intel® Processor Graphics or on an Intel® Neural Compute Stick 2 device, you must complete the additional configuration steps. For details, see:
* Steps for Intel® Processor Graphics (GPU) section in the [installation instructions](../install_guides/installing-openvino-linux.md)
* Steps for Intel® Neural Compute Stick 2 section in the [installation instructions](../install_guides/installing-openvino-linux.md).
The following paragraphs describe each demo script.
@ -149,7 +151,7 @@ To run the script performing inference on Intel® Processor Graphics:
When the verification script completes, you see an image that displays the resulting frame with detections rendered as bounding boxes, and text:
![](https://docs.openvinotoolkit.org/latest/inference_pipeline_script_lnx.png)
![](../img/inference_pipeline_script_lnx.png)
</details>
@ -187,9 +189,9 @@ You will perform the following steps:
Each demo and code sample is a separate application, but they use the same behavior and components. The code samples and demo applications are:
* [Code Samples](https://docs.openvinotoolkit.org/latest/_docs_IE_DG_Samples_Overview.html) - Small console applications that show how to utilize specific OpenVINO capabilities within an application and execute specific tasks such as loading a model, running inference, querying specific device capabilities, and more.
* [Code Samples](../IE_DG/Samples_Overview.html) - Small console applications that show how to utilize specific OpenVINO capabilities within an application and execute specific tasks such as loading a model, running inference, querying specific device capabilities, and more.
* [Demo Applications](https://docs.openvinotoolkit.org/latest/_demos_README.html) - Console applications that provide robust application templates to support developers in implementing specific deep learning scenarios. They may also involve more complex processing pipelines that gather analysis from several models that run inference simultaneously. For example concurrently detecting a person in a video stream and detecting attributes such as age, gender and/or emotions.
* [Demo Applications](@ref omz_demos_README) - Console applications that provide robust application templates to support developers in implementing specific deep learning scenarios. They may also involve more complex processing pipelines that gather analysis from several models that run inference simultaneously. For example concurrently detecting a person in a video stream and detecting attributes such as age, gender and/or emotions.
Inputs you'll need to specify:
- **A compiled OpenVINO™ code sample or demo application** that runs inferencing against a model that has been run through the Model Optimizer, resuiting in an IR, using the other inputs you provide.
@ -201,7 +203,7 @@ Inputs you'll need to specify:
To perform sample inference, run the Image Classification code sample and Security Barrier Camera demo application that were automatically compiled when you ran the Image Classification and Inference Pipeline demo scripts. The binary files are in the `~/inference_engine_cpp_samples_build/intel64/Release` and `~/inference_engine_demos_build/intel64/Release` directories, respectively.
To run other sample code or demo applications, build them from the source files delivered as part of the OpenVINO toolkit. To learn how to build these, see the [Inference Engine Code Samples Overview](https://docs.openvinotoolkit.org/latest/_docs_IE_DG_Samples_Overview.html#build_samples_linux) and the [Demo Applications Overview](https://docs.openvinotoolkit.org/latest/_demos_README.html#build_the_demo_applications) sections.
To run other sample code or demo applications, build them from the source files delivered as part of the OpenVINO toolkit. To learn how to build these, see the [Inference Engine Code Samples Overview](../IE_DG/Samples_Overview.md) and [Demo Applications Overview](@ref omz_demos_README) sections.
### <a name="download-models"></a> Step 1: Download the Models
@ -211,7 +213,7 @@ You must have a model that is specific for you inference task. Example model typ
- Custom (Often based on SSD)
Options to find a model suitable for the OpenVINO™ toolkit are:
- Download public and Intel's pre-trained models from the [Open Model Zoo](https://github.com/opencv/open_model_zoo) using [Model Downloader tool](https://docs.openvinotoolkit.org/latest/_tools_downloader_README.html#model_downloader_usage).
- Download public and Intel's pre-trained models from the [Open Model Zoo](https://github.com/opencv/open_model_zoo) using [Model Downloader tool](@ref omz_tools_downloader_README).
- Download from GitHub*, Caffe* Zoo, TensorFlow* Zoo, etc.
- Train your own model.
@ -362,7 +364,7 @@ As an alternative, the Intel® Distribution of OpenVINO™ toolkit includes two
### <a name="run-image-classification"></a>Step 4: Run the Image Classification Code Sample
> **NOTE**: The Image Classification code sample is automatically compiled when you ran the Image Classification demo script. If you want to compile it manually, see the [Inference Engine Code Samples Overview](https://docs.openvinotoolkit.org/latest/_docs_IE_DG_Samples_Overview.html#build_samples_linux) section.
> **NOTE**: The Image Classification code sample is automatically compiled when you ran the Image Classification demo script. If you want to compile it manually, see the [Inference Engine Code Samples Overview](../IE_DG/Samples_Overview.html#build_samples_linux) section.
To run the **Image Classification** code sample with an input image on the IR:
@ -390,21 +392,25 @@ The following commands run the Image Classification Code Sample using the `car.p
**GPU:**
> **NOTE**: Running inference on Intel® Processor Graphics (GPU) requires
[additional hardware configuration steps](https://docs.openvinotoolkit.org/latest/_docs_install_guides_installing_openvino_linux.html#additional-GPU-steps).
> **NOTE**: Running inference on Intel® Processor Graphics (GPU) requires additional hardware configuration steps. For details, see the Steps for Intel® Processor Graphics (GPU) section in the [installation instructions](../install_guides/installing-openvino-linux.md).
```sh
./classification_sample_async -i /opt/intel/openvino/deployment_tools/demo/car.png -m ~/models/public/squeezenet1.1/ir/squeezenet1.1.xml -d GPU
```
**MYRIAD:**
> **NOTE**: Running inference on VPU devices (Intel® Movidius™ Neural Compute
Stick or Intel® Neural Compute Stick 2) with the MYRIAD plugin requires
[additional hardware configuration steps](inference-engine/README.md#optional-additional-installation-steps-for-the-intel-movidius-neural-compute-stick-and-neural-compute-stick-2).
> **NOTE**: Running inference on VPU devices (Intel® Neural Compute Stick 2) with the MYRIAD plugin requires additional hardware configuration steps. For details, see the Steps for Intel® Neural Compute Stick 2 section in the [installation instructions](../install_guides/installing-openvino-linux.md).
```sh
./classification_sample_async -i /opt/intel/openvino/deployment_tools/demo/car.png -m ~/models/public/squeezenet1.1/ir/squeezenet1.1.xml -d MYRIAD
```
**HDDL:**
> **NOTE**: Running inference on the Intel® Vision Accelerator Design with Intel® Movidius™ VPUs device with the HDDL plugin requires additional hardware configuration steps. For details, see the Steps for Intel® Vision Accelerator Design with Intel® Movidius™ VPUs section in the [installation instructions](../install_guides/installing-openvino-linux.md).
```sh
./classification_sample_async -i /opt/intel/openvino/deployment_tools/demo/car.png -m ~/models/public/squeezenet1.1/ir/squeezenet1.1.xml -d HDDL
```
When the Sample Application completes, you see the label and confidence for the top-10 categories on the display. Below is a sample output with inference results on CPU:
```sh
Top 10 results:
@ -437,7 +443,7 @@ Throughput: 375.3339402 FPS
### <a name="run-security-barrier"></a>Step 5: Run the Security Barrier Camera Demo Application
> **NOTE**: The Security Barrier Camera Demo Application is automatically compiled when you ran the Inference Pipeline demo scripts. If you want to build it manually, see the [Demo Applications Overview](https://docs.openvinotoolkit.org/latest/_demos_README.html#build_the_demo_applications) section.
> **NOTE**: The Security Barrier Camera Demo Application is automatically compiled when you ran the Inference Pipeline demo scripts. If you want to build it manually, see the [Demo Applications Overview](@ref omz_demos_README) section.
To run the **Security Barrier Camera Demo Application** using an input image on the prepared IRs:
@ -465,20 +471,25 @@ To run the **Security Barrier Camera Demo Application** using an input image on
**GPU:**
> **NOTE**: Running inference on Intel® Processor Graphics (GPU) requires [additional hardware configuration steps](https://docs.openvinotoolkit.org/latest/_docs_install_guides_installing_openvino_linux.html#additional-GPU-steps).
> **NOTE**: Running inference on Intel® Processor Graphics (GPU) requires additional hardware configuration steps. For details, see the Steps for Intel® Processor Graphics (GPU) section in the [installation instructions](../install_guides/installing-openvino-linux.md).
```sh
./security_barrier_camera_demo -i /opt/intel/openvino/deployment_tools/demo/car_1.bmp -m <path_to_model>/vehicle-license-plate-detection-barrier-0106.xml -m_va <path_to_model>/vehicle-attributes-recognition-barrier-0039.xml -m_lpr <path_to_model>/license-plate-recognition-barrier-0001.xml -d GPU
```
**MYRIAD:**
> **NOTE**: Running inference on VPU devices (Intel® Movidius™ Neural Compute
Stick or Intel® Neural Compute Stick 2) with the MYRIAD plugin requires
[additional hardware configuration steps](https://docs.openvinotoolkit.org/latest/_docs_install_guides_installing_openvino_linux.html#additional-NCS-steps).
> **NOTE**: Running inference on the Intel® Neural Compute Stick 2 device with the MYRIAD plugin requires additional hardware configuration steps. For details, see the Steps for Intel® Neural Compute Stick 2 section in the [installation instructions](../install_guides/installing-openvino-linux.md).
```sh
./classification_sample_async -i <DLDT_DIR>/inference-engine/samples/sample_data/car.png -m <ir_dir>/squeezenet1.1.xml -d MYRIAD
```
**HDDL:**
> **NOTE**: Running inference on the Intel® Vision Accelerator Design with Intel® Movidius™ VPUs device with the HDDL plugin requires additional hardware configuration steps. For details, see the Steps for Intel® Vision Accelerator Design with Intel® Movidius™ VPUs section in the [installation instructions](../install_guides/installing-openvino-linux.md).
```sh
./classification_sample_async -i <DLDT_DIR>/inference-engine/samples/sample_data/car.png -m <ir_dir>/squeezenet1.1.xml -d HDDL
```
</details>
## <a name="basic-guidelines-sample-application"></a>Basic Guidelines for Using Code Samples and Demo Applications
@ -548,9 +559,9 @@ You can see all the sample application’s parameters by adding the `-h` or `--h
Use these resources to learn more about the OpenVINO™ toolkit:
* [OpenVINO™ Release Notes](https://software.intel.com/en-us/articles/OpenVINO-RelNotes)
* [Introduction to Intel® Deep Learning Deployment Toolkit](https://docs.openvinotoolkit.org/latest/_docs_IE_DG_Introduction.html)
* [Inference Engine Developer Guide](https://docs.openvinotoolkit.org/latest/_docs_IE_DG_Deep_Learning_Inference_Engine_DevGuide.html)
* [Model Optimizer Developer Guide](https://docs.openvinotoolkit.org/latest/_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide.html)
* [Inference Engine Samples Overview](https://docs.openvinotoolkit.org/latest/_docs_IE_DG_Samples_Overview.html)
* [Introduction to Intel® Deep Learning Deployment Toolkit](../IE_DG/Introduction.md)
* [Inference Engine Developer Guide](../IE_DG/Deep_Learning_Inference_Engine_DevGuide.md)
* [Model Optimizer Developer Guide](../MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md)
* [Inference Engine Samples Overview](../IE_DG/Samples_Overview.md)
* [Overview of OpenVINO™ Toolkit Pre-Trained Models](https://software.intel.com/en-us/openvino-toolkit/documentation/pretrained-models)
* [OpenVINO™ Hello World Face Detection Exercise](https://github.com/intel-iot-devkit/inference-tutorials-generic)

18
docs/get_started/get_started_macos.md
View File

@ -15,10 +15,10 @@ The toolkit consists of three primary components:
In addition, demo scripts, code samples and demo applications are provided to help you get up and running with the toolkit:
* **Demo Scripts** - Batch scripts that automatically perform the workflow steps to demonstrate running inference pipelines for different scenarios.
* [**Code Samples**](../IE_DG/Samples_Overview.md) - Small console applications that show you how to:
* **[Code Samples](../IE_DG/Samples_Overview.md)** - Small console applications that show you how to:
* Utilize specific OpenVINO capabilities in an application.
* Perform specific tasks, such as loading a model, running inference, querying specific device capabilities, and more.
* [**Demo Applications**](@ref omz_demos_README) - Console applications that provide robust application templates to help you implement specific deep learning scenarios. These applications involve increasingly complex processing pipelines that gather analysis data from several models that run inference simultaneously, such as detecting a person in a video stream along with detecting the person's physical attributes, such as age, gender, and emotional state.
* **[Demo Applications](@ref omz_demos_README)** - Console applications that provide robust application templates to help you implement specific deep learning scenarios. These applications involve increasingly complex processing pipelines that gather analysis data from several models that run inference simultaneously, such as detecting a person in a video stream along with detecting the person's physical attributes, such as age, gender, and emotional state.
## <a name="openvino-installation"></a>Intel® Distribution of OpenVINO™ toolkit Installation and Deployment Tools Directory Structure
This guide assumes you completed all Intel® Distribution of OpenVINO™ toolkit installation and configuration steps. If you have not yet installed and configured the toolkit, see [Install Intel® Distribution of OpenVINO™ toolkit for macOS*](../install_guides/installing-openvino-macos.md).
@ -77,7 +77,7 @@ The demo scripts can run inference on any [supported target device](https://soft
./<script_name> -d [CPU, MYRIAD]
```
Before running the demo applications on Intel® Neural Compute Stick 2 device, you must complete the [Steps for Intel® Neural Compute Stick 2](../install_guides/installing-openvino-macos.md#additional-NCS2-steps).
Before running the demo applications on Intel® Neural Compute Stick 2 device, you must complete additional configuration steps. For details, see the Steps for Intel® Neural Compute Stick 2 section in the [installation instructions](../install_guides/installing-openvino-macos.md).
The following paragraphs describe each demo script.
@ -198,7 +198,7 @@ Inputs you need to specify when using a code sample or demo application:
To perform sample inference, run the Image Classification code sample and Security Barrier Camera demo application that are automatically compiled when you run the Image Classification and Inference Pipeline demo scripts. The binary files are in the `~/inference_engine_samples_build/intel64/Release` and `~/inference_engine_demos_build/intel64/Release` directories, respectively.
You can also build all available sample code and demo applications from the source files delivered with the OpenVINO toolkit. To learn how to do this, see the [Inference Engine Code Samples Overview](../IE_DG/Samples_Overview.md) and the [Demo Applications Overview](@ref omz_demos_README#build_the_demo_applications) documents.
You can also build all available sample code and demo applications from the source files delivered with the OpenVINO toolkit. To learn how to do this, see the instructions in the [Inference Engine Code Samples Overview](../IE_DG/Samples_Overview.md) and [Demo Applications Overview](@ref omz_demos_README) sections.
### <a name="download-models"></a> Step 1: Download the Models
@ -208,7 +208,7 @@ You must have a model that is specific for you inference task. Example model typ
- Custom (Often based on SSD)
Options to find a model suitable for the OpenVINO™ toolkit are:
- Download public and Intel's pre-trained models from the [Open Model Zoo](https://github.com/opencv/open_model_zoo) using [Model Downloader tool](@ref omz_tools_downloader_README#model_downloader_usage).
- Download public and Intel's pre-trained models from the [Open Model Zoo](https://github.com/opencv/open_model_zoo) using [Model Downloader tool](@ref omz_tools_downloader_README).
- Download from GitHub*, Caffe* Zoo, TensorFlow* Zoo, and other resources.
- Train your own model.
@ -389,8 +389,7 @@ The following commands run the Image Classification Code Sample using the `car.p
**MYRIAD:**
> **NOTE**: Running inference on VPU devices (Intel® Neural Compute Stick 2) with the MYRIAD plugin requires
[additional hardware configuration steps](../install_guides/installing-openvino-macos.md#additional-NCS2-steps).
> **NOTE**: Running inference on VPU devices (Intel® Neural Compute Stick 2) with the MYRIAD plugin requires additional hardware configuration steps. For details, see the Steps for Intel® Neural Compute Stick 2 section in the [installation instructions](../install_guides/installing-openvino-macos.md).
```sh
./classification_sample_async -i /opt/intel/openvino/deployment_tools/demo/car.png -m ~/models/public/squeezenet1.1/ir/squeezenet1.1.xml -d MYRIAD
```
@ -421,7 +420,7 @@ classid probability label
### <a name="run-security-barrier"></a>Step 5: Run the Security Barrier Camera Demo Application
> **NOTE**: The Security Barrier Camera Demo Application is automatically compiled when you ran the Inference Pipeline demo scripts. If you want to build it manually, see the [Demo Applications Overview](@ref omz_demos_README#build_the_demo_applications) section.
> **NOTE**: The Security Barrier Camera Demo Application is automatically compiled when you run the Inference Pipeline demo scripts. If you want to build it manually, see the instructions in the [Demo Applications Overview](@ref omz_demos_README) section.
To run the **Security Barrier Camera Demo Application** using an input image on the prepared IRs:
@ -449,8 +448,7 @@ To run the **Security Barrier Camera Demo Application** using an input image on
**MYRIAD:**
> **NOTE**: Running inference on VPU devices (Intel® Neural Compute Stick 2) with the MYRIAD plugin requires
[additional hardware configuration steps](../install_guides/installing-openvino-macos.md#additional-NCS2-steps).
> **NOTE**: Running inference on VPU devices (Intel® Neural Compute Stick 2) with the MYRIAD plugin requires additional hardware configuration steps. For details, see the Steps for Intel® Neural Compute Stick 2 section in the [installation instructions](../install_guides/installing-openvino-macos.md).
```sh
./classification_sample_async -i <INSTALL_DIR>/inference-engine/samples/sample_data/car.png -m <ir_dir>/squeezenet1.1.xml -d MYRIAD
```

29
docs/get_started/get_started_windows.md
View File

@ -16,10 +16,10 @@ The toolkit consists of three primary components:
In addition, demo scripts, code samples and demo applications are provided to help you get up and running with the toolkit:
* **Demo Scripts** - Batch scripts that automatically perform the workflow steps to demonstrate running inference pipelines for different scenarios.
* [**Code Samples**](../IE_DG/Samples_Overview.md) - Small console applications that show you how to:
* **[Code Samples](../IE_DG/Samples_Overview.md)** - Small console applications that show you how to:
* Utilize specific OpenVINO capabilities in an application.
* Perform specific tasks, such as loading a model, running inference, querying specific device capabilities, and more.
* [**Demo Applications**](@ref omz_demos_README) - Console applications that provide robust application templates to help you implement specific deep learning scenarios. These applications involve increasingly complex processing pipelines that gather analysis data from several models that run inference simultaneously, such as detecting a person in a video stream along with detecting the person's physical attributes, such as age, gender, and emotional state.
* **[Demo Applications](@ref omz_demos_README)** - Console applications that provide robust application templates to help you implement specific deep learning scenarios. These applications involve increasingly complex processing pipelines that gather analysis data from several models that run inference simultaneously, such as detecting a person in a video stream along with detecting the person's physical attributes, such as age, gender, and emotional state.
## <a name="openvino-installation"></a>Intel® Distribution of OpenVINO™ toolkit Installation and Deployment Tools Directory Structure
This guide assumes you completed all Intel® Distribution of OpenVINO™ toolkit installation and configuration steps. If you have not yet installed and configured the toolkit, see [Install Intel® Distribution of OpenVINO™ toolkit for Windows*](../install_guides/installing-openvino-windows.md).
@ -76,9 +76,10 @@ The demo scripts can run inference on any [supported target device](https://soft
.\<script_name> -d [CPU, GPU, MYRIAD, HDDL]
```
Before running the demo applications on Intel® Processor Graphics, you must complete the [Steps for Intel® Processor Graphics (GPU)](../install_guides/installing-openvino-windows.md#Install-GPU).
Before running the demo applications on Intel® Processor Graphics or Intel® Vision Accelerator Design with Intel® Movidius™ VPUs, you must complete additional hardware configuration steps. For details, see the following sections in the [installation instructions](../install_guides/installing-openvino-windows.md):
* Additional Installation Steps for Intel® Processor Graphics (GPU)
* Additional Installation Steps for Intel® Vision Accelerator Design with Intel® Movidius™ VPUs
Before running the demo applications on Intel® Vision Accelerator Design with Intel® Movidius™ VPUs, you must complete the [Steps for Intel® Vision Accelerator Design with Intel® Movidius™ VPUs](../install_guides/installing-openvino-windows.md#hddl-myriad).
The following paragraphs describe each demo script.
### Image Classification Demo Script
@ -198,7 +199,7 @@ Inputs you need to specify when using a code sample or demo application:
To perform sample inference, run the Image Classification code sample and Security Barrier Camera demo application that are automatically compiled when you run the Image Classification and Inference Pipeline demo scripts. The binary files are in the `C:\Users\<USER_ID>\Intel\OpenVINO\inference_engine_cpp_samples_build\intel64\Release` and `C:\Users\<USER_ID>\Intel\OpenVINO\inference_engine_demos_build\intel64\Release` directories, respectively.
You can also build all available sample code and demo applications from the source files delivered with the OpenVINO toolkit. To learn how to do this, see the [Inference Engine Code Samples Overview](../IE_DG/Samples_Overview.md#build_samples_linux) and the [Demo Applications Overview](@ref omz_demos_README#build_the_demo_applications) sections.
You can also build all available sample code and demo applications from the source files delivered with the OpenVINO™ toolkit. To learn how to do this, see the instruction in the [Inference Engine Code Samples Overview](../IE_DG/Samples_Overview.md) and [Demo Applications Overview](@ref omz_demos_README) sections.
### <a name="download-models"></a> Step 1: Download the Models
@ -208,7 +209,7 @@ You must have a model that is specific for you inference task. Example model typ
- Custom (Often based on SSD)
Options to find a model suitable for the OpenVINO™ toolkit are:
- Download public and Intel's pre-trained models from the [Open Model Zoo](https://github.com/opencv/open_model_zoo) using [Model Downloader tool](@ref omz_tools_downloader_README#model_downloader_usage).
- Download public and Intel's pre-trained models from the [Open Model Zoo](https://github.com/opencv/open_model_zoo) using the [Model Downloader tool](@ref omz_tools_downloader_README).
- Download from GitHub*, Caffe* Zoo, TensorFlow* Zoo, and other resources.
- Train your own model.
@ -359,7 +360,7 @@ As an alternative, the Intel® Distribution of OpenVINO™ toolkit includes two
### <a name="run-image-classification"></a>Step 4: Run the Image Classification Code Sample
> **NOTE**: The Image Classification code sample is automatically compiled when you run the Image Classification demo script. If you want to compile it manually, see the [Inference Engine Code Samples Overview](../IE_DG/Samples_Overview.md#build_samples_windows) section.
> **NOTE**: The Image Classification code sample is automatically compiled when you run the Image Classification demo script. If you want to compile it manually, see the Build the Sample Applications on Microsoft Windows* OS section in [Inference Engine Code Samples Overview](../IE_DG/Samples_Overview.md).
To run the **Image Classification** code sample with an input image on the IR:
@ -387,7 +388,7 @@ The following commands run the Image Classification Code Sample using the `car.p
**GPU:**
> **NOTE**: Running inference on Intel® Processor Graphics (GPU) requires [additional hardware configuration steps](../install_guides/installing-openvino-windows.md#Install-GPU).
> **NOTE**: Running inference on Intel® Processor Graphics (GPU) requires additional hardware configuration steps. For details, see the Steps for Intel® Processor Graphics (GPU) section in the [installation instructions](../install_guides/installing-openvino-windows.md).
```bat
.\classification_sample_async -i <INSTALL_DIR>\deployment_tools\demo\car.png -m C:\Users\<USER_ID>\models\public\squeezenet1.1\ir\squeezenet1.1.xml -d GPU
```
@ -424,7 +425,7 @@ classid probability label
### <a name="run-security-barrier"></a>Step 5: Run the Security Barrier Camera Demo Application
> **NOTE**: The Security Barrier Camera Demo Application is automatically compiled when you run the Inference Pipeline demo scripts. If you want to build it manually, see the [Demo Applications Overview](@ref omz_demos_README#build_the_demo_applications) section.
> **NOTE**: The Security Barrier Camera Demo Application is automatically compiled when you run the Inference Pipeline demo scripts. If you want to build it manually, see the instructions in the [Demo Applications Overview](@ref omz_demos_README) section.
To run the **Security Barrier Camera Demo Application** using an input image on the prepared IRs:
@ -452,7 +453,7 @@ To run the **Security Barrier Camera Demo Application** using an input image on
**GPU:**
> **NOTE**: Running inference on Intel® Processor Graphics (GPU) requires [additional hardware configuration steps](../install_guides/installing-openvino-windows.md#Install-GPU).
> **NOTE**: Running inference on Intel® Processor Graphics (GPU) requires additional hardware configuration steps. For details, see the Steps for Intel® Processor Graphics (GPU) section in the [installation instructions](../install_guides/installing-openvino-windows.md).
```bat
.\security_barrier_camera_demo -i <INSTALL_DIR>\deployment_tools\demo\car_1.bmp -m <path_to_model>/vehicle-license-plate-detection-barrier-0106.xml -m_va <path_to_model>/vehicle-attributes-recognition-barrier-0039.xml -m_lpr <path_to_model>/license-plate-recognition-barrier-0001.xml -d GPU
```
@ -532,9 +533,9 @@ You can see all the sample application’s parameters by adding the `-h` or `--h
Use these resources to learn more about the OpenVINO™ toolkit:
* [OpenVINO™ Release Notes](https://software.intel.com/en-us/articles/OpenVINO-RelNotes)
* [Introduction to Intel® Deep Learning Deployment Toolkit](https://docs.openvinotoolkit.org/latest/_docs_IE_DG_Introduction.html)
* [Inference Engine Developer Guide](https://docs.openvinotoolkit.org/latest/_docs_IE_DG_Deep_Learning_Inference_Engine_DevGuide.html)
* [Model Optimizer Developer Guide](https://docs.openvinotoolkit.org/latest/_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide.html)
* [Inference Engine Samples Overview](https://docs.openvinotoolkit.org/latest/_docs_IE_DG_Samples_Overview.html)
* [Introduction to Intel® Deep Learning Deployment Toolkit](../IE_DG/Introduction.md)
* [Inference Engine Developer Guide](../IE_DG/Deep_Learning_Inference_Engine_DevGuide.md)
* [Model Optimizer Developer Guide](../MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md)
* [Inference Engine Samples Overview](../IE_DG/Samples_Overview.md)
* [Overview of OpenVINO™ Toolkit Pre-Trained Models](https://software.intel.com/en-us/openvino-toolkit/documentation/pretrained-models)
* [OpenVINO™ Hello World Face Detection Exercise](https://github.com/intel-iot-devkit/inference-tutorials-generic)

12
docs/install_guides/PAC_Configure.md
View File

@ -10,7 +10,7 @@ The following describes the set-up of the Intel® Distribution of OpenVINO™ to
[OpenCL™ on Intel® PAC Quick Start Guide](https://www.intel.com/content/dam/altera-www/global/en_US/pdfs/literature/ug/ug-qs-ias-opencl-a10.pdf)
[Installing the Intel® Distribution of OpenVINO™ toolkit for Linux*](https://docs.openvinotoolkit.org/latest/_docs_install_guides_installing_openvino_linux.html)
[Installing the Intel® Distribution of OpenVINO™ toolkit for Linux*](installing-openvino-linux.md)
(Optional): Install NTFS support for transferring large installers if already downloaded on another machine.
```sh
@ -211,7 +211,7 @@ cp ~/openvino_models/models/public/squeezenet1.1/squeezenet1.1.* .
cp ~/openvino_models/ir/public/squeezenet1.1/FP16/squeezenet1.1.labels .
```
2. Note that the `squeezenet1.1.labels` file contains the classes used by ImageNet and is included here so that the inference results show text rather than classification numbers. Convert the model with the [Model Optimizer](https://docs.openvinotoolkit.org/latest/_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide.html). Note that the command below uses the alias defined in the script above and is not referred to in other documentation.
2. Note that the `squeezenet1.1.labels` file contains the classes used by ImageNet and is included here so that the inference results show text rather than classification numbers. Convert the model with the [Model Optimizer Developer Guide](../MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md). Note that the command below uses the alias defined in the script above and is not referred to in other documentation.
```sh
mo --input_model squeezenet1.1.caffemodel
```
@ -234,8 +234,6 @@ Use the [Intel® Distribution of OpenVINO™ toolkit with FPGA Hello World Face
## Additional Resources
Intel® Distribution of OpenVINO™ toolkit home page: [https://software.intel.com/en-us/openvino-toolkit](https://software.intel.com/en-us/openvino-toolkit)
Intel® Distribution of OpenVINO™ toolkit documentation: [https://docs.openvinotoolkit.org](https://docs.openvinotoolkit.org)
Inference Engine FPGA plugin documentation: [https://docs.openvinotoolkit.org/latest/_docs_IE_DG_supported_plugins_FPGA.html](https://docs.openvinotoolkit.org/latest/_docs_IE_DG_supported_plugins_FPGA.html)
* Intel® Distribution of OpenVINO™ toolkit home page: [https://software.intel.com/en-us/openvino-toolkit](https://software.intel.com/en-us/openvino-toolkit).
* Intel® Distribution of OpenVINO™ toolkit documentation: [https://docs.openvinotoolkit.org](https://docs.openvinotoolkit.org).
* [Inference Engine FPGA plugin documentation](../IE_DG/supported_plugins/FPGA.md).

10
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View File

@ -257,7 +257,7 @@ aocl program acl0 /opt/intel/openvino/bitstreams/a10_vision_design_sg2_bitstream
> **NOTE**: The SqueezeNet Caffe* model was already downloaded and converted to an FP16 IR when you ran the Image Classification Verification Script while [installing the Intel® Distribution of OpenVINO™ toolkit for Linux* with FPGA Support](installing-openvino-linux-fpga.md). Read this section only if you want to convert the model manually, otherwise skip and go to the next section to run the Image Classification sample application.
In this section, you will create an FP16 model suitable for hardware accelerators. For more information, see the [FPGA plugin](https://docs.openvinotoolkit.org/latest/_docs_IE_DG_supported_plugins_FPGA.html) section in the Inference Engine Developer Guide.
In this section, you will create an FP16 model suitable for hardware accelerators. For more information, see the [FPGA plugin](../IE_DG/supported_plugins/FPGA.md) section in the Inference Engine Developer Guide.
1. Create a directory for the FP16 SqueezeNet Model:
@ -319,8 +319,6 @@ Use the [Intel® Distribution of OpenVINO™ toolkit with FPGA Hello World Face
## Additional Resources
Intel® Distribution of OpenVINO™ toolkit home page: [https://software.intel.com/en-us/openvino-toolkit](https://software.intel.com/en-us/openvino-toolkit)
Intel® Distribution of OpenVINO™ toolkit documentation: [https://docs.openvinotoolkit.org/](https://docs.openvinotoolkit.org/)
Inference Engine FPGA plugin documentation: [https://docs.openvinotoolkit.org/latest/_docs_IE_DG_supported_plugins_FPGA.html](https://docs.openvinotoolkit.org/latest/_docs_IE_DG_supported_plugins_FPGA.html)
* Intel® Distribution of OpenVINO™ toolkit home page: [https://software.intel.com/en-us/openvino-toolkit](https://software.intel.com/en-us/openvino-toolkit).
* Intel® Distribution of OpenVINO™ toolkit documentation: [https://docs.openvinotoolkit.org](https://docs.openvinotoolkit.org).
* [Inference Engine FPGA plugin documentation](../IE_DG/supported_plugins/FPGA.md).

10
docs/install_guides/VisionAcceleratorFPGA_Configure_Windows.md
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@ -52,7 +52,7 @@ aocl program acl0 "C:\Program Files (x86)\IntelSWTools\openvino\a10_vision_desig
> **NOTE**: The SqueezeNet Caffe* model was already downloaded and converted to an FP16 IR when you ran the Image Classification Verification Script while [installing the Intel® Distribution of OpenVINO™ toolkit for Windows* with FPGA Support](installing-openvino-windows-fpga.md). Read this section only if you want to convert the model manually, otherwise skip and go to the next section to run the Image Classification sample application.
In this section, you will prepare a sample FP16 model suitable for hardware accelerators. For more information, see the [FPGA plugin](https://docs.openvinotoolkit.org/latest/_docs_IE_DG_supported_plugins_FPGA.html) section in the Inference Engine Developer Guide.
In this section, you will prepare a sample FP16 model suitable for hardware accelerators. For more information, see the [FPGA plugin](../IE_DG/supported_plugins/FPGA.html) section in the Inference Engine Developer Guide.
1. Create a directory for the FP16 SqueezeNet Model:
```sh
@ -108,8 +108,6 @@ Congratulations, you are done with the Intel® Distribution of OpenVINO™ toolk
## Additional Resources
Intel® Distribution of OpenVINO™ toolkit home page: [https://software.intel.com/en-us/openvino-toolkit](https://software.intel.com/en-us/openvino-toolkit)
Intel® Distribution of OpenVINO™ toolkit documentation: [https://docs.openvinotoolkit.org/](https://docs.openvinotoolkit.org/)
Inference Engine FPGA plugin documentation: [https://docs.openvinotoolkit.org/latest/_docs_IE_DG_supported_plugins_FPGA.html](https://docs.openvinotoolkit.org/latest/_docs_IE_DG_supported_plugins_FPGA.html)
* Intel® Distribution of OpenVINO™ toolkit home page: [https://software.intel.com/en-us/openvino-toolkit](https://software.intel.com/en-us/openvino-toolkit).
* Intel® Distribution of OpenVINO™ toolkit documentation: [https://docs.openvinotoolkit.org](https://docs.openvinotoolkit.org).
* [Inference Engine FPGA plugin documentation](../IE_DG/supported_plugins/FPGA.md).

16
docs/install_guides/deployment-manager-tool.md
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@ -8,14 +8,14 @@ The Deployment Manager is a Python\* command-line tool that is delivered within
* Intel® Distribution of OpenVINO™ toolkit for Linux\* (version 2019 R3 or higher) or Intel® Distribution of OpenVINO™ toolkit for Windows\* (version 2019 R4 or higher) installed on your development machine.
* Python\* 3.6 or higher is required to run the Deployment Manager.
* To run inference on a target device other than CPU, device drivers must be pre-installed. To install, see the following steps:
* **For Linux**:
* [Steps for Intel® Processor Graphics (GPU)](https://docs.openvinotoolkit.org/latest/_docs_install_guides_installing_openvino_linux.html#additional-GPU-steps)
* [Steps for Intel® Neural Compute Stick 2](https://docs.openvinotoolkit.org/latest/_docs_install_guides_installing_openvino_linux.html#additional-NCS-steps)
* [Steps for Intel® Vision Accelerator Design with Intel® Movidius™ VPUs](https://docs.openvinotoolkit.org/latest/_docs_install_guides_installing_openvino_linux.html#install-VPU)
* **For Windows**:
* [Steps for Intel® Processor Graphics (GPU)](https://docs.openvinotoolkit.org/latest/_docs_install_guides_installing_openvino_windows.html#Install-GPU)
* [Steps for the Intel® Vision Accelerator Design with Intel® Movidius™ VPUs](https://docs.openvinotoolkit.org/latest/_docs_install_guides_installing_openvino_windows.html#hddl-myriad)
* To run inference on a target device other than CPU, device drivers must be pre-installed:
* For **Linux**, see the following sections in the [installation instructions for Linux](../install_guides/installing-openvino-linux.md):
* Steps for Intel® Processor Graphics (GPU) section
* Steps for Intel® Neural Compute Stick 2 section
* Steps for Intel® Vision Accelerator Design with Intel® Movidius™ VPUs
* **For Windows**, see the following sections in the [installation instructions for Windows](../install_guides/installing-openvino-windows.md):
* Steps for Intel® Processor Graphics (GPU)
* Steps for the Intel® Vision Accelerator Design with Intel® Movidius™ VPUs
> **IMPORTANT**: The operating system on the target host must be the same as the development system on which you are creating the package. For example, if the target system is Ubuntu 16.04, the deployment package must be created from the OpenVINO™ toolkit installed on Ubuntu 16.04.

10
docs/install_guides/installing-openvino-apt.md
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@ -124,11 +124,11 @@ sudo apt autoremove intel-openvino-<PACKAGE_TYPE>-ubuntu<OS_VERSION>-<VERSION>.<
**Additional Resources**
- Intel® Distribution of OpenVINO™ toolkit home page: [https://software.intel.com/en-us/openvino-toolkit](https://software.intel.com/en-us/openvino-toolkit)
- OpenVINO™ toolkit online documentation: [https://docs.openvinotoolkit.org](https://docs.openvinotoolkit.org)
- [Model Optimizer Developer Guide](https://docs.openvinotoolkit.org/latest/_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide.html).
- [Inference Engine Developer Guide](https://docs.openvinotoolkit.org/latest/_docs_IE_DG_Deep_Learning_Inference_Engine_DevGuide.html)
- For more information on Sample Applications, see the [Inference Engine Samples Overview](https://docs.openvinotoolkit.org/latest/_docs_IE_DG_Samples_Overview.html).
- Intel® Distribution of OpenVINO™ toolkit home page: [https://software.intel.com/en-us/openvino-toolkit](https://software.intel.com/en-us/openvino-toolkit).
- OpenVINO™ toolkit online documentation: [https://docs.openvinotoolkit.org](https://docs.openvinotoolkit.org).
- [Model Optimizer Developer Guide](../MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md).
- [Inference Engine Developer Guide](../IE_DG/Deep_Learning_Inference_Engine_DevGuide.md).
- For more information on Sample Applications, see the [Inference Engine Samples Overview](../IE_DG/Samples_Overview.md).
- For information on Inference Engine Tutorials, see the [Inference Tutorials](https://github.com/intel-iot-devkit/inference-tutorials-generic).
- For IoT Libraries & Code Samples see the [Intel® IoT Developer Kit](https://github.com/intel-iot-devkit).

10
docs/install_guides/installing-openvino-conda.md
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@ -54,11 +54,11 @@ Now you can start to develop and run your application.
## Additional Resources
- Intel® Distribution of OpenVINO™ toolkit home page: [https://software.intel.com/en-us/openvino-toolkit](https://software.intel.com/en-us/openvino-toolkit)
- OpenVINO™ toolkit online documentation: [https://docs.openvinotoolkit.org](https://docs.openvinotoolkit.org)
- [Model Optimizer Developer Guide](https://docs.openvinotoolkit.org/latest/_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide.html).
- [Inference Engine Developer Guide](https://docs.openvinotoolkit.org/latest/_docs_IE_DG_Deep_Learning_Inference_Engine_DevGuide.html)
- For more information on Sample Applications, see the [Inference Engine Samples Overview](https://docs.openvinotoolkit.org/latest/_docs_IE_DG_Samples_Overview.html).
- Intel® Distribution of OpenVINO™ toolkit home page: [https://software.intel.com/en-us/openvino-toolkit](https://software.intel.com/en-us/openvino-toolkit).
- OpenVINO™ toolkit online documentation: [https://docs.openvinotoolkit.org](https://docs.openvinotoolkit.org).
- [Model Optimizer Developer Guide](../MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md).
- [Inference Engine Developer Guide](../IE_DG/Deep_Learning_Inference_Engine_DevGuide.md).
- For more information on Sample Applications, see the [Inference Engine Samples Overview](../IE_DG/Samples_Overview.md).
- For information on Inference Engine Tutorials, see the [Inference Tutorials](https://github.com/intel-iot-devkit/inference-tutorials-generic).
- Intel® Distribution of OpenVINO™ toolkit Anaconda* home page: [https://anaconda.org/intel/openvino-ie4py](https://anaconda.org/intel/openvino-ie4py)

4
docs/install_guides/installing-openvino-linux-fpga.md
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@ -336,8 +336,8 @@ trusted-host = mirrors.aliyun.com
- OpenVINO™ toolkit online documentation: [https://docs.openvinotoolkit.org](https://docs.openvinotoolkit.org)
- [Inference Engine FPGA plugin documentation](../IE_DG/supported_plugins/FPGA.md)
- [Model Optimizer Developer Guide](../MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md).
- For more information on Sample Applications, see the [Inference Engine Samples Overview](https://docs.openvinotoolkit.org/latest/_docs_IE_DG_Samples_Overview.html)
- To learn about pre-trained models for OpenVINO™ toolkit, see the [Pre-Trained Models Overview](https://docs.openvinotoolkit.org/latest/_docs_docs_Pre_Trained_Models.html)
- For more information on Sample Applications, see the [Inference Engine Samples Overview](../IE_DG/Samples_Overview.md).
- To learn about pre-trained models for OpenVINO™ toolkit, see the [Pre-Trained Models Overview](@ref omz_models_intel_index).
- For information on Inference Engine Tutorials, see the [Inference Tutorials](https://github.com/intel-iot-devkit/inference-tutorials-generic)
- For IoT Libraries & Code Samples see the [Intel® IoT Developer Kit](https://github.com/intel-iot-devkit).

6
docs/install_guides/installing-openvino-linux.md
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@ -458,9 +458,9 @@ trusted-host = mirrors.aliyun.com
- Intel® Distribution of OpenVINO™ toolkit home page: [https://software.intel.com/en-us/openvino-toolkit](https://software.intel.com/en-us/openvino-toolkit)
- OpenVINO™ toolkit online documentation: [https://docs.openvinotoolkit.org](https://docs.openvinotoolkit.org)
- [Model Optimizer Developer Guide](https://docs.openvinotoolkit.org/latest/_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide.html)
- [Inference Engine Developer Guide](https://docs.openvinotoolkit.org/latest/_docs_IE_DG_Deep_Learning_Inference_Engine_DevGuide.html)
- For more information on Sample Applications, see the [Inference Engine Samples Overview](https://docs.openvinotoolkit.org/latest/_docs_IE_DG_Samples_Overview.html)
- [Model Optimizer Developer Guide](../MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md).
- [Inference Engine Developer Guide](../IE_DG/Deep_Learning_Inference_Engine_DevGuide.md).
- For more information on Sample Applications, see the [Inference Engine Samples Overview](../IE_DG/Samples_Overview.md).
- For information on a set of pre-trained models, see the [Overview of OpenVINO™ Toolkit Pre-Trained Models](@ref omz_models_intel_index)
- For information on Inference Engine Tutorials, see the [Inference Tutorials](https://github.com/intel-iot-devkit/inference-tutorials-generic)
- For IoT Libraries and Code Samples see the [Intel® IoT Developer Kit](https://github.com/intel-iot-devkit).

12
docs/install_guides/installing-openvino-windows-fpga.md
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@ -436,13 +436,13 @@ Refer to the [OpenVINO™ with FPGA Hello World Face Detection Exercise](https:
**Additional Resources**
- Intel® Distribution of OpenVINO™ toolkit home page: [https://software.intel.com/en-us/openvino-toolkit](https://software.intel.com/en-us/openvino-toolkit)
- OpenVINO™ toolkit online documentation: [https://docs.openvinotoolkit.org](https://docs.openvinotoolkit.org)
- [Inference Engine FPGA plugin documentation](../IE_DG/supported_plugins/FPGA.md)
- Intel® Distribution of OpenVINO™ toolkit home page: [https://software.intel.com/en-us/openvino-toolkit](https://software.intel.com/en-us/openvino-toolkit).
- OpenVINO™ toolkit online documentation: [https://docs.openvinotoolkit.org](https://docs.openvinotoolkit.org).
- [Inference Engine FPGA plugin documentation](../IE_DG/supported_plugins/FPGA.md).
- [Model Optimizer Developer Guide](../MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md).
- For more information on Sample Applications, see the [Inference Engine Samples Overview](https://docs.openvinotoolkit.org/latest/_docs_IE_DG_Samples_Overview.html)
- To learn about pre-trained models for OpenVINO™ toolkit, see the [Pre-Trained Models Overview](https://docs.openvinotoolkit.org/latest/_docs_docs_Pre_Trained_Models.html)
- For information on Inference Engine Tutorials, see the [Inference Tutorials](https://github.com/intel-iot-devkit/inference-tutorials-generic)
- For more information on Sample Applications, see the [Inference Engine Samples Overview](../IE_DG/Samples_Overview.md).
- To learn about pre-trained models for OpenVINO™ toolkit, see the [Pre-Trained Models Overview](@ref omz_models_intel_index).
- For information on Inference Engine Tutorials, see the [Inference Tutorials](https://github.com/intel-iot-devkit/inference-tutorials-generic).
- For IoT Libraries & Code Samples see the [Intel® IoT Developer Kit](https://github.com/intel-iot-devkit).
To learn more about converting models, go to:

2
docs/install_guides/installing-openvino-windows.md
View File

@ -443,7 +443,7 @@ cd C:\Users\<username>\Documents\Intel\OpenVINO\inference_engine_samples_build\i
classification_sample_async.exe -i "C:\Program Files (x86)\IntelSWTools\openvino\deployment_tools\demo\car.png" -m "C:\Users\<username>\Documents\Intel\OpenVINO\openvino_models\ir\public\squeezenet1.1\FP16\squeezenet1.1.xml" -d HDDL
```
For information on Sample Applications, see the [Inference Engine Samples Overview](https://docs.openvinotoolkit.org/latest/_docs_IE_DG_Samples_Overview.html).
For information on Sample Applications, see the [Inference Engine Samples Overview](../IE_DG/Samples_Overview.md).
Congratulations, you have finished the installation of the Intel® Distribution of OpenVINO™ toolkit for Windows*. To learn more about how the Intel® Distribution of OpenVINO™ toolkit works, the Hello World tutorial and other resources are provided below.

6
docs/install_guides/installing-openvino-yum.md
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@ -103,9 +103,9 @@ sudo yum autoremove intel-openvino-runtime-centos<OS_VERSION>-<VERSION>.<UPDATE>
- Intel® Distribution of OpenVINO™ toolkit home page: [https://software.intel.com/en-us/openvino-toolkit](https://software.intel.com/en-us/openvino-toolkit)
- OpenVINO™ toolkit online documentation: [https://docs.openvinotoolkit.org](https://docs.openvinotoolkit.org)
- [Model Optimizer Developer Guide](https://docs.openvinotoolkit.org/latest/_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide.html).
- [Inference Engine Developer Guide](https://docs.openvinotoolkit.org/latest/_docs_IE_DG_Deep_Learning_Inference_Engine_DevGuide.html)
- For more information on Sample Applications, see the [Inference Engine Samples Overview](https://docs.openvinotoolkit.org/latest/_docs_IE_DG_Samples_Overview.html).
- [Model Optimizer Developer Guide](../MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md).
- [Inference Engine Developer Guide](../IE_DG/Deep_Learning_Inference_Engine_DevGuide.md).
- For more information on Sample Applications, see the [Inference Engine Samples Overview](../IE_DG/Samples_Overview.md).
- For information on Inference Engine Tutorials, see the [Inference Tutorials](https://github.com/intel-iot-devkit/inference-tutorials-generic).
- For IoT Libraries & Code Samples see the [Intel® IoT Developer Kit](https://github.com/intel-iot-devkit).