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diff --git a/inference-engine/samples/benchmark_app/README.md b/inference-engine/samples/benchmark_app/README.md index e3a125cb0..ab0bbd73c 100644 --- a/inference-engine/samples/benchmark_app/README.md +++ b/inference-engine/samples/benchmark_app/README.md @@ -1,10 +1,13 @@ # Benchmark Application Demo -This topic demonstrates how to run the Benchmark Application demo, which performs inference using convolutional networks. +This topic demonstrates how to use the Benchmark Application to estimate deep learning inference performance on supported devices. Performance can be measured for two inference modes: synchronous and asynchronous. + +> **NOTE:** This topic describes usage of C++ implementation of the Benchmark Application. For the Python* implementation, refer to [Benchmark Application (Python*)](./samples/python_samples/benchmark_app/README.md) + ## How It Works -**NOTE:** To achieve benchmark results similar to the official published results, set CPU frequency to 2.9GHz and GPU frequency to 1GHz. +> **NOTE:** To achieve benchmark results similar to the official published results, set CPU frequency to 2.9GHz and GPU frequency to 1GHz. Upon the start-up, the application reads command-line parameters and loads a network and images to the Inference Engine plugin. The number of infer requests and execution approach depend on a mode defined with the `-api` command-line parameter. @@ -56,15 +59,24 @@ Options: Running the application with the empty list of options yields the usage message given above and an error message. -To run the demo, you can use one-layer public models or one-layer pre-trained and optimized models delivered with the package that support images as input. +You can run the application for one input layer four-dimensional models that support images as input, for example, public +AlexNet and GoogLeNet models that can be downloaded +with the OpenVINO [Model Downloader](https://github.com/opencv/open_model_zoo/tree/2018/model_downloader). + +> **NOTE**: To run the application, the model should be first converted to the Inference Engine format (\*.xml + \*.bin) +using the [Model Optimizer tool](./docs/MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md). -For example, to do inference on an image using a trained network with multiple outputs on CPU, run the following command: +For example, to perform inference on CPU in the synchronous mode and get estimated performance metrics for AlexNet model, run the following command: + +```sh +./benchmark_app -i <path_to_image>/inputImage.bmp -m <path_to_model>/alexnet_fp32.xml -d CPU -api sync +``` +For the asynchronous mode: ```sh -./benchmark_app -i <path_to_image>/inputImage.bmp -m <path_to_model>/multiple-output.xml -d CPU +./benchmark_app -i <path_to_image>/inputImage.bmp -m <path_to_model>/alexnet_fp32.xml -d CPU -api async ``` -**NOTE**: Public models should be first converted to the Inference Engine format (\*.xml + \*.bin) using the [Model Optimizer tool](./docs/Model_Optimizer_Developer_Guide/Deep_Learning_Model_Optimizer_DevGuide.md). ## Demo Output @@ -84,4 +96,6 @@ For asynchronous API, the application outputs only throughput: ``` ## See Also -* [Using Inference Engine Samples](./docs/Inference_Engine_Developer_Guide/Samples_Overview.md) +* [Using Inference Engine Samples](./docs/IE_DG/Samples_Overview.md) +* [Model Optimizer tool](./docs/MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md) +* [Model Downloader](https://github.com/opencv/open_model_zoo/tree/2018/model_downloader) |