- 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
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}
}