- Description:
ImageNet-A is a set of images labelled with ImageNet labels that were obtained by collecting new data and keeping only those images that ResNet-50 models fail to correctly classify. For more details please refer to the paper.
The label space is the same as that of ImageNet2012. Each example is represented as a dictionary with the following keys:
- 'image': The image, a (H, W, 3)-tensor.
- 'label': An integer in the range [0, 1000).
'file_name': A unique sting identifying the example within the dataset.
Additional Documentation: Explore on Papers With Code
Source code:
tfds.datasets.imagenet_a.Builder
Versions:
0.1.0
(default): No release notes.
Download size:
655.70 MiB
Dataset size:
650.87 MiB
Auto-cached (documentation): No
Splits:
Split | Examples |
---|---|
'test' |
7,500 |
- Feature structure:
FeaturesDict({
'file_name': Text(shape=(), dtype=string),
'image': Image(shape=(None, None, 3), dtype=uint8),
'label': ClassLabel(shape=(), dtype=int64, num_classes=1000),
})
- Feature documentation:
Feature | Class | Shape | Dtype | Description |
---|---|---|---|---|
FeaturesDict | ||||
file_name | Text | string | ||
image | Image | (None, None, 3) | uint8 | |
label | ClassLabel | int64 |
Supervised keys (See
as_supervised
doc):('image', 'label')
Figure (tfds.show_examples):
- Examples (tfds.as_dataframe):
- Citation:
@article{hendrycks2019nae,
title={Natural Adversarial Examples},
author={Dan Hendrycks and Kevin Zhao and Steven Basart and Jacob Steinhardt and Dawn Song},
journal={arXiv preprint arXiv:1907.07174},
year={2019}
}