COCO Dataset

The Common Objects in Context (COCO) dataset is a large-scale image dataset widely used as a benchmark for evaluating object detection, instance segmentation, and related computer vision tasks. Current research focuses on improving model robustness and efficiency, addressing issues like background false positives, occlusion handling, and class imbalance, often employing transformer-based architectures and techniques like knowledge distillation and contrastive learning. The COCO dataset's significance lies in its role in driving advancements in object recognition and scene understanding, with implications for applications ranging from autonomous driving to assistive technologies.