coco

coco is configured with tfds.object_detection.coco.CocoConfig and has the following configurations predefined (defaults to the first one):

  • 2014 (v1.1.0) (Size: 37.57 GiB): COCO is a large-scale object detection, segmentation, and captioning dataset. This version contains images, bounding boxes " and labels for the 2014 version. 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 defines 91 classes but the data only uses 80 classes.
    • Panotptic annotations defines defines 200 classes but only uses 133.
  • 2017 (v1.1.0) (Size: 25.20 GiB): COCO is a large-scale object detection, segmentation, and captioning dataset. This version contains images, bounding boxes " and labels for the 2017 version. 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 defines 91 classes but the data only uses 80 classes.
    • Panotptic annotations defines defines 200 classes but only uses 133.
  • 2017_panoptic (v1.1.0) (Size: 19.57 GiB): COCO is a large-scale object detection, segmentation, and captioning dataset. This version contains images, bounding boxes " and labels for the 2017 version. 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 defines 91 classes but the data only uses 80 classes.
    • Panotptic annotations defines defines 200 classes but only uses 133.

coco/2014

COCO is a large-scale object detection, segmentation, and captioning dataset. This version contains images, bounding boxes " and labels for the 2014 version. 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 defines 91 classes but the data only uses 80 classes. * Panotptic annotations defines defines 200 classes but only uses 133.

Versions:

  • 1.1.0 (default):

Statistics

Split Examples
ALL 245,496
TRAIN 82,783
TEST2015 81,434
TEST 40,775
VALIDATION 40,504

Features

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

Homepage

coco/2017

COCO is a large-scale object detection, segmentation, and captioning dataset. This version contains images, bounding boxes " and labels for the 2017 version. 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 defines 91 classes but the data only uses 80 classes. * Panotptic annotations defines defines 200 classes but only uses 133.

Versions:

  • 1.1.0 (default):

Statistics

Split Examples
ALL 163,957
TRAIN 118,287
TEST 40,670
VALIDATION 5,000

Features

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

Homepage

coco/2017_panoptic

COCO is a large-scale object detection, segmentation, and captioning dataset. This version contains images, bounding boxes " and labels for the 2017 version. 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 defines 91 classes but the data only uses 80 classes. * Panotptic annotations defines defines 200 classes but only uses 133.

Versions:

  • 1.1.0 (default):

Statistics

Split Examples
ALL 123,287
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': Tensor(shape=(), dtype=tf.int64),
    'panoptic_image': Image(shape=(None, None, 3), dtype=tf.uint8),
    'panoptic_image/filename': Text(shape=(), dtype=tf.string),
    'panoptic_objects': Sequence({
        'area': Tensor(shape=(), dtype=tf.int64),
        'bbox': BBoxFeature(shape=(4,), dtype=tf.float32),
        'id': Tensor(shape=(), dtype=tf.int64),
        'is_crowd': Tensor(shape=(), dtype=tf.bool),
        'label': ClassLabel(shape=(), dtype=tf.int64, num_classes=133),
    }),
})

Homepage

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}
}