YOLOv10: Object Detection King Is Back

This is the 10th iteration of the YOLO model. YOLO has been one of the most important models in the entire Computer Vision field for a while, it was one of the earliest models of Vision and has been used in countless applications. Every version of YOLO has introduced some cool new tricks, that are not just applicable to YOLO itself, but also for the overall DL architecture design. For instance, YOLOv7 delved quite a lot into how to better data augmentation, YOLOv9 introduced reversible architecture, and so on and so forth. So, what’s new with YOLOv10? YOLOv10 is all about inference speed, despite all the advancements, YOLO remains quite a heavy model to date, often requiring GPUs, especially with the newer versions. I know there have been few attempts in the past like TinyYOLO but with the newer YOLOv10, we hope to see even better models.

Topics Covered

• Introduction
• Removing Non-Maximum Suppression (NMS)
• Spatial-Channel Decoupled Downsampling
• Rank-Guided Block Design
• Lightweight Classification Head
• Accuracy-driven model design

Since YOLO architecture has been under development for so many years, it has grown to become quite a sophisticated network. Today’s blog is 4th in our series on Object Detection. I would highly recommend checking out our explainers on the earlier version of YOLOs. Each one of these introduce the reader to brand new ideas in the vision…