Overview
The COCO (Common Objects in Context) dataset is a large-scale object detection, segmentation, and captioning dataset. It has become a standard benchmark for training and evaluating computer vision models. COCO features over 330K images, with 1.5 million object instances, 80 object categories, and 5 captions per image. The dataset is designed to provide a rich and diverse set of images with complex scenes, making it suitable for training models that can generalize well to real-world scenarios. COCO's annotations include object bounding boxes, segmentation masks, keypoints, and image captions. Researchers and developers use COCO to develop and evaluate algorithms for object detection, instance segmentation, keypoint detection, and image captioning. The dataset promotes research into scene understanding and visual recognition tasks.