Overlapped 50 50

Overlapped 50-50 is a dataset splitting method in the field of computer vision, aiming to set the overlap ratio of samples between the training set and the test set at 50%. This ensures that the model faces both partially seen and completely unseen data during evaluation. The goal of this method is to enhance the model's generalization and robustness, making it more stable and reliable in practical applications. Overlapped 50-50 has significant application value in tasks such as image recognition and object detection, helping to more realistically simulate the data distribution scenarios encountered in real-world settings.