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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.
Aug 23, 2020 · The COCO dataset includes 330K images of complex scenes exhaustively annotated with 80 object categories with segmentation masks, 91 stuff categories with segmentation masks, person keypoint annotations, and 5 captions per image.
We use MS COCO[9] dataset for this challenge. For im-age segmentation tasks, MS COCO provides both training and validation data in the form of images and comprehen-sive annotations for all three segmentation tasks. These data are commonly used by the vision community, and related vision competitions for object detection and segmentation are ...
COCO Keypoints Dataset (I) • 17 types of keypoints. • 58,945 images. • 156,165 annotated people. • 1,710,498 total keypoints. Overall Statistics (train/val):
Joint COCO and Mapillary Workshop at ICCV 2019: COCO Keypoint Detection Challenge Track Technical Report: ByteDance HRNet Bin Xiao, Zaizhou Gong, Yifan Lu, Linfu Wen ByteDance AI Lab Abstract In this report, we present our multi-person keypoint de-tection system for COCO Keypoint Detection Challenge 2019. It contains three main components ...
Oct 27, 2019 · Mapillary Vistas is complementary to COCO in terms of dataset focus and can be readily used for studying various recognition tasks in a visually distinct domain from COCO. COCO focuses on recognition in natural scenes, while Mapillary focuses on recognition of street-view scenes.
mentaion (COCO val) 1.1.2 Semantic134 As suggested in [11], the lack of thing class supervision can introduce discontinuity in stuff segmentation. We modified our semantic branch from predicting 54 categories (53 stuff classes + void class), to predicting 134 categories (all 133 classes + void class), which improves PQ by 0.5. 1.1.3 Task Specialization and Loss Re-weighting
• Dataset: COCO- Panoptic (Stuff Parts) Average the two models for panoptic calculation ·
