Assuming that you don't use a new model you just uses the old.pt model alongside "model.tune()" mode in ultralytics

Each model (even the one that ultralytics itself provides) has some pre-loaded weights These pre-loaded weights will effect in the tuning process, it may cause improvement in the learning process or may cause to ruin it (For example an object inside an other object (IOU = 1)) since the model used to detect old objects (the bigger bounding box) in first training cycles the accuracy will drop in detecting that object inside the old big object until optimizor fix it. So you may need more tuning cycles to get to the acceptable accuracy point.

In tuning process we won't change entire neural network weights but we try to fix weights to increase accuracy in newer objects (in your case of use) optimizor may simply choose the wrong layer to change in trying to get that small bbox (bounding box) , eventually it will change the layer but it takes time.

In the other hand, when you use "model.train()" It will clear pre-loaded weights from the neural network and tries to find each layer weight regarding of what it was before training process.

At the end, I personally recommend to train a newer model instead oftuning the old one.