This repository implements an object detection system for optical remote sensing images using Adaptive Mask R-CNN. The model leverages transfer learning, data augmentation, and fine-tuning to address challenges like object scale variability, small object size, and data scarcity in remote sensing datasets.
The implementation explores different optimization methods (Adam, SGD, RMSprop, Adam_SGD) to achieve the best performance.