coco_captions

• Description:

COCO is a large-scale object detection, segmentation, and captioning dataset. This version contains images, bounding boxes, labels, and captions from COCO 2014, split into the subsets defined by Karpathy and Li (2015). This effectively divides the original COCO 2014 validation data into new 5000-image validation and test sets, plus a "restval" set containing the remaining ~30k images. All splits have caption annotations.

• Additional Documentation: Explore on Papers With Code

• Config description: This version contains images, bounding boxes and labels for the 2014 version.

• Homepage: http://cocodataset.org/#home

• Source code: tfds.object_detection.CocoCaptions

• Versions:

  • 1.1.0 (default): No release notes.

• Download size: 37.61 GiB

• Dataset size: 18.83 GiB

• Auto-cached (documentation): No

• Splits:

Split	Examples
'restval'	30,504
'test'	5,000
'train'	82,783
'val'	5,000

• Feature structure:

FeaturesDict({
    'captions': Sequence({
        'id': int64,
        'text': string,
    }),
    'image': Image(shape=(None, None, 3), dtype=uint8),
    'image/filename': Text(shape=(), dtype=string),
    'image/id': int64,
    'objects': Sequence({
        'area': int64,
        'bbox': BBoxFeature(shape=(4,), dtype=float32),
        'id': int64,
        'is_crowd': bool,
        'label': ClassLabel(shape=(), dtype=int64, num_classes=80),
    }),
})

• Feature documentation:

Feature	Class	Shape	Dtype	Description
	FeaturesDict
captions	Sequence
captions/id	Tensor		int64
captions/text	Tensor		string
image	Image	(None, None, 3)	uint8
image/filename	Text		string
image/id	Tensor		int64
objects	Sequence
objects/area	Tensor		int64
objects/bbox	BBoxFeature	(4,)	float32
objects/id	Tensor		int64
objects/is_crowd	Tensor		bool
objects/label	ClassLabel		int64

• Supervised keys (See as_supervised doc): None

• Figure (tfds.show_examples):

• Examples (tfds.as_dataframe):

• Citation:

@article{DBLP:journals/corr/LinMBHPRDZ14,
  author    = {Tsung{-}Yi Lin and
               Michael Maire and
               Serge J. Belongie and
               Lubomir D. Bourdev and
               Ross B. Girshick and
               James Hays and
               Pietro Perona and
               Deva Ramanan and
               Piotr Doll{'{a} }r and
               C. Lawrence Zitnick},
  title     = {Microsoft {COCO:} Common Objects in Context},
  journal   = {CoRR},
  volume    = {abs/1405.0312},
  year      = {2014},
  url       = {http://arxiv.org/abs/1405.0312},
  archivePrefix = {arXiv},
  eprint    = {1405.0312},
  timestamp = {Mon, 13 Aug 2018 16:48:13 +0200},
  biburl    = {https://dblp.org/rec/bib/journals/corr/LinMBHPRDZ14},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}@inproceedings{DBLP:conf/cvpr/KarpathyL15,
  author    = {Andrej Karpathy and
               Fei{-}Fei Li},
  title     = {Deep visual-semantic alignments for generating image
               descriptions},
  booktitle = { {IEEE} Conference on Computer Vision and Pattern Recognition,
               {CVPR} 2015, Boston, MA, USA, June 7-12, 2015},
  pages     = {3128--3137},
  publisher = { {IEEE} Computer Society},
  year      = {2015},
  url       = {https://doi.org/10.1109/CVPR.2015.7298932},
  doi       = {10.1109/CVPR.2015.7298932},
  timestamp = {Wed, 16 Oct 2019 14:14:50 +0200},
  biburl    = {https://dblp.org/rec/conf/cvpr/KarpathyL15.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

coco_captions/2014 (default config)