Boost Your YOLOv8 Model Performance with OpenVINO Acceleration

Accelerating AI with OpenVINO and YOLOv8

Glenn Jocher, the founder of Ultralytics, and Raymond Lowe from Intel, delve into enhancing the performance of YOLOv8 models using OpenVINO acceleration. The discussion covers the journey from YOLO models on Darknet to PyTorch, and eventually to the significant speed gains achievable with OpenVINO deployment on edge devices.

Background and Transition to OpenVINO

Glenn Jocher, an AI enthusiast, moved YOLO models from their original Darknet architecture to PyTorch, focusing on making them more user-friendly. He realized the potential of OpenVINO in providing faster deployment speeds on edge devices, which led to energy savings and the ability to run larger or more accurate models.

Raymond Lowe's Journey at Intel

Raymond’s fascination with computer vision began in school, and he continued to pursue this interest through his Master’s and PhD, focusing on optimizing HDR video performance. His journey led him to Intel, where he became involved with OpenVINO, a tool that significantly increases performance while decreasing energy consumption.

From Aerospace to AI

Glenn originally aspired to be a pilot but eventually found a passion for data analysis in particle physics and AI, leading to the foundation of Ultralytics. His work on vision models aims to make a tangible impact on society.

The Benefits of Export Formats and OpenVINO

Exporting a trained PyTorch model into a format suitable for edge devices is key to performance. OpenVINO serves as a bridge, offering a way to run models more efficiently on various hardware.

OpenVINO's Edge in AI Deployment

Raymond emphasizes the importance of choosing the right processor and leveraging OpenVINO's cross-architecture support. OpenVINO's Auto plugin simplifies deployment by automatically selecting the best hardware backend for running models.

The Power of Quantization

Quantization involves compressing models without significant accuracy loss, resulting in faster inference times. Post-training quantization allows for the compression of an already trained model by using a representative dataset, while quantization-aware training incorporates this process from the beginning.

Integrating OpenVINO with YOLOv8

The integration of OpenVINO with YOLOv8 is streamlined, offering simple export and inference commands. Benchmarks indicate that OpenVINO is the fastest format for running YOLOv8 models on Intel hardware.

Closing Thoughts

The session concludes with a Q&A addressing the impact of OpenVINO on model accuracy and comparisons with other platforms like CUDA and sparse ml. The speakers encourage engagement through a survey to gather feedback and feature requests.

For a detailed guide on how to optimize your YOLOv8 models with OpenVINO, check out the Ultralytics documentation and the OpenVINO toolkit.

Watch the full video for in-depth insights and demonstrations: OpenVINO Acceleration of YOLOv8 Models.