Overlapped 100 50

Overlapped 100-50 is a dataset construction method for computer vision tasks, designed to improve the generalization and robustness of models in complex scenarios by introducing overlapping samples. This method ensures a certain degree of sample overlap between the training and test sets through carefully designed data distribution, thereby more realistically simulating the actual data distribution encountered in real-world applications and enhancing the model's practical performance. It is particularly valuable for evaluating and optimizing computer vision models, especially when dealing with large-scale, high-complexity datasets, as it can effectively boost the performance and reliability of the models.