I have been trying yo reproduce the results of a SOTA paper regarding object detection. I have reimplemented their method and trained on the same dataset, based on the paper, however I was not able to achieve their results on the datasets they use for evaluation, no matter what I have tried. Then I also studied their referenced papers and realised that many of them use a train-test split strategy for evaluating their models. This means that they use a part of the evaluation dataset for finetuning their already trained model and then evaluate it on the testing part of the same dataset. In the case of these papers, this fact was explicitly mentioned.