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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).
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Nov 12, 2023 · The COCO (Common Objects in Context) dataset is a large-scale object detection, segmentation, and captioning dataset. It is designed to encourage research on a wide variety of object categories and is commonly used for benchmarking computer vision models.
The MS COCO ( Microsoft Common Objects in Context) dataset is a large-scale object detection, segmentation, key-point detection, and captioning dataset. The dataset consists of 328K images.
MS 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.
Jun 29, 2021 · How to download, visualize, and explore the COCO dataset or subsets with FiftyOne and add model predictions and evaluate them with COCO-style evaluation
FiftyOne is an open-source tool facilitating visualization and access to COCO data resources and serves as an evaluation tool for model analysis on COCO. What is COCO? COCO is a large-scale object detection, segmentation, and captioning dataset.
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.
The COCO train, validation, and test sets, containing more than 200,000 images and 80 object categories, are available on the download page. All object instances are annotated with a detailed segmentation mask.
