Supported Models

The OpenVINO team continues the effort to support as many models out-of-the-box as possible. Based on our research and user feedback, we prioritize the most common models and test them before every release. These models are considered officially supported.

Click for supported models [PDF]

Note that the list provided here does not include all models supported by OpenVINO.
If your model is not included but is similar to those that are, it is still very likely to work. If your model fails to execute properly there are a few options available:

  • If the model originates from a framework like TensorFlow or PyTorch, OpenVINO™ offers a hybrid solution. The original model can be run without explicit conversion into the OpenVINO format (option available from OpenVINO 2023.0).

  • You can create a GitHub request for the operation(s) that are missing. These requests are reviewed regularly. You will be informed if and how the request will be accommodated. Additionally, your request may trigger a reply from someone in the community who can help.

  • As OpenVINO™ is open source you can enhance it with your own contribution to the GitHub repository. To learn more, see the articles on OpenVINO Extensibility.

The following table summarizes the number of models supported by OpenVINO™ in different categories:

Model Categories:

Number of Models:

Object Detection

149

Instance Segmentation

3

Semantic Segmentation

19

Image Processing, Enhancement

16

Monodepth

2

Colorization

2

Behavior / Decision Prediction

1

Action Recognition

2

Time Series Forecasting

1

Image Classification

68

Image Classification, Dual Path Network

1

Image Classification, Emotion

1

Image Translation

1

Natural language Processing

35

Text Detection

18

Audio Enhancement

3

Sound Classification

2