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* Doc Migration from Gitlab (#1289) * doc migration * fix * Update FakeQuantize_1.md * Update performance_benchmarks.md * Updates graphs for FPGA * Update performance_benchmarks.md * Change DL Workbench structure (#1) * Changed DL Workbench structure * Fixed tags * fixes * Update ie_docs.xml * Update performance_benchmarks_faq.md * Fixes in DL Workbench layout * Fixes for CVS-31290 * [DL Workbench] Minor correction * Fix for CVS-30955 * Added nGraph deprecation notice as requested by Zoe * fix broken links in api doxy layouts * CVS-31131 fixes * Additional fixes * Fixed POT TOC * Update PAC_Configure.md PAC DCP 1.2.1 install guide. * Update inference_engine_intro.md * fix broken link * Update opset.md * fix * added opset4 to layout * added new opsets to layout, set labels for them * Update VisionAcceleratorFPGA_Configure.md Updated from 2020.3 to 2020.4 Co-authored-by: domi2000 <domi2000@users.noreply.github.com>
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Hello Classification C++ Sample
This topic describes how to run the Hello Infer Classification sample application. The sample is simplified version of Image Classification Sample Async and developed with support of UNICODE. It demonstrates how to use the following Inference Engine API in applications:
- Synchronous Infer Request API
- Input auto-resize API. It allows to set image of the original size as input for a network with other input size. Resize will be performed automatically by the corresponding plugin just before inference.
There is also an API introduced to crop a ROI object and set it as input without additional memory re-allocation. To properly demonstrate this API, it is required to run several networks in pipeline which is out of scope of this sample. Please refer to [Security Barrier Camera Demo](@ref omz_demos_security_barrier_camera_demo_README), or [Crossroad Camera Demo](@ref omz_demos_crossroad_camera_demo_README) with an example of using of new crop ROI API.
Refer to Integrate the Inference Engine New Request API with Your Application for details.
Note
: By default, Inference Engine samples and demos expect input with BGR channels order. If you trained your model to work with RGB order, you need to manually rearrange the default channels order in the sample or demo application or reconvert your model using the Model Optimizer tool with
--reverse_input_channelsargument specified. For more information about the argument, refer to When to Reverse Input Channels section of Converting a Model Using General Conversion Parameters.
Running
To run the sample, you can use public or pre-trained models. To download the pre-trained models, use the OpenVINO [Model Downloader](@ref omz_tools_downloader_README) or go to https://download.01.org/opencv/.
Note
: Before running the sample with a trained model, make sure the model is converted to the Inference Engine format (*.xml + *.bin) using the Model Optimizer tool.
You can do inference of an image using a trained AlexNet network on a GPU using the following command:
./hello_classification <path_to_model>/alexnet_fp32.xml <path_to_image>/cat.bmp GPU
Sample Output
The application outputs top-10 inference results.