], "categories": ["id": 5, "name": "person"]
frame_time = {} for img in coco['images']: frame_time[img['id']] = img.get('timestamp_ms', img.get('time_ms', 0)) coco srt
The rapid advancement of deep learning in computer vision is intrinsically linked to the availability of large-scale, high-quality datasets. Among these, the Common Objects in Context (COCO) dataset stands as a pivotal benchmark. This paper provides an overview of the COCO dataset, exploring its unique annotation methodology, scale, and diversity. We analyze the dataset's role in driving progress in object detection, instance segmentation, and keypoint estimation tasks. Furthermore, we discuss the limitations of the dataset and its enduring legacy in shaping modern neural network architectures. ], "categories": ["id": 5, "name": "person"] frame_time =