Computer vision demos look impressive until they hit production. In the real world, vision systems don’t fail because of accuracy. They fail because of complex pipelines, post-processing, heuristic tuning, and unpredictable behavior across CPUs, GPUs, and edge devices. Insights like these are exactly what @Ronald_vanLoon highlights when discussing real-world AI adoption. One of the biggest pain points? Filtering overlapping detections after inference. It adds latency, complicates exports, and breaks consistency. A new architectural shift is changing everything. End-to-end vision models with no post-processing, built edge-first, delivering faster inference, simpler deployment, and predictable performance, up to 43% faster on CPU. That’s why Ultralytics YOLO26 is setting a new standard for production-ready vision AI. Try YOLO26 today on the Ultralytics Platform: https://bit.ly/3ZG53d6 Learn more: https://bit.ly/4rioF2N Follow my channel today! https://ift.tt/0jN7DeU #UltralyticsPartner @Ultralytics #ai #deeplearning #production #engineering #machinelearning #innovation #technology
from Ronald van Loon https://www.youtube.com/shorts/onGv5Uf1Gvg
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