28 lines
2.5 KiB
Markdown
28 lines
2.5 KiB
Markdown
# Image Classification Async C++ Sample
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This sample demonstrates how to do inference of image classification models using Asynchronous Inference Request API.
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Models with only one input and output are supported.
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In addition to regular images, the sample also supports single-channel ``ubyte`` images as an input for LeNet model.
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For more detailed information on how this sample works, check the dedicated [article](https://docs.openvino.ai/2025/get-started/learn-openvino/openvino-samples/image-classification-async.html)
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## Requirements
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| Options | Values |
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| ---------------------------| -------------------------------------------------------------------------------------------------------------------------------------|
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| Model Format | OpenVINO™ toolkit Intermediate Representation (\*.xml + \*.bin), ONNX (\*.onnx) |
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| Supported devices | [All](https://docs.openvino.ai/2025/documentation/compatibility-and-support/supported-devices.html) |
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| Other language realization | [Python](https://docs.openvino.ai/2025/get-started/learn-openvino/openvino-samples/image-classification-async.html) |
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The following C++ API is used in the application:
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| Feature | API | Description |
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| -------------------------| ----------------------------------------------------------------------|----------------------------------------------------------------------------------------|
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| Asynchronous Infer | ``ov::InferRequest::start_async``, ``ov::InferRequest::set_callback`` | Do asynchronous inference with callback. |
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| Model Operations | ``ov::Output::get_shape``, ``ov::set_batch`` | Manage the model, operate with its batch size. Set batch size using input image count. |
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| Infer Request Operations | ``ov::InferRequest::get_input_tensor`` | Get an input tensor. |
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| Tensor Operations | ``ov::shape_size``, ``ov::Tensor::data`` | Get a tensor shape size and its data. |
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