# OpenVINO™

[OpenVINO™](https://docs.openvino.ai/) is the primary toolkit for optimizing and
deploying deep-learning inference in Robotics AI Suite applications. It supports
models from common frameworks and can target available Intel compute devices.

Use the current [OpenVINO™ installation documentation](https://docs.openvino.ai/canonical/get-started/install-openvino.html) for the selected environment.

## Software Solutions

OpenVINO™ software solutions cover object detection, segmentation, and
RealSense camera workflows.


::::{grid} 2

:::{grid-item-card} **Semantic Segmentation with RealSense**
:link: reference_applications/segmentation_realsense_tutorial
:link-type: doc
:link-alt: clickable cards

Run semantic segmentation on RealSense image data using OpenVINO™ inference.
:::

:::{grid-item-card} **Object Detection**
:link: reference_applications/object_detection_tutorial
:link-type: doc
:link-alt: clickable cards

Deploy object-detection workloads with ROS 2 camera inputs and OpenVINO™ acceleration.
:::

:::{grid-item-card} **OpenVINO™ Multi-Camera Demo**
:link: reference_applications/openvino_multicam_demo
:link-type: doc
:link-alt: clickable cards

Process multiple camera streams in a single OpenVINO™-powered demo pipeline.
:::

:::{grid-item-card} **YOLOv8 with OpenVINO™**
:link: reference_applications/yolov8_openvino_tutorial
:link-type: doc
:link-alt: clickable cards

Use a YOLOv8 model with OpenVINO™ for accelerated object detection on robotics systems.
:::

:::{grid-item-card} **OpenVINO™ Supported Models**
:link: models/index
:link-type: doc
:link-alt: clickable cards

Optimize and deploy perception, manipulation, and vision-language-action models with OpenVINO™.
:::

:::{grid-item-card} **Pi0.5 Model Optimization**
:link: pi05-optimization
:link-type: doc
:link-alt: clickable cards

Convert, compress, benchmark, and validate the Pi0.5 vision-language-action model.
:::
::::



:::{toctree}
:hidden:

Software Solutions <reference_applications/index>
models/index
pi05-optimization
OpenVINO™ Physical AI <openvino_physical_ai_runtime>
:::


## Additional Guidance

- [OpenVINO™ Supported Models](models/index.md) includes reusable perception,
  manipulation, and foundation-model guidance. The workflows require the
    [platform getting-started guide](../../platform_foundation/getting_started.md)
    when used with the Humanoid Toolkit.

## Benchmarking

Use the upstream [OpenVINO™ Benchmark Tool](https://docs.openvino.ai/canonical/get-started/learn-openvino/openvino-samples/benchmark-tool.html)
to estimate deep-learning inference throughput and latency on supported Intel
devices. Install OpenVINO™ and its samples with the [OpenVINO™ sample guidance](https://docs.openvino.ai/canonical/get-started/learn-openvino/openvino-samples/get-started-demos.html)
before benchmarking.

Use the same OpenVINO™ version to convert a model and to run inference unless the
model's documentation explicitly supports a different compatibility path.