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Merge IE & nGraph DG (#10055)

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* Changed folder for documentation

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* Merged nGraph DG to OpenVINO Runtime UG

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This commit is contained in:
Ilya Churaev
2022-02-07 06:57:35 +03:00
committed by GitHub
parent 788fb5c010
commit a2ca1d4499
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10
samples/c/hello_classification/README.md
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@@ -17,7 +17,7 @@ Hello Classification C sample application demonstrates how to use the following
| Validated Models | [alexnet](@ref omz_models_model_alexnet), [googlenet-v1](@ref omz_models_model_googlenet_v1)
| Model Format | Inference Engine Intermediate Representation (\*.xml + \*.bin), ONNX (\*.onnx)
| Validated images | The sample uses OpenCV\* to [read input image](https://docs.opencv.org/master/d4/da8/group__imgcodecs.html#ga288b8b3da0892bd651fce07b3bbd3a56) (\*.bmp, \*.png)
| Supported devices | [All](../../../docs/IE_DG/supported_plugins/Supported_Devices.md) |
| Supported devices | [All](../../../docs/OV_Runtime_UG/supported_plugins/Supported_Devices.md) |
| Other language realization | [C++](../../../samples/cpp/hello_classification/README.md), [Python](../../python/hello_classification/README.md) |
## How It Works
@@ -26,11 +26,11 @@ Upon the start-up, the sample application reads command line parameters, loads s
Then, the sample creates an synchronous inference request object. When inference is done, the application outputs data to the standard output stream.
You can see the explicit description of
each sample step at [Integration Steps](../../../docs/IE_DG/Integrate_with_customer_application_new_API.md) section of "Integrate the Inference Engine with Your Application" guide.
each sample step at [Integration Steps](../../../docs/OV_Runtime_UG/Integrate_with_customer_application_new_API.md) section of "Integrate the Inference Engine with Your Application" guide.
## Building
To build the sample, please use instructions available at [Build the Sample Applications](../../../docs/IE_DG/Samples_Overview.md) section in Inference Engine Samples guide.
To build the sample, please use instructions available at [Build the Sample Applications](../../../docs/OV_Runtime_UG/Samples_Overview.md) section in Inference Engine Samples guide.
## Running
@@ -92,8 +92,8 @@ This sample is an API example, for any performance measurements please use the d
## See Also
- [Integrate the Inference Engine with Your Application](../../../docs/IE_DG/Integrate_with_customer_application_new_API.md)
- [Using Inference Engine Samples](../../../docs/IE_DG/Samples_Overview.md)
- [Integrate the Inference Engine with Your Application](../../../docs/OV_Runtime_UG/Integrate_with_customer_application_new_API.md)
- [Using Inference Engine Samples](../../../docs/OV_Runtime_UG/Samples_Overview.md)
- [Model Downloader](@ref omz_tools_downloader)
- [Model Optimizer](../../../docs/MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md)

8
samples/c/hello_nv12_input_classification/README.md
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@@ -15,7 +15,7 @@ Basic Inference Engine API is covered by [Hello Classification C sample](../hell
| Validated Models | [alexnet](@ref omz_models_model_alexnet)
| Model Format | Inference Engine Intermediate Representation (\*.xml + \*.bin), ONNX (\*.onnx)
| Validated images | An uncompressed image in the NV12 color format - \*.yuv
| Supported devices | [All](../../../docs/IE_DG/supported_plugins/Supported_Devices.md) |
| Supported devices | [All](../../../docs/OV_Runtime_UG/supported_plugins/Supported_Devices.md) |
| Other language realization | [C++](../../../samples/cpp/hello_nv12_input_classification/README.md) |
## How It Works
@@ -29,7 +29,7 @@ each sample step at [Integration Steps](https://docs.openvino.ai/latest/openvino
## Building
To build the sample, please use instructions available at [Build the Sample Applications](../../../docs/IE_DG/Samples_Overview.md) section in Inference Engine Samples guide.
To build the sample, please use instructions available at [Build the Sample Applications](../../../docs/OV_Runtime_UG/Samples_Overview.md) section in Inference Engine Samples guide.
## Running
@@ -107,8 +107,8 @@ This sample is an API example, for any performance measurements please use the d
## See Also
- [Integrate the Inference Engine with Your Application](../../../docs/IE_DG/Integrate_with_customer_application_new_API.md)
- [Using Inference Engine Samples](../../../docs/IE_DG/Samples_Overview.md)
- [Integrate the Inference Engine with Your Application](../../../docs/OV_Runtime_UG/Integrate_with_customer_application_new_API.md)
- [Using Inference Engine Samples](../../../docs/OV_Runtime_UG/Samples_Overview.md)
- [Model Downloader](@ref omz_tools_downloader)
- [Model Optimizer](../../../docs/MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md)

4
samples/cpp/benchmark_app/README.md
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@@ -56,7 +56,7 @@ Note that the benchmark_app usually produces optimal performance for any device
But it is still may be sub-optimal for some cases, especially for very small networks. More details can read in [Performance Optimization Guide](../../../docs/optimization_guide/dldt_optimization_guide.md).
As explained in the [Performance Optimization Guide](../../../docs/optimization_guide/dldt_optimization_guide.md) section, for all devices, including new [MULTI device](../../../docs/IE_DG/supported_plugins/MULTI.md) it is preferable to use the FP16 IR for the model.
As explained in the [Performance Optimization Guide](../../../docs/optimization_guide/dldt_optimization_guide.md) section, for all devices, including new [MULTI device](../../../docs/OV_Runtime_UG/supported_plugins/MULTI.md) it is preferable to use the FP16 IR for the model.
Also if latency of the CPU inference on the multi-socket machines is of concern, please refer to the same
[Performance Optimization Guide](../../../docs/optimization_guide/dldt_optimization_guide.md).
@@ -243,6 +243,6 @@ Below are fragments of sample output static and dynamic networks:
```
## See Also
* [Using Inference Engine Samples](../../../docs/IE_DG/Samples_Overview.md)
* [Using Inference Engine Samples](../../../docs/OV_Runtime_UG/Samples_Overview.md)
* [Model Optimizer](../../../docs/MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md)
* [Model Downloader](@ref omz_tools_downloader)

10
samples/cpp/classification_sample_async/README.md
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@@ -21,7 +21,7 @@ Basic Inference Engine API is covered by [Hello Classification C++ sample](../he
| Validated Models | [alexnet](@ref omz_models_model_alexnet), [googlenet-v1](@ref omz_models_model_googlenet_v1)
| Model Format | Inference Engine Intermediate Representation (\*.xml + \*.bin), ONNX (\*.onnx)
| Validated images | The sample uses OpenCV\* to [read input image](https://docs.opencv.org/master/d4/da8/group__imgcodecs.html#ga288b8b3da0892bd651fce07b3bbd3a56) (\*.bmp, \*.png), single-channel `ubyte` images.
| Supported devices | [All](../../../docs/IE_DG/supported_plugins/Supported_Devices.md) |
| Supported devices | [All](../../../docs/OV_Runtime_UG/supported_plugins/Supported_Devices.md) |
| Other language realization | [Python](../../../samples/python/classification_sample_async/README.md) |
## How It Works
@@ -37,11 +37,11 @@ After that, the application starts inference for the first infer request and wai
When inference is done, the application outputs data to the standard output stream. You can place labels in .labels file near the model to get pretty output.
You can see the explicit description of
each sample step at [Integration Steps](../../../docs/IE_DG/Integrate_with_customer_application_new_API.md) section of "Integrate the Inference Engine with Your Application" guide.
each sample step at [Integration Steps](../../../docs/OV_Runtime_UG/Integrate_with_customer_application_new_API.md) section of "Integrate the Inference Engine with Your Application" guide.
## Building
To build the sample, please use instructions available at [Build the Sample Applications](../../../docs/IE_DG/Samples_Overview.md) section in Inference Engine Samples guide.
To build the sample, please use instructions available at [Build the Sample Applications](../../../docs/OV_Runtime_UG/Samples_Overview.md) section in Inference Engine Samples guide.
## Running
@@ -164,7 +164,7 @@ classid probability
## See Also
- [Integrate the Inference Engine with Your Application](../../../docs/IE_DG/Integrate_with_customer_application_new_API.md)
- [Using Inference Engine Samples](../../../docs/IE_DG/Samples_Overview.md)
- [Integrate the Inference Engine with Your Application](../../../docs/OV_Runtime_UG/Integrate_with_customer_application_new_API.md)
- [Using Inference Engine Samples](../../../docs/OV_Runtime_UG/Samples_Overview.md)
- [Model Downloader](@ref omz_tools_downloader)
- [Model Optimizer](../../../docs/MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md)

10
samples/cpp/hello_classification/README.md
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@@ -17,7 +17,7 @@ Hello Classification C++ sample application demonstrates how to use the followin
| Validated Models | [alexnet](@ref omz_models_model_alexnet), [googlenet-v1](@ref omz_models_model_googlenet_v1)
| Model Format | Inference Engine Intermediate Representation (\*.xml + \*.bin), ONNX (\*.onnx)
| Validated images | The sample uses OpenCV\* to [read input image](https://docs.opencv.org/master/d4/da8/group__imgcodecs.html#ga288b8b3da0892bd651fce07b3bbd3a56) (\*.bmp, \*.png)
| Supported devices | [All](../../../docs/IE_DG/supported_plugins/Supported_Devices.md) |
| Supported devices | [All](../../../docs/OV_Runtime_UG/supported_plugins/Supported_Devices.md) |
| Other language realization | [C](../../../samples/c/hello_classification/README.md), [Python](../../../samples/python/hello_classification/README.md) |
## How It Works
@@ -26,11 +26,11 @@ Upon the start-up, the sample application reads command line parameters, loads s
Then, the sample creates an synchronous inference request object. When inference is done, the application outputs data to the standard output stream.
You can see the explicit description of
each sample step at [Integration Steps](../../../docs/IE_DG/Integrate_with_customer_application_new_API.md) section of "Integrate the Inference Engine with Your Application" guide.
each sample step at [Integration Steps](../../../docs/OV_Runtime_UG/Integrate_with_customer_application_new_API.md) section of "Integrate the Inference Engine with Your Application" guide.
## Building
To build the sample, please use instructions available at [Build the Sample Applications](../../../docs/IE_DG/Samples_Overview.md) section in Inference Engine Samples guide.
To build the sample, please use instructions available at [Build the Sample Applications](../../../docs/OV_Runtime_UG/Samples_Overview.md) section in Inference Engine Samples guide.
## Running
@@ -92,7 +92,7 @@ This sample is an API example, for any performance measurements please use the d
## See Also
- [Integrate the Inference Engine with Your Application](../../../docs/IE_DG/Integrate_with_customer_application_new_API.md)
- [Using Inference Engine Samples](../../../docs/IE_DG/Samples_Overview.md)
- [Integrate the Inference Engine with Your Application](../../../docs/OV_Runtime_UG/Integrate_with_customer_application_new_API.md)
- [Using Inference Engine Samples](../../../docs/OV_Runtime_UG/Samples_Overview.md)
- [Model Downloader](@ref omz_tools_downloader)
- [Model Optimizer](../../../docs/MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md)

12
samples/cpp/hello_nv12_input_classification/README.md
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@@ -1,6 +1,6 @@
# Hello NV12 Input Classification C++ Sample {#openvino_inference_engine_samples_hello_nv12_input_classification_README}
This sample demonstrates how to execute an inference of image classification networks like AlexNet with images in NV12 color format using Synchronous Inference Request API and [input reshape feature](../../../docs/IE_DG/ShapeInference.md).
This sample demonstrates how to execute an inference of image classification networks like AlexNet with images in NV12 color format using Synchronous Inference Request API and [input reshape feature](../../../docs/OV_Runtime_UG/ShapeInference.md).
Hello NV12 Input Classification C++ Sample demonstrates how to use the NV12 automatic input pre-processing API of the Inference Engine in your applications:
@@ -18,7 +18,7 @@ Basic Inference Engine API is covered by [Hello Classification C++ sample](../he
| Validated Models | [alexnet](@ref omz_models_model_alexnet)
| Model Format | Inference Engine Intermediate Representation (\*.xml + \*.bin), ONNX (\*.onnx)
| Validated images | An uncompressed image in the NV12 color format - \*.yuv
| Supported devices | [All](../../../docs/IE_DG/supported_plugins/Supported_Devices.md) |
| Supported devices | [All](../../../docs/OV_Runtime_UG/supported_plugins/Supported_Devices.md) |
| Other language realization | [C](../../../samples/c/hello_nv12_input_classification/README.md) |
## How It Works
@@ -26,11 +26,11 @@ Basic Inference Engine API is covered by [Hello Classification C++ sample](../he
Upon the start-up, the sample application reads command-line parameters, loads specified network and an
image in the NV12 color format to an Inference Engine plugin. Then, the sample creates an synchronous inference request object. When inference is done, the application outputs data to the standard output stream. You can place labels in .labels file near the model to get pretty output.
You can see the explicit description of each sample step at [Integration Steps](../../../docs/IE_DG/Integrate_with_customer_application_new_API.md) section of "Integrate the Inference Engine with Your Application" guide.
You can see the explicit description of each sample step at [Integration Steps](../../../docs/OV_Runtime_UG/Integrate_with_customer_application_new_API.md) section of "Integrate the Inference Engine with Your Application" guide.
## Building
To build the sample, please use instructions available at [Build the Sample Applications](../../../docs/IE_DG/Samples_Overview.md) section in Inference Engine Samples guide.
To build the sample, please use instructions available at [Build the Sample Applications](../../../docs/OV_Runtime_UG/Samples_Overview.md) section in Inference Engine Samples guide.
## Running
@@ -113,7 +113,7 @@ This sample is an API example, for any performance measurements please use the d
## See Also
- [Integrate the Inference Engine with Your Application](../../../docs/IE_DG/Integrate_with_customer_application_new_API.md)
- [Using Inference Engine Samples](../../../docs/IE_DG/Samples_Overview.md)
- [Integrate the Inference Engine with Your Application](../../../docs/OV_Runtime_UG/Integrate_with_customer_application_new_API.md)
- [Using Inference Engine Samples](../../../docs/OV_Runtime_UG/Samples_Overview.md)
- [Model Downloader](@ref omz_tools_downloader)
- [Model Optimizer](../../../docs/MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md)

10
samples/cpp/hello_query_device/README.md
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@@ -1,6 +1,6 @@
# Hello Query Device C++ Sample {#openvino_inference_engine_samples_hello_query_device_README}
This sample demonstrates how to execute an query Inference Engine devices, prints their metrics and default configuration values, using [Query Device API feature](../../../docs/IE_DG/InferenceEngine_QueryAPI.md).
This sample demonstrates how to execute an query Inference Engine devices, prints their metrics and default configuration values, using [Query Device API feature](../../../docs/OV_Runtime_UG/InferenceEngine_QueryAPI.md).
Hello Query Device C++ sample application demonstrates how to use the following Inference Engine C++ API in applications:
@@ -12,7 +12,7 @@ Basic Inference Engine API is covered by [Hello Classification C++ sample](../he
| Options | Values |
|:--- |:---
| Supported devices | [All](../../../docs/IE_DG/supported_plugins/Supported_Devices.md) |
| Supported devices | [All](../../../docs/OV_Runtime_UG/supported_plugins/Supported_Devices.md) |
| Other language realization | [Python](../../../samples/python/hello_query_device/README.md) |
## How It Works
@@ -21,7 +21,7 @@ The sample queries all available Inference Engine devices, prints their supporte
## Building
To build the sample, please use instructions available at [Build the Sample Applications](../../../docs/IE_DG/Samples_Overview.md) section in Inference Engine Samples guide.
To build the sample, please use instructions available at [Build the Sample Applications](../../../docs/OV_Runtime_UG/Samples_Overview.md) section in Inference Engine Samples guide.
## Running
@@ -89,5 +89,5 @@ Available devices:
## See Also
- [Integrate the Inference Engine with Your Application](../../../docs/IE_DG/Integrate_with_customer_application_new_API.md)
- [Using Inference Engine Samples](../../../docs/IE_DG/Samples_Overview.md)
- [Integrate the Inference Engine with Your Application](../../../docs/OV_Runtime_UG/Integrate_with_customer_application_new_API.md)
- [Using Inference Engine Samples](../../../docs/OV_Runtime_UG/Samples_Overview.md)

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samples/cpp/hello_reshape_ssd/README.md
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@@ -1,6 +1,6 @@
# Hello Reshape SSD C++ Sample {#openvino_inference_engine_samples_hello_reshape_ssd_README}
This sample demonstrates how to execute an inference of object detection networks like SSD-VGG using Synchronous Inference Request API, [input reshape feature](../../../docs/IE_DG/ShapeInference.md).
This sample demonstrates how to execute an inference of object detection networks like SSD-VGG using Synchronous Inference Request API, [input reshape feature](../../../docs/OV_Runtime_UG/ShapeInference.md).
Hello Reshape SSD C++ sample application demonstrates how to use the following Inference Engine C++ API in applications:
@@ -17,7 +17,7 @@ Basic Inference Engine API is covered by [Hello Classification C++ sample](../he
| Validated Models | [person-detection-retail-0013](@ref omz_models_model_person_detection_retail_0013)
| Model Format | Inference Engine Intermediate Representation (\*.xml + \*.bin), ONNX (\*.onnx)
| Validated images | The sample uses OpenCV\* to [read input image](https://docs.opencv.org/master/d4/da8/group__imgcodecs.html#ga288b8b3da0892bd651fce07b3bbd3a56) (\*.bmp, \*.png)
| Supported devices | [All](../../../docs/IE_DG/supported_plugins/Supported_Devices.md) |
| Supported devices | [All](../../../docs/OV_Runtime_UG/supported_plugins/Supported_Devices.md) |
| Other language realization | [Python](../../../samples/python/hello_reshape_ssd/README.md) |
## How It Works
@@ -26,11 +26,11 @@ Upon the start-up the sample application reads command line parameters, loads sp
Engine plugin. Then, the sample creates an synchronous inference request object. When inference is done, the application creates output image and output data to the standard output stream.
You can see the explicit description of
each sample step at [Integration Steps](../../../docs/IE_DG/Integrate_with_customer_application_new_API.md) section of "Integrate the Inference Engine with Your Application" guide.
each sample step at [Integration Steps](../../../docs/OV_Runtime_UG/Integrate_with_customer_application_new_API.md) section of "Integrate the Inference Engine with Your Application" guide.
## Building
To build the sample, please use instructions available at [Build the Sample Applications](../../../docs/IE_DG/Samples_Overview.md) section in Inference Engine Samples guide.
To build the sample, please use instructions available at [Build the Sample Applications](../../../docs/OV_Runtime_UG/Samples_Overview.md) section in Inference Engine Samples guide.
## Running
@@ -104,7 +104,7 @@ The resulting image was saved in the file: hello_reshape_ssd_batch_0.bmp
## See Also
- [Integrate the Inference Engine with Your Application](../../../docs/IE_DG/Integrate_with_customer_application_new_API.md)
- [Using Inference Engine Samples](../../../docs/IE_DG/Samples_Overview.md)
- [Integrate the Inference Engine with Your Application](../../../docs/OV_Runtime_UG/Integrate_with_customer_application_new_API.md)
- [Using Inference Engine Samples](../../../docs/OV_Runtime_UG/Samples_Overview.md)
- [Model Downloader](@ref omz_tools_downloader)
- [Model Optimizer](../../../docs/MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md)

14
samples/cpp/ngraph_function_creation_sample/README.md
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@@ -1,6 +1,6 @@
# nGraph Function Creation C++ Sample {#openvino_inference_engine_samples_ngraph_function_creation_sample_README}
This sample demonstrates how to execute an synchronous inference using [nGraph function feature](../../../docs/nGraph_DG/build_function.md) to create a network, which uses weights from LeNet classification network, which is known to work well on digit classification tasks.
This sample demonstrates how to execute an synchronous inference using [OpenVINO Model feature](../../../docs/OV_Runtime_UG/model_representation.md) to create a network, which uses weights from LeNet classification network, which is known to work well on digit classification tasks.
The sample supports only single-channel [MNIST database](http://yann.lecun.com/exdb/mnist) images as an input.
@@ -22,18 +22,18 @@ Basic Inference Engine API is covered by [Hello Classification C++ sample](../he
| Validated Models | LeNet
| Model Format | Network weights file (\*.bin)
| Validated images | single-channel `MNIST ubyte` images
| Supported devices | [All](../../../docs/IE_DG/supported_plugins/Supported_Devices.md) |
| Supported devices | [All](../../../docs/OV_Runtime_UG/supported_plugins/Supported_Devices.md) |
| Other language realization | [Python](../../../samples/python/ngraph_function_creation_sample/README.md) |
## How It Works
At startup, the sample application reads command-line parameters, prepares input data, creates a network using the [nGraph function feature](../../../docs/nGraph_DG/build_function.md) and passed weights file, loads the network and image(s) to the Inference Engine plugin, performs synchronous inference and processes output data, logging each step in a standard output stream. You can place labels in .labels file near the model to get pretty output.
At startup, the sample application reads command-line parameters, prepares input data, creates a network using the [OpenVINO Model feature](../../../docs/OV_Runtime_UG/model_representation.md) and passed weights file, loads the network and image(s) to the Inference Engine plugin, performs synchronous inference and processes output data, logging each step in a standard output stream. You can place labels in .labels file near the model to get pretty output.
You can see the explicit description of each sample step at [Integration Steps](../../../docs/IE_DG/Integrate_with_customer_application_new_API.md) section of "Integrate the Inference Engine with Your Application" guide.
You can see the explicit description of each sample step at [Integration Steps](../../../docs/OV_Runtime_UG/Integrate_with_customer_application_new_API.md) section of "Integrate the Inference Engine with Your Application" guide.
## Building
To build the sample, please use instructions available at [Build the Sample Applications](../../../docs/IE_DG/Samples_Overview.md) section in Inference Engine Samples guide.
To build the sample, please use instructions available at [Build the Sample Applications](../../../docs/OV_Runtime_UG/Samples_Overview.md) section in Inference Engine Samples guide.
## Running
@@ -146,6 +146,6 @@ classid probability
## See Also
- [Integrate the Inference Engine with Your Application](../../../docs/IE_DG/Integrate_with_customer_application_new_API.md)
- [Using Inference Engine Samples](../../../docs/IE_DG/Samples_Overview.md)
- [Integrate the Inference Engine with Your Application](../../../docs/OV_Runtime_UG/Integrate_with_customer_application_new_API.md)
- [Using Inference Engine Samples](../../../docs/OV_Runtime_UG/Samples_Overview.md)
- [Model Optimizer](../../../docs/MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md)

10
samples/cpp/speech_sample/README.md
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@@ -23,7 +23,7 @@ Basic Inference Engine API is covered by [Hello Classification C++ sample](../he
|:--- |:---
| Validated Models | Acoustic model based on Kaldi\* neural networks (see [Model Preparation](#model-preparation) section)
| Model Format | Inference Engine Intermediate Representation (\*.xml + \*.bin)
| Supported devices | See [Execution Modes](#execution-modes) section below and [List Supported Devices](../../../docs/IE_DG/supported_plugins/Supported_Devices.md) |
| Supported devices | See [Execution Modes](#execution-modes) section below and [List Supported Devices](../../../docs/OV_Runtime_UG/supported_plugins/Supported_Devices.md) |
## How It Works
@@ -32,7 +32,7 @@ frames according to the `-bs` parameter. Batching across utterances is not supp
statistics are provided for each speech utterance as shown above.
You can see the explicit description of
each sample step at [Integration Steps](../../../docs/IE_DG/Integrate_with_customer_application_new_API.md) section of "Integrate the Inference Engine with Your Application" guide.
each sample step at [Integration Steps](../../../docs/OV_Runtime_UG/Integrate_with_customer_application_new_API.md) section of "Integrate the Inference Engine with Your Application" guide.
### GNA-specific details
@@ -80,7 +80,7 @@ In addition to performing inference directly from a GNA model file, these combin
## Building
To build the sample, please use instructions available at [Build the Sample Applications](../../../docs/IE_DG/Samples_Overview.md) section in Inference Engine Samples guide.
To build the sample, please use instructions available at [Build the Sample Applications](../../../docs/OV_Runtime_UG/Samples_Overview.md) section in Inference Engine Samples guide.
## Running
@@ -241,7 +241,7 @@ All of mentioned files can be downloaded from [https://storage.openvinotoolkit.o
## See Also
- [Integrate the Inference Engine with Your Application](../../../docs/IE_DG/Integrate_with_customer_application_new_API.md)
- [Using Inference Engine Samples](../../../docs/IE_DG/Samples_Overview.md)
- [Integrate the Inference Engine with Your Application](../../../docs/OV_Runtime_UG/Integrate_with_customer_application_new_API.md)
- [Using Inference Engine Samples](../../../docs/OV_Runtime_UG/Samples_Overview.md)
- [Model Downloader](@ref omz_tools_downloader)
- [Model Optimizer](../../../docs/MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md)

8
samples/python/classification_sample_async/README.md
View File

@@ -16,7 +16,7 @@ Basic Inference Engine API is covered by [Hello Classification Python* Sample](.
| :------------------------- | :-------------------------------------------------------------------------------------------------------- |
| Validated Models | [alexnet](@ref omz_models_model_alexnet) |
| Model Format | Inference Engine Intermediate Representation (.xml + .bin), ONNX (.onnx) |
| Supported devices | [All](../../../docs/IE_DG/supported_plugins/Supported_Devices.md) |
| Supported devices | [All](../../../docs/OV_Runtime_UG/supported_plugins/Supported_Devices.md) |
| Other language realization | [C++](../../../samples/cpp/classification_sample_async/README.md) |
## How It Works
@@ -24,7 +24,7 @@ Basic Inference Engine API is covered by [Hello Classification Python* Sample](.
At startup, the sample application reads command-line parameters, prepares input data, loads a specified model and image(s) to the Inference Engine plugin, performs synchronous inference, and processes output data, logging each step in a standard output stream.
You can see the explicit description of
each sample step at [Integration Steps](../../../docs/IE_DG/Integrate_with_customer_application_new_API.md) section of "Integrate the Inference Engine with Your Application" guide.
each sample step at [Integration Steps](../../../docs/OV_Runtime_UG/Integrate_with_customer_application_new_API.md) section of "Integrate the Inference Engine with Your Application" guide.
## Running
@@ -146,8 +146,8 @@ The sample application logs each step in a standard output stream and outputs to
## See Also
- [Integrate the Inference Engine with Your Application](../../../docs/IE_DG/Integrate_with_customer_application_new_API.md)
- [Using Inference Engine Samples](../../../docs/IE_DG/Samples_Overview.md)
- [Integrate the Inference Engine with Your Application](../../../docs/OV_Runtime_UG/Integrate_with_customer_application_new_API.md)
- [Using Inference Engine Samples](../../../docs/OV_Runtime_UG/Samples_Overview.md)
- [Model Downloader](@ref omz_tools_downloader)
- [Model Optimizer](../../../docs/MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md)

8
samples/python/hello_classification/README.md
View File

@@ -15,7 +15,7 @@ The following Inference Engine Python API is used in the application:
| :------------------------- | :-------------------------------------------------------------------------------------------------------- |
| Validated Models | [alexnet](@ref omz_models_model_alexnet), [googlenet-v1](@ref omz_models_model_googlenet_v1) |
| Model Format | Inference Engine Intermediate Representation (.xml + .bin), ONNX (.onnx) |
| Supported devices | [All](../../../docs/IE_DG/supported_plugins/Supported_Devices.md) |
| Supported devices | [All](../../../docs/OV_Runtime_UG/supported_plugins/Supported_Devices.md) |
| Other language realization | [C++](../../../samples/cpp/hello_classification/README.md), [C](../../c/hello_classification/README.md) |
## How It Works
@@ -23,7 +23,7 @@ The following Inference Engine Python API is used in the application:
At startup, the sample application reads command-line parameters, prepares input data, loads a specified model and image to the Inference Engine plugin, performs synchronous inference, and processes output data, logging each step in a standard output stream.
You can see the explicit description of
each sample step at [Integration Steps](../../../docs/IE_DG/Integrate_with_customer_application_new_API.md) section of "Integrate the Inference Engine with Your Application" guide.
each sample step at [Integration Steps](../../../docs/OV_Runtime_UG/Integrate_with_customer_application_new_API.md) section of "Integrate the Inference Engine with Your Application" guide.
## Running
@@ -117,8 +117,8 @@ The sample application logs each step in a standard output stream and outputs to
## See Also
- [Integrate the Inference Engine with Your Application](../../../docs/IE_DG/Integrate_with_customer_application_new_API.md)
- [Using Inference Engine Samples](../../../docs/IE_DG/Samples_Overview.md)
- [Integrate the Inference Engine with Your Application](../../../docs/OV_Runtime_UG/Integrate_with_customer_application_new_API.md)
- [Using Inference Engine Samples](../../../docs/OV_Runtime_UG/Samples_Overview.md)
- [Model Downloader](@ref omz_tools_downloader)
- [Model Optimizer](../../../docs/MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md)

6
samples/python/hello_query_device/README.md
View File

@@ -1,6 +1,6 @@
# Hello Query Device Python* Sample {#openvino_inference_engine_ie_bridges_python_sample_hello_query_device_README}
This sample demonstrates how to show Inference Engine devices and prints their metrics and default configuration values using [Query Device API feature](../../../docs/IE_DG/InferenceEngine_QueryAPI.md).
This sample demonstrates how to show Inference Engine devices and prints their metrics and default configuration values using [Query Device API feature](../../../docs/OV_Runtime_UG/InferenceEngine_QueryAPI.md).
The following Inference Engine Python API is used in the application:
@@ -11,7 +11,7 @@ The following Inference Engine Python API is used in the application:
| Options | Values |
| :------------------------- | :---------------------------------------------------------------------- |
| Supported devices | [All](../../../docs/IE_DG/supported_plugins/Supported_Devices.md) |
| Supported devices | [All](../../../docs/OV_Runtime_UG/supported_plugins/Supported_Devices.md) |
| Other language realization | [C++](../../../samples/cpp/hello_query_device/README.md) |
## How It Works
@@ -103,7 +103,7 @@ The application prints all available devices with their supported metrics and de
```
## See Also
- [Using Inference Engine Samples](../../../docs/IE_DG/Samples_Overview.md)
- [Using Inference Engine Samples](../../../docs/OV_Runtime_UG/Samples_Overview.md)
[IECore]:https://docs.openvino.ai/latest/ie_python_api/classie__api_1_1IECore.html
[IECore.get_metric]:https://docs.openvino.ai/latest/ie_python_api/classie__api_1_1IECore.html#af1cdf2ecbea6399c556957c2c2fdf8eb

10
samples/python/hello_reshape_ssd/README.md
View File

@@ -1,6 +1,6 @@
# Hello Reshape SSD Python* Sample {#openvino_inference_engine_ie_bridges_python_sample_hello_reshape_ssd_README}
This sample demonstrates how to do synchronous inference of object detection networks using [Shape Inference feature](../../../docs/IE_DG/ShapeInference.md).
This sample demonstrates how to do synchronous inference of object detection networks using [Shape Inference feature](../../../docs/OV_Runtime_UG/ShapeInference.md).
Models with only 1 input and output are supported.
The following Inference Engine Python API is used in the application:
@@ -16,7 +16,7 @@ Basic Inference Engine API is covered by [Hello Classification Python* Sample](.
| :------------------------- | :-------------------------------------------------------------------------------------------------------------------------- |
| Validated Models | [mobilenet-ssd](@ref omz_models_model_mobilenet_ssd) |
| Model Format | Inference Engine Intermediate Representation (.xml + .bin), ONNX (.onnx) |
| Supported devices | [All](../../../docs/IE_DG/supported_plugins/Supported_Devices.md) |
| Supported devices | [All](../../../docs/OV_Runtime_UG/supported_plugins/Supported_Devices.md) |
| Other language realization | [C++](../../../samples/cpp/hello_reshape_ssd/README.md) |
## How It Works
@@ -25,7 +25,7 @@ At startup, the sample application reads command-line parameters, prepares input
As a result, the program creates an output image, logging each step in a standard output stream.
You can see the explicit description of
each sample step at [Integration Steps](../../../docs/IE_DG/Integrate_with_customer_application_new_API.md) section of "Integrate the Inference Engine with Your Application" guide.
each sample step at [Integration Steps](../../../docs/OV_Runtime_UG/Integrate_with_customer_application_new_API.md) section of "Integrate the Inference Engine with Your Application" guide.
## Running
@@ -114,8 +114,8 @@ The sample application logs each step in a standard output stream and creates an
## See Also
- [Integrate the Inference Engine with Your Application](../../../docs/IE_DG/Integrate_with_customer_application_new_API.md)
- [Using Inference Engine Samples](../../../docs/IE_DG/Samples_Overview.md)
- [Integrate the Inference Engine with Your Application](../../../docs/OV_Runtime_UG/Integrate_with_customer_application_new_API.md)
- [Using Inference Engine Samples](../../../docs/OV_Runtime_UG/Samples_Overview.md)
- [Model Downloader](@ref omz_tools_downloader)
- [Model Optimizer](../../../docs/MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md)

12
samples/python/ngraph_function_creation_sample/README.md
View File

@@ -1,6 +1,6 @@
# nGraph Function Creation Python* Sample {#openvino_inference_engine_ie_bridges_python_sample_ngraph_function_creation_sample_README}
This sample demonstrates how to execute an inference using [nGraph function feature](../../../docs/nGraph_DG/build_function.md) to create a network that uses weights from LeNet classification network, which is known to work well on digit classification tasks. So you don't need an XML file, the model will be created from the source code on the fly.
This sample demonstrates how to execute an inference using [OpenVINO Model feature](../../../docs/OV_Runtime_UG/model_representation.md) to create a network that uses weights from LeNet classification network, which is known to work well on digit classification tasks. So you don't need an XML file, the model will be created from the source code on the fly.
In addition to regular grayscale images with a digit, the sample also supports single-channel `ubyte` images as an input.
@@ -18,15 +18,15 @@ Basic Inference Engine API is covered by [Hello Classification Python* Sample](.
| Validated Models | LeNet |
| Model Format | Network weights file (\*.bin) |
| Validated images | The sample uses OpenCV\* to [read input grayscale image](https://docs.opencv.org/master/d4/da8/group__imgcodecs.html#ga288b8b3da0892bd651fce07b3bbd3a56) (\*.bmp, \*.png) or single-channel `ubyte` image |
| Supported devices | [All](../../../docs/IE_DG/supported_plugins/Supported_Devices.md) |
| Supported devices | [All](../../../docs/OV_Runtime_UG/supported_plugins/Supported_Devices.md) |
| Other language realization | [C++](../../../samples/cpp/ngraph_function_creation_sample/README.md) |
## How It Works
At startup, the sample application reads command-line parameters, prepares input data, creates a network using [nGraph function feature](../../../docs/nGraph_DG/build_function.md) and passed weights file, loads the network and image(s) to the Inference Engine plugin, performs synchronous inference, and processes output data, logging each step in a standard output stream.
At startup, the sample application reads command-line parameters, prepares input data, creates a network using [OpenVINO Model feature](../../../docs/OV_Runtime_UG/model_representation.md) and passed weights file, loads the network and image(s) to the Inference Engine plugin, performs synchronous inference, and processes output data, logging each step in a standard output stream.
You can see the explicit description of
each sample step at [Integration Steps](../../../docs/IE_DG/Integrate_with_customer_application_new_API.md) section of "Integrate the Inference Engine with Your Application" guide.
each sample step at [Integration Steps](../../../docs/OV_Runtime_UG/Integrate_with_customer_application_new_API.md) section of "Integrate the Inference Engine with Your Application" guide.
## Running
@@ -109,8 +109,8 @@ The sample application logs each step in a standard output stream and outputs to
## See Also
- [Integrate the Inference Engine with Your Application](../../../docs/IE_DG/Integrate_with_customer_application_new_API.md)
- [Using Inference Engine Samples](../../../docs/IE_DG/Samples_Overview.md)
- [Integrate the Inference Engine with Your Application](../../../docs/OV_Runtime_UG/Integrate_with_customer_application_new_API.md)
- [Using Inference Engine Samples](../../../docs/OV_Runtime_UG/Samples_Overview.md)
- [Model Downloader](@ref omz_tools_downloader)
- [Model Optimizer](../../../docs/MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md)

8
samples/python/speech_sample/README.md
View File

@@ -19,7 +19,7 @@ Basic Inference Engine API is covered by [Hello Classification Python* Sample](.
| :------------------------- | :---------------------------------------------------------------------------------------------------- |
| Validated Models | Acoustic model based on Kaldi* neural networks (see [Model Preparation](#model-preparation) section) |
| Model Format | Inference Engine Intermediate Representation (.xml + .bin) |
| Supported devices | See [Execution Modes](#execution-modes) section below and [List Supported Devices](../../../docs/IE_DG/supported_plugins/Supported_Devices.md) |
| Supported devices | See [Execution Modes](#execution-modes) section below and [List Supported Devices](../../../docs/OV_Runtime_UG/supported_plugins/Supported_Devices.md) |
| Other language realization | [C++](../../../samples/cpp/speech_sample/README.md) |
## How It Works
@@ -27,7 +27,7 @@ Basic Inference Engine API is covered by [Hello Classification Python* Sample](.
At startup, the sample application reads command-line parameters, loads a specified model and input data to the Inference Engine plugin, performs synchronous inference on all speech utterances stored in the input file, logging each step in a standard output stream.
You can see the explicit description of
each sample step at [Integration Steps](../../../docs/IE_DG/Integrate_with_customer_application_new_API.md) section of "Integrate the Inference Engine with Your Application" guide.
each sample step at [Integration Steps](../../../docs/OV_Runtime_UG/Integrate_with_customer_application_new_API.md) section of "Integrate the Inference Engine with Your Application" guide.
## GNA-specific details
@@ -238,8 +238,8 @@ The sample application logs each step in a standard output stream.
## See Also
- [Integrate the Inference Engine with Your Application](../../../docs/IE_DG/Integrate_with_customer_application_new_API.md)
- [Using Inference Engine Samples](../../../docs/IE_DG/Samples_Overview.md)
- [Integrate the Inference Engine with Your Application](../../../docs/OV_Runtime_UG/Integrate_with_customer_application_new_API.md)
- [Using Inference Engine Samples](../../../docs/OV_Runtime_UG/Samples_Overview.md)
- [Model Downloader](@ref omz_tools_downloader)
- [Model Optimizer](../../../docs/MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md)