- Description:
Shape/texture conflict stimuli from "ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness."
Note that, although the dataset source contains images with matching shape and texture and we include them here, they are ignored for most evaluations in the original paper.
Source code:
tfds.image_classification.GeirhosConflictStimuli
Versions:
1.0.0
(default): No release notes.
Download size:
153.96 MiB
Dataset size:
130.44 MiB
Auto-cached (documentation): Only when
shuffle_files=False
(test)Splits:
Split | Examples |
---|---|
'test' |
1,280 |
- Feature structure:
FeaturesDict({
'file_name': Text(shape=(), dtype=string),
'image': Image(shape=(None, None, 3), dtype=uint8),
'shape_imagenet_labels': Sequence(ClassLabel(shape=(), dtype=int64, num_classes=1000)),
'shape_label': ClassLabel(shape=(), dtype=int64, num_classes=16),
'texture_imagenet_labels': Sequence(ClassLabel(shape=(), dtype=int64, num_classes=1000)),
'texture_label': ClassLabel(shape=(), dtype=int64, num_classes=16),
})
- Feature documentation:
Feature | Class | Shape | Dtype | Description |
---|---|---|---|---|
FeaturesDict | ||||
file_name | Text | string | ||
image | Image | (None, None, 3) | uint8 | |
shape_imagenet_labels | Sequence(ClassLabel) | (None,) | int64 | |
shape_label | ClassLabel | int64 | ||
texture_imagenet_labels | Sequence(ClassLabel) | (None,) | int64 | |
texture_label | ClassLabel | int64 |
Supervised keys (See
as_supervised
doc):('image', 'shape_label')
Figure (tfds.show_examples):
- Examples (tfds.as_dataframe):
- Citation:
@inproceedings{
geirhos2018imagenettrained,
title={ImageNet-trained {CNN}s are biased towards texture; increasing shape
bias improves accuracy and robustness.},
author={Robert Geirhos and Patricia Rubisch and Claudio Michaelis and
Matthias Bethge and Felix A. Wichmann and Wieland Brendel},
booktitle={International Conference on Learning Representations},
year={2019},
url={https://openreview.net/forum?id=Bygh9j09KX},
}