If I have, say, a Yolo or RetinaNet Object Detection Model... if I train it with 10 vs 50 classes, (assuming 3000 training data images per class), will the model with 10 classes perform similarly to the model with 50 classes? Is there a 'soft limit' to the number of classes a model can successfully hold in 'weight memory' ?.
I notice for most COCO examples, the class # is set at 80. Is there deteriorating performance when they push that number up into the 200s?
Is there any benchmarks done on this type of question? I would assume its a well discussed problem, i.e. splitting object detection of many classes across multiple trained models or packing them all into a single model?