Nested bounding boxes

I have a dataset (60K images) They contain 2 classes (vehicle, license plate) I tried to Train my YOLO models (yolo5un, yolo8n and yolo11n) to train on this dataset But since the classes are nested (the plate class is inside the vehicle class bounding box) I couldn't get more than 72% map55-95,(forced to use 416x416 image size because deployment size is this) Is there any way/tool/optimization/hayperparameter that I could use to improve my accuracy ? Like changing model (this model had to be small so I could get less than 50ms pre, inference-post processing time in format MNN with 3 channels