On May 27, 2026, at 10:00 pm PDT (1:00 pm EDT) Intel will deliver a webinar “From Annotation to Deployment: Building an Object Detection Pipeline with Geti, YOLO26, and OpenVINO™” From the event page:
Learn from Ultralytics and Intel® AI experts working side by side in this hands-on session and discover how to build production-ready computer vision pipelines for real-world manufacturing and industrial scenarios. We’ll guide you through a complete, end-to-end object detection workflow—from dataset creation and model training to optimization and edge deployment.
Start by rapidly annotating and iteratively refining image labels with Geti Instant Learn, enabling efficient dataset bootstrapping even with limited data. Using this dataset, we’ll fine-tune an Ultralytics YOLO26 model for custom object detection—using hazelnuts as a practical example of common manufacturing inspection tasks. The trained model is then converted and optimized with OpenVINO™, leveraging techniques such as hardware acceleration and precision optimization to achieve low-latency inference. Finally, we’ll deploy and benchmark the solution on Intel® Core™ Processors Series 2 and Intel® Core™ Ultra processors, highlighting key considerations for latency, throughput, and resource utilization in edge environments.
Developers will leave with actionable insights to build industrial-grade AI pipelines:
- End-to-End Solution: From image annotation to edge deployment, making vision AI practical for real-world applications.
- Industrial Relevance: Apply object detection to manufacturing and inspection tasks to boost automation and efficiency.
- Rapid Development: Accelerate dataset creation using Geti Instant Learn, reducing time-to-deployment.
- Optimized Edge AI: Deploy high-performance models with OpenVINO on Intel® hardware for low-latency, efficient inference.
Featured Speakers:
Adrian Boguszewski, AI Software Evangelist – EMEA, Intel
Francesco Mattioli, Partnership and Ecosystem Manager, Ultralytics
For more information and to register, visit the event page.
