clevr

• Description:

CLEVR is a diagnostic dataset that tests a range of visual reasoning abilities. It contains minimal biases and has detailed annotations describing the kind of reasoning each question requires.

• Additional Documentation: Explore on Papers With Code

• Homepage: https://cs.stanford.edu/people/jcjohns/clevr/

• Source code: tfds.datasets.clevr.Builder

• Versions:

  • 3.0.0: No release notes.
  • 3.1.0 (default): Add question/answer text.

• Download size: 17.72 GiB

• Dataset size: 17.75 GiB

• Auto-cached (documentation): No

• Splits:

Split	Examples
'test'	15,000
'train'	70,000
'validation'	15,000

• Feature structure:

FeaturesDict({
    'file_name': Text(shape=(), dtype=string),
    'image': Image(shape=(None, None, 3), dtype=uint8),
    'objects': Sequence({
        '3d_coords': Tensor(shape=(3,), dtype=float32),
        'color': ClassLabel(shape=(), dtype=int64, num_classes=8),
        'material': ClassLabel(shape=(), dtype=int64, num_classes=2),
        'pixel_coords': Tensor(shape=(3,), dtype=float32),
        'rotation': float32,
        'shape': ClassLabel(shape=(), dtype=int64, num_classes=3),
        'size': ClassLabel(shape=(), dtype=int64, num_classes=2),
    }),
    'question_answer': Sequence({
        'answer': Text(shape=(), dtype=string),
        'question': Text(shape=(), dtype=string),
    }),
})

• Feature documentation:

Feature	Class	Shape	Dtype	Description
	FeaturesDict
file_name	Text		string
image	Image	(None, None, 3)	uint8
objects	Sequence
objects/3d_coords	Tensor	(3,)	float32
objects/color	ClassLabel		int64
objects/material	ClassLabel		int64
objects/pixel_coords	Tensor	(3,)	float32
objects/rotation	Tensor		float32
objects/shape	ClassLabel		int64
objects/size	ClassLabel		int64
question_answer	Sequence
question_answer/answer	Text		string
question_answer/question	Text		string

• Supervised keys (See as_supervised doc): None

• Figure (tfds.show_examples):

• Examples (tfds.as_dataframe):

• Citation:

@inproceedings{johnson2017clevr,
  title={ {CLEVR}: A diagnostic dataset for compositional language and elementary visual reasoning},
  author={Johnson, Justin and Hariharan, Bharath and van der Maaten, Laurens and Fei-Fei, Li and Lawrence Zitnick, C and Girshick, Ross},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  year={2017}
}