From 687cb2746f86cf847eadd5672d6eeba83f9a3040 Mon Sep 17 00:00:00 2001 From: Olivia Jain Date: Thu, 29 Jul 2021 20:35:19 -0700 Subject: [PATCH] Update OpenVINO Sample Code Links (#8550) * 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 --- .../OpenVINO_EP_samples/squeezenet_classification_cpp.md | 4 ++-- .../tiny_yolo_v2_object_detection_python.md | 2 +- .../OpenVINO_EP_samples/yolov3_object_detection_csharp.md | 2 +- 3 files changed, 4 insertions(+), 4 deletions(-) diff --git a/docs/tutorials/tutorials/OpenVINO_EP_samples/squeezenet_classification_cpp.md b/docs/tutorials/tutorials/OpenVINO_EP_samples/squeezenet_classification_cpp.md index 3e8bdd536a..4591815182 100644 --- a/docs/tutorials/tutorials/OpenVINO_EP_samples/squeezenet_classification_cpp.md +++ b/docs/tutorials/tutorials/OpenVINO_EP_samples/squeezenet_classification_cpp.md @@ -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) -``` \ No newline at end of file +``` diff --git a/docs/tutorials/tutorials/OpenVINO_EP_samples/tiny_yolo_v2_object_detection_python.md b/docs/tutorials/tutorials/OpenVINO_EP_samples/tiny_yolo_v2_object_detection_python.md index 7497a69344..861f720341 100644 --- a/docs/tutorials/tutorials/OpenVINO_EP_samples/tiny_yolo_v2_object_detection_python.md +++ b/docs/tutorials/tutorials/OpenVINO_EP_samples/tiny_yolo_v2_object_detection_python.md @@ -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 diff --git a/docs/tutorials/tutorials/OpenVINO_EP_samples/yolov3_object_detection_csharp.md b/docs/tutorials/tutorials/OpenVINO_EP_samples/yolov3_object_detection_csharp.md index 41e5278f46..27df383eae 100644 --- a/docs/tutorials/tutorials/OpenVINO_EP_samples/yolov3_object_detection_csharp.md +++ b/docs/tutorials/tutorials/OpenVINO_EP_samples/yolov3_object_detection_csharp.md @@ -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® 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