coco

Visualization: Explore in Know Your Data
Description:

COCO is a large-scale object detection, segmentation, and captioning dataset.

Homepage: http://cocodataset.org/#home
Source code: tfds.object_detection.Coco
Versions:
- 1.1.0 (default): No release notes.
Dataset size: Unknown size
Auto-cached (documentation): Unknown
Supervised keys (See as_supervised doc): None
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}
}

coco/2014 (default config)

Config description: This version contains images, bounding boxes and labels for the 2014 version.
Download size: 37.57 GiB
Splits:

Split	Examples
`'test'`	40,775
`'test2015'`	81,434
`'train'`	82,783
`'validation'`	40,504

Features:

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

Figure (tfds.show_examples):

Visualization

Examples (tfds.as_dataframe):

coco/2017

Config description: This version contains images, bounding boxes and labels for the 2017 version.
Download size: 25.20 GiB
Splits:

Split	Examples
`'test'`	40,670
`'train'`	118,287
`'validation'`	5,000

Features:

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

Figure (tfds.show_examples):

Visualization

Examples (tfds.as_dataframe):

coco/2017_panoptic

Config description: This version contains images, bounding boxes and labels for the 2017 version.
Download size: 19.57 GiB
Splits:

Split	Examples
`'train'`	118,287
`'validation'`	5,000

Features:

FeaturesDict({
    'image': Image(shape=(None, None, 3), dtype=tf.uint8),
    'image/filename': Text(shape=(), dtype=tf.string),
    'image/id': tf.int64,
    'panoptic_image': Image(shape=(None, None, 3), dtype=tf.uint8),
    'panoptic_image/filename': Text(shape=(), dtype=tf.string),
    'panoptic_objects': Sequence({
        'area': tf.int64,
        'bbox': BBoxFeature(shape=(4,), dtype=tf.float32),
        'id': tf.int64,
        'is_crowd': tf.bool,
        'label': ClassLabel(shape=(), dtype=tf.int64, num_classes=133),
    }),
})

Figure (tfds.show_examples): Not supported.
Examples (tfds.as_dataframe):