Overlapped 25 25

Overlapped 25-25 is a data annotation method in the field of computer vision, designed to increase the diversity and annotation accuracy of datasets by annotating images multiple times with 25% overlapping regions. This method can effectively reduce annotation errors and enhance the robustness of model training, thereby improving the performance and reliability of visual recognition tasks. In practical applications, Overlapped 25-25 is widely used in object detection, image segmentation, and scene understanding, contributing to the development of more accurate and generalized computer vision models.