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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).
COCO minitrain is a curated mini training set (25K images ≈ 20% of train2017) for COCO. It is useful for hyperparameter tuning and reducing the cost of ablation experiments. minitrain 's object instance statistics match those of train2017 (see the stats page ). val2017 performance of a model trained on minitrain is strongly positively ...
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.
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.
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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.
COCO is a large-scale object detection, segmentation, and captioning dataset. This is part of the fast.ai datasets collection hosted by AWS for convenience of fast.ai students. If you use this dataset in your research please cite arXiv:1405.0312 [cs.CV].
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.
