ultralytics/docs/en/integrations/openvino.md

comments	description	keywords
true	Learn to export YOLOv8 models to OpenVINO format for up to 3x CPU speedup and hardware acceleration on Intel GPU and NPU.	YOLOv8, OpenVINO, model export, Intel, AI inference, CPU speedup, GPU acceleration, NPU, deep learning

Intel OpenVINO Export

In this guide, we cover exporting YOLOv8 models to the OpenVINO format, which can provide up to 3x CPU speedup, as well as accelerating YOLO inference on Intel GPU and NPU hardware.

OpenVINO, short for Open Visual Inference & Neural Network Optimization toolkit, is a comprehensive toolkit for optimizing and deploying AI inference models. Even though the name contains Visual, OpenVINO also supports various additional tasks including language, audio, time series, etc.

Watch: How To Export and Optimize an Ultralytics YOLOv8 Model for Inference with OpenVINO.

Usage Examples

Export a YOLOv8n model to OpenVINO format and run inference with the exported model.

!!! Example

=== "Python"

    ```python
    from ultralytics import YOLO

    # Load a YOLOv8n PyTorch model
    model = YOLO("yolov8n.pt")

    # Export the model
    model.export(format="openvino")  # creates 'yolov8n_openvino_model/'

    # Load the exported OpenVINO model
    ov_model = YOLO("yolov8n_openvino_model/")

    # Run inference
    results = ov_model("https://ultralytics.com/images/bus.jpg")
    ```

=== "CLI"

    ```bash
    # Export a YOLOv8n PyTorch model to OpenVINO format
    yolo export model=yolov8n.pt format=openvino  # creates 'yolov8n_openvino_model/'

    # Run inference with the exported model
    yolo predict model=yolov8n_openvino_model source='https://ultralytics.com/images/bus.jpg'
    ```

Arguments

Key	Value	Description
format	'openvino'	format to export to
imgsz	640	image size as scalar or (h, w) list, i.e. (640, 480)
half	False	FP16 quantization
int8	False	INT8 quantization
batch	1	batch size for inference
dynamic	False	allows dynamic input sizes

Benefits of OpenVINO

1. Performance: OpenVINO delivers high-performance inference by utilizing the power of Intel CPUs, integrated and discrete GPUs, and FPGAs.
2. Support for Heterogeneous Execution: OpenVINO provides an API to write once and deploy on any supported Intel hardware (CPU, GPU, FPGA, VPU, etc.).
3. Model Optimizer: OpenVINO provides a Model Optimizer that imports, converts, and optimizes models from popular deep learning frameworks such as PyTorch, TensorFlow, TensorFlow Lite, Keras, ONNX, PaddlePaddle, and Caffe.
4. Ease of Use: The toolkit comes with more than 80 tutorial notebooks (including YOLOv8 optimization) teaching different aspects of the toolkit.

OpenVINO Export Structure

When you export a model to OpenVINO format, it results in a directory containing the following:

1. XML file: Describes the network topology.
2. BIN file: Contains the weights and biases binary data.
3. Mapping file: Holds mapping of original model output tensors to OpenVINO tensor names.

You can use these files to run inference with the OpenVINO Inference Engine.

Using OpenVINO Export in Deployment

Once you have the OpenVINO files, you can use the OpenVINO Runtime to run the model. The Runtime provides a unified API to inference across all supported Intel hardware. It also provides advanced capabilities like load balancing across Intel hardware and asynchronous execution. For more information on running the inference, refer to the Inference with OpenVINO Runtime Guide.

Remember, you'll need the XML and BIN files as well as any application-specific settings like input size, scale factor for normalization, etc., to correctly set up and use the model with the Runtime.

In your deployment application, you would typically do the following steps:

1. Initialize OpenVINO by creating core = Core().
2. Load the model using the core.read_model() method.
3. Compile the model using the core.compile_model() function.
4. Prepare the input (image, text, audio, etc.).
5. Run inference using compiled_model(input_data).

For more detailed steps and code snippets, refer to the OpenVINO documentation or API tutorial.

OpenVINO YOLOv8 Benchmarks

YOLOv8 benchmarks below were run by the Ultralytics team on 4 different model formats measuring speed and accuracy: PyTorch, TorchScript, ONNX and OpenVINO. Benchmarks were run on Intel Flex and Arc GPUs, and on Intel Xeon CPUs at FP32 precision (with the half=False argument).

!!! Note

The benchmarking results below are for reference and might vary based on the exact hardware and software configuration of a system, as well as the current workload of the system at the time the benchmarks are run.

All benchmarks run with `openvino` Python package version [2023.0.1](https://pypi.org/project/openvino/2023.0.1/).

Intel Flex GPU

The Intel® Data Center GPU Flex Series is a versatile and robust solution designed for the intelligent visual cloud. This GPU supports a wide array of workloads including media streaming, cloud gaming, AI visual inference, and virtual desktop Infrastructure workloads. It stands out for its open architecture and built-in support for the AV1 encode, providing a standards-based software stack for high-performance, cross-architecture applications. The Flex Series GPU is optimized for density and quality, offering high reliability, availability, and scalability.

Benchmarks below run on Intel® Data Center GPU Flex 170 at FP32 precision.

Model	Format	Status	Size (MB)	mAP50-95(B)	Inference time (ms/im)
YOLOv8n	PyTorch	✅	6.2	0.3709	21.79
YOLOv8n	TorchScript	✅	12.4	0.3704	23.24
YOLOv8n	ONNX	✅	12.2	0.3704	37.22
YOLOv8n	OpenVINO	✅	12.3	0.3703	3.29
YOLOv8s	PyTorch	✅	21.5	0.4471	31.89
YOLOv8s	TorchScript	✅	42.9	0.4472	32.71
YOLOv8s	ONNX	✅	42.8	0.4472	43.42
YOLOv8s	OpenVINO	✅	42.9	0.4470	3.92
YOLOv8m	PyTorch	✅	49.7	0.5013	50.75
YOLOv8m	TorchScript	✅	99.2	0.4999	47.90
YOLOv8m	ONNX	✅	99.0	0.4999	63.16
YOLOv8m	OpenVINO	✅	49.8	0.4997	7.11
YOLOv8l	PyTorch	✅	83.7	0.5293	77.45
YOLOv8l	TorchScript	✅	167.2	0.5268	85.71
YOLOv8l	ONNX	✅	166.8	0.5268	88.94
YOLOv8l	OpenVINO	✅	167.0	0.5264	9.37
YOLOv8x	PyTorch	✅	130.5	0.5404	100.09
YOLOv8x	TorchScript	✅	260.7	0.5371	114.64
YOLOv8x	ONNX	✅	260.4	0.5371	110.32
YOLOv8x	OpenVINO	✅	260.6	0.5367	15.02

This table represents the benchmark results for five different models (YOLOv8n, YOLOv8s, YOLOv8m, YOLOv8l, YOLOv8x) across four different formats (PyTorch, TorchScript, ONNX, OpenVINO), giving us the status, size, mAP50-95(B) metric, and inference time for each combination.

Intel Arc GPU

Intel® Arc™ represents Intel's foray into the dedicated GPU market. The Arc™ series, designed to compete with leading GPU manufacturers like AMD and Nvidia, caters to both the laptop and desktop markets. The series includes mobile versions for compact devices like laptops, and larger, more powerful versions for desktop computers.

The Arc™ series is divided into three categories: Arc™ 3, Arc™ 5, and Arc™ 7, with each number indicating the performance level. Each category includes several models, and the 'M' in the GPU model name signifies a mobile, integrated variant.

Early reviews have praised the Arc™ series, particularly the integrated A770M GPU, for its impressive graphics performance. The availability of the Arc™ series varies by region, and additional models are expected to be released soon. Intel® Arc™ GPUs offer high-performance solutions for a range of computing needs, from gaming to content creation.

Benchmarks below run on Intel® Arc 770 GPU at FP32 precision.

Model	Format	Status	Size (MB)	metrics/mAP50-95(B)	Inference time (ms/im)
YOLOv8n	PyTorch	✅	6.2	0.3709	88.79
YOLOv8n	TorchScript	✅	12.4	0.3704	102.66
YOLOv8n	ONNX	✅	12.2	0.3704	57.98
YOLOv8n	OpenVINO	✅	12.3	0.3703	8.52
YOLOv8s	PyTorch	✅	21.5	0.4471	189.83
YOLOv8s	TorchScript	✅	42.9	0.4472	227.58
YOLOv8s	ONNX	✅	42.7	0.4472	142.03
YOLOv8s	OpenVINO	✅	42.9	0.4469	9.19
YOLOv8m	PyTorch	✅	49.7	0.5013	411.64
YOLOv8m	TorchScript	✅	99.2	0.4999	517.12
YOLOv8m	ONNX	✅	98.9	0.4999	298.68
YOLOv8m	OpenVINO	✅	99.1	0.4996	12.55
YOLOv8l	PyTorch	✅	83.7	0.5293	725.73
YOLOv8l	TorchScript	✅	167.1	0.5268	892.83
YOLOv8l	ONNX	✅	166.8	0.5268	576.11
YOLOv8l	OpenVINO	✅	167.0	0.5262	17.62
YOLOv8x	PyTorch	✅	130.5	0.5404	988.92
YOLOv8x	TorchScript	✅	260.7	0.5371	1186.42
YOLOv8x	ONNX	✅	260.4	0.5371	768.90
YOLOv8x	OpenVINO	✅	260.6	0.5367	19

Intel Xeon CPU

The Intel® Xeon® CPU is a high-performance, server-grade processor designed for complex and demanding workloads. From high-end cloud computing and virtualization to artificial intelligence and machine learning applications, Xeon® CPUs provide the power, reliability, and flexibility required for today's data centers.

Notably, Xeon® CPUs deliver high compute density and scalability, making them ideal for both small businesses and large enterprises. By choosing Intel® Xeon® CPUs, organizations can confidently handle their most demanding computing tasks and foster innovation while maintaining cost-effectiveness and operational efficiency.

Benchmarks below run on 4th Gen Intel® Xeon® Scalable CPU at FP32 precision.

Model	Format	Status	Size (MB)	metrics/mAP50-95(B)	Inference time (ms/im)
YOLOv8n	PyTorch	✅	6.2	0.3709	24.36
YOLOv8n	TorchScript	✅	12.4	0.3704	23.93
YOLOv8n	ONNX	✅	12.2	0.3704	39.86
YOLOv8n	OpenVINO	✅	12.3	0.3704	11.34
YOLOv8s	PyTorch	✅	21.5	0.4471	33.77
YOLOv8s	TorchScript	✅	42.9	0.4472	34.84
YOLOv8s	ONNX	✅	42.8	0.4472	43.23
YOLOv8s	OpenVINO	✅	42.9	0.4471	13.86
YOLOv8m	PyTorch	✅	49.7	0.5013	53.91
YOLOv8m	TorchScript	✅	99.2	0.4999	53.51
YOLOv8m	ONNX	✅	99.0	0.4999	64.16
YOLOv8m	OpenVINO	✅	99.1	0.4996	28.79
YOLOv8l	PyTorch	✅	83.7	0.5293	75.78
YOLOv8l	TorchScript	✅	167.2	0.5268	79.13
YOLOv8l	ONNX	✅	166.8	0.5268	88.45
YOLOv8l	OpenVINO	✅	167.0	0.5263	56.23
YOLOv8x	PyTorch	✅	130.5	0.5404	96.60
YOLOv8x	TorchScript	✅	260.7	0.5371	114.28
YOLOv8x	ONNX	✅	260.4	0.5371	111.02
YOLOv8x	OpenVINO	✅	260.6	0.5371	83.28

Intel Core CPU

The Intel® Core® series is a range of high-performance processors by Intel. The lineup includes Core i3 (entry-level), Core i5 (mid-range), Core i7 (high-end), and Core i9 (extreme performance). Each series caters to different computing needs and budgets, from everyday tasks to demanding professional workloads. With each new generation, improvements are made to performance, energy efficiency, and features.

Benchmarks below run on 13th Gen Intel® Core® i7-13700H CPU at FP32 precision.

Model	Format	Status	Size (MB)	metrics/mAP50-95(B)	Inference time (ms/im)
YOLOv8n	PyTorch	✅	6.2	0.4478	104.61
YOLOv8n	TorchScript	✅	12.4	0.4525	112.39
YOLOv8n	ONNX	✅	12.2	0.4525	28.02
YOLOv8n	OpenVINO	✅	12.3	0.4504	23.53
YOLOv8s	PyTorch	✅	21.5	0.5885	194.83
YOLOv8s	TorchScript	✅	43.0	0.5962	202.01
YOLOv8s	ONNX	✅	42.8	0.5962	65.74
YOLOv8s	OpenVINO	✅	42.9	0.5966	38.66
YOLOv8m	PyTorch	✅	49.7	0.6101	355.23
YOLOv8m	TorchScript	✅	99.2	0.6120	424.78
YOLOv8m	ONNX	✅	99.0	0.6120	173.39
YOLOv8m	OpenVINO	✅	99.1	0.6091	69.80
YOLOv8l	PyTorch	✅	83.7	0.6591	593.00
YOLOv8l	TorchScript	✅	167.2	0.6580	697.54
YOLOv8l	ONNX	✅	166.8	0.6580	342.15
YOLOv8l	OpenVINO	✅	167.0	0.0708	117.69
YOLOv8x	PyTorch	✅	130.5	0.6651	804.65
YOLOv8x	TorchScript	✅	260.8	0.6650	921.46
YOLOv8x	ONNX	✅	260.4	0.6650	526.66
YOLOv8x	OpenVINO	✅	260.6	0.6619	158.73

Reproduce Our Results

To reproduce the Ultralytics benchmarks above on all export formats run this code:

!!! Example

=== "Python"

    ```python
    from ultralytics import YOLO

    # Load a YOLOv8n PyTorch model
    model = YOLO("yolov8n.pt")

    # Benchmark YOLOv8n speed and accuracy on the COCO8 dataset for all export formats
    results = model.benchmarks(data="coco8.yaml")
    ```

=== "CLI"

    ```bash
    # Benchmark YOLOv8n speed and accuracy on the COCO8 dataset for all export formats
    yolo benchmark model=yolov8n.pt data=coco8.yaml
    ```

Note that benchmarking results might vary based on the exact hardware and software configuration of a system, as well as the current workload of the system at the time the benchmarks are run. For the most reliable results use a dataset with a large number of images, i.e. `data='coco128.yaml' (128 val images), or `data='coco.yaml'` (5000 val images).

Conclusion

The benchmarking results clearly demonstrate the benefits of exporting the YOLOv8 model to the OpenVINO format. Across different models and hardware platforms, the OpenVINO format consistently outperforms other formats in terms of inference speed while maintaining comparable accuracy.

For the Intel® Data Center GPU Flex Series, the OpenVINO format was able to deliver inference speeds almost 10 times faster than the original PyTorch format. On the Xeon CPU, the OpenVINO format was twice as fast as the PyTorch format. The accuracy of the models remained nearly identical across the different formats.

The benchmarks underline the effectiveness of OpenVINO as a tool for deploying deep learning models. By converting models to the OpenVINO format, developers can achieve significant performance improvements, making it easier to deploy these models in real-world applications.

For more detailed information and instructions on using OpenVINO, refer to the official OpenVINO documentation.

FAQ

How do I export YOLOv8 models to OpenVINO format?

Exporting YOLOv8 models to the OpenVINO format can significantly enhance CPU speed and enable GPU and NPU accelerations on Intel hardware. To export, you can use either Python or CLI as shown below:

!!! Example

=== "Python"

    ```python
    from ultralytics import YOLO

    # Load a YOLOv8n PyTorch model
    model = YOLO("yolov8n.pt")

    # Export the model
    model.export(format="openvino")  # creates 'yolov8n_openvino_model/'
    ```

=== "CLI"

    ```bash
    # Export a YOLOv8n PyTorch model to OpenVINO format
    yolo export model=yolov8n.pt format=openvino  # creates 'yolov8n_openvino_model/'
    ```

For more information, refer to the export formats documentation.

What are the benefits of using OpenVINO with YOLOv8 models?

Using Intel's OpenVINO toolkit with YOLOv8 models offers several benefits:

1. Performance: Achieve up to 3x speedup on CPU inference and leverage Intel GPUs and NPUs for acceleration.
2. Model Optimizer: Convert, optimize, and execute models from popular frameworks like PyTorch, TensorFlow, and ONNX.
3. Ease of Use: Over 80 tutorial notebooks are available to help users get started, including ones for YOLOv8.
4. Heterogeneous Execution: Deploy models on various Intel hardware with a unified API.

For detailed performance comparisons, visit our benchmarks section.

How can I run inference using a YOLOv8 model exported to OpenVINO?

After exporting a YOLOv8 model to OpenVINO format, you can run inference using Python or CLI:

!!! Example

=== "Python"

    ```python
    from ultralytics import YOLO

    # Load the exported OpenVINO model
    ov_model = YOLO("yolov8n_openvino_model/")

    # Run inference
    results = ov_model("https://ultralytics.com/images/bus.jpg")
    ```

=== "CLI"

    ```bash
    # Run inference with the exported model
    yolo predict model=yolov8n_openvino_model source='https://ultralytics.com/images/bus.jpg'
    ```

Refer to our predict mode documentation for more details.

Why should I choose Ultralytics YOLOv8 over other models for OpenVINO export?

Ultralytics YOLOv8 is optimized for real-time object detection with high accuracy and speed. Specifically, when combined with OpenVINO, YOLOv8 provides:

• Up to 3x speedup on Intel CPUs
• Seamless deployment on Intel GPUs and NPUs
• Consistent and comparable accuracy across various export formats

For in-depth performance analysis, check our detailed YOLOv8 benchmarks on different hardware.

Can I benchmark YOLOv8 models on different formats such as PyTorch, ONNX, and OpenVINO?

Yes, you can benchmark YOLOv8 models in various formats including PyTorch, TorchScript, ONNX, and OpenVINO. Use the following code snippet to run benchmarks on your chosen dataset:

!!! Example

=== "Python"

    ```python
    from ultralytics import YOLO

    # Load a YOLOv8n PyTorch model
    model = YOLO("yolov8n.pt")

    # Benchmark YOLOv8n speed and accuracy on the COCO8 dataset for all export formats
    results = model.benchmarks(data="coco8.yaml")
    ```

=== "CLI"

    ```bash
    # Benchmark YOLOv8n speed and accuracy on the COCO8 dataset for all export formats
    yolo benchmark model=yolov8n.pt data=coco8.yaml
    ```

For detailed benchmark results, refer to our benchmarks section and export formats documentation.