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Update OpenVINO Sample Code Links (#8550)

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* Update squeezenet_classification_cpp.md

* Update tiny_yolo_v2_object_detection_python.md

* Update yolov3_object_detection_csharp.md

* Update squeezenet_classification_cpp.md

* Update tiny_yolo_v2_object_detection_python.md

* Update yolov3_object_detection_csharp.md
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Olivia Jain 2021-07-29 20:35:19 -07:00 committed by GitHub
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docs/tutorials/tutorials/OpenVINO_EP_samples/squeezenet_classification_cpp.md
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@ -9,7 +9,7 @@ nav_exclude: true
2. The sample involves presenting an image to the ONNX Runtime (RT), which uses the OpenVINO Execution Provider for ONNX RT to run inference on various Intel hardware devices like Intel CPU, GPU, VPU and more. The sample uses OpenCV for image processing and ONNX Runtime OpenVINO EP for inference. After the sample image is inferred, the terminal will output the predicted label classes in order of their confidence.
The source code for this sample is available [here](https://github.com/microsoft/onnxruntime/tree/master/samples/c_cxx/OpenVINO_EP/squeezenet_classification).
The source code for this sample is available [here](https://github.com/microsoft/onnxruntime-inference-examples/tree/main/c_cxx/OpenVINO_EP/squeezenet_classification).
# How to build
@ -77,4 +77,4 @@ Example:
```
./run_squeezenet --use_cpu squeezenet1.1-7.onnx demo.jpeg synset.txt (using Default CPU)
```
```

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docs/tutorials/tutorials/OpenVINO_EP_samples/tiny_yolo_v2_object_detection_python.md
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@ -9,7 +9,7 @@ nav_exclude: true
2. The sample involves presenting a frame-by-frame video to ONNX Runtime (RT), which uses the OpenVINO Execution Provider to run inference on various Intel hardware devices as mentioned before and perform object detection to detect up to 20 different objects like birds, buses, cars, people and much more.
The source code for this sample is available [here](https://github.com/microsoft/onnxruntime/tree/master/samples/python/OpenVINO_EP/tiny_yolo_v2_object_detection).
The source code for this sample is available [here](https://github.com/microsoft/onnxruntime-inference-examples/tree/main/python/OpenVINO_EP/tiny_yolo_v2_object_detection).
# How to build

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docs/tutorials/tutorials/OpenVINO_EP_samples/yolov3_object_detection_csharp.md
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@ -9,7 +9,7 @@ nav_exclude: true
2. The sample involves presenting an image to the ONNX Runtime (RT), which uses the OpenVINO Execution Provider for ONNX RT to run inference on Intel<sup>®</sup> NCS2 stick (MYRIADX device). The sample uses ImageSharp for image processing and ONNX Runtime OpenVINO EP for inference.
The source code for this sample is available [here](https://github.com/microsoft/onnxruntime/tree/master/samples/c_sharp/OpenVINO_EP/yolov3_object_detection).
The source code for this sample is available [here](https://github.com/microsoft/onnxruntime-inference-examples/tree/main/c_sharp/OpenVINO_EP/yolov3_object_detection).
# How to build