YOLOv10 eliminates the need for non-maximum suppression (NMS) during inference by employing consistent dual assignments for training. This approach reduces inference latency and enhances prediction efficiency. The architecture also includes a one-to-one head for inference, ensuring that each object gets a single best prediction. For a detailed explanation, see the Consistent Dual Assignments for NMS-Free Training section.