Ground truth annotations for this subset are not publicly available, and the results are submitted to the COCO evaluation server for performance evaluation. Test2017: This subset consists of 20K images used for testing and benchmarking the trained models.Val2017: This subset has 5K images used for validation purposes during model training.Train2017: This subset contains 118K images for training object detection, segmentation, and captioning models.The COCO dataset is split into three subsets: COCO provides standardized evaluation metrics like mean Average Precision (mAP) for object detection, and mean Average Recall (mAR) for segmentation tasks, making it suitable for comparing model performance.Annotations include object bounding boxes, segmentation masks, and captions for each image.

The dataset comprises 80 object categories, including common objects like cars, bicycles, and animals, as well as more specific categories such as umbrellas, handbags, and sports equipment.COCO contains 330K images, with 200K images having annotations for object detection, segmentation, and captioning tasks.It is an essential dataset for researchers and developers working on object detection, segmentation, and pose estimation tasks. It is designed to encourage research on a wide variety of object categories and is commonly used for benchmarking computer vision models. The COCO (Common Objects in Context) dataset is a large-scale object detection, segmentation, and captioning dataset.

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