Efficient-b0 tends to learn faster compared to its family members. Since the dataset is smaller, with 64 as batch size, unfreezing all layers would be better for b0 compared to the same config in b4, the quality of purchase depends on it, I think you may perform a small experiment with the best-unlabelled images (64 batches, 0.x LR, y epochs) and see if b0 outperforms b4.
In any case, I don’t think it matters. If all the evaluations will run on the same configuration then the performance will be equally good or bad for all the participants. However, with the above experiment, I guess you and we would be able to see if the purchase makes any sense or not.