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lvis

LVIS: A dataset for large vocabulary instance segmentation.

Split Examples
'test' 19,822
'train' 100,170
'validation' 19,809
  • Feature structure:
FeaturesDict({
    'image': Image(shape=(None, None, 3), dtype=uint8),
    'image/id': int64,
    'neg_category_ids': Sequence(ClassLabel(shape=(), dtype=int64, num_classes=1203)),
    'not_exhaustive_category_ids': Sequence(ClassLabel(shape=(), dtype=int64, num_classes=1203)),
    'objects': Sequence({
        'area': int64,
        'bbox': BBoxFeature(shape=(4,), dtype=float32),
        'id': int64,
        'label': ClassLabel(shape=(), dtype=int64, num_classes=1203),
        'segmentation': Image(shape=(None, None, 1), dtype=uint8),
    }),
})
  • Feature documentation:
Feature Class Shape Dtype Description
FeaturesDict
image Image (None, None, 3) uint8
image/id Tensor int64
neg_category_ids Sequence(ClassLabel) (None,) int64
not_exhaustive_category_ids Sequence(ClassLabel) (None,) int64
objects Sequence
objects/area Tensor int64
objects/bbox BBoxFeature (4,) float32
objects/id Tensor int64
objects/label ClassLabel int64
objects/segmentation Image (None, None, 1) uint8

Visualization

  • Citation:
@inproceedings{gupta2019lvis,
  title={ {LVIS}: A Dataset for Large Vocabulary Instance Segmentation},
  author={Gupta, Agrim and Dollar, Piotr and Girshick, Ross},
  booktitle={Proceedings of the {IEEE} Conference on Computer Vision and Pattern Recognition},
  year={2019}
}