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Mar 17, 2025 · Explore the COCO-Seg dataset, an extension of COCO, with detailed segmentation annotations. Learn how to train YOLO models with COCO-Seg.

The features are – 265,016 COCO and abstract scenes, three questions per image, ten true answers per question, three likely to be correct answers per question, automatic evaluation metric.

Jun 1, 2024 · COCO is a large-scale object detection, segmentation, and captioning dataset. Note: * Some images from the train and validation sets don't have annotations. * Coco 2014 and 2017 uses the same images, but different train/val/test splits * The test split don't have any annotations (only images).

Mar 17, 2025 · What is the COCO dataset and why is it important for computer vision? The COCO dataset (Common Objects in Context) is a large-scale dataset used for object detection, segmentation, and captioning. It contains 330K images with detailed annotations for 80 object categories, making it essential for benchmarking and training computer vision models.

COCO-Stuff augments all 164K images of the popular COCO [2] dataset with pixel-level stuff annotations. These annotations can be used for scene understanding tasks like semantic segmentation, object detection and image captioning.

COCO-Stuff 10K Dataset: Common Objects in Context Stuff 10k v1.1 is a dataset for instance segmentation, semantic segmentation, and object detection tasks. It is applicable or relevant across various domains.

What is COCO? COCO is a large-scale object detection, segmentation, and captioning dataset. COCO has several features: Object segmentation; Recognition in context; Superpixel stuff segmentation; 330K images (>200K labeled) 1.5 million object instances; 80 object categories; 91 stuff categories; 5 captions per image; 250,000 people with keypoints