Datasets:
Tasks:
Image Classification
Modalities:
Image
Languages:
English
Size:
10K<n<100K
ArXiv:
Tags:
License:
annotations_creators: | |
- expert-generated | |
- machine-generated | |
language_creators: | |
- machine-generated | |
language: | |
- en | |
license: | |
- mit | |
multilinguality: | |
- monolingual | |
pretty_name: fashion-mnist-corrupted | |
size_categories: | |
- 10K<n<100K | |
source_datasets: | |
- extended|fashion_mnist | |
task_categories: | |
- image-classification | |
task_ids: [] | |
# Fashion-Mnist-C (Corrupted Fashion-Mnist) | |
A corrupted Fashion-MNIST benchmark for testing out-of-distribution robustness of computer vision models, which were trained on Fashion-Mmnist. | |
[Fashion-Mnist](https://github.com/zalandoresearch/fashion-mnist) is a drop-in replacement for MNIST and Fashion-Mnist-C is a corresponding drop-in replacement for [MNIST-C](https://arxiv.org/abs/1906.02337). | |
## Corruptions | |
The following corruptions are applied to the images, equivalently to MNIST-C: | |
- **Noise** (shot noise and impulse noise) | |
- **Blur** (glass and motion blur) | |
- **Transformations** (shear, scale, rotate, brightness, contrast, saturate, inverse) | |
In addition, we apply various **image flippings and turnings**: For fashion images, flipping the image does not change its label, | |
and still keeps it a valid image. However, we noticed that in the nominal fmnist dataset, most images are identically oriented | |
(e.g. most shoes point to the left side). Thus, flipped images provide valid OOD inputs. | |
Most corruptions are applied at a randomly selected level of *severity*, s.t. some corrupted images are really hard to classify whereas for others the corruption, while present, is subtle. | |
## Examples | |
| Turned | Blurred | Rotated | Noise | Noise | Turned | | |
| ------------- | ------------- | --------| --------- | -------- | --------- | | |
| <img src="https://github.com/testingautomated-usi/fashion-mnist-c/raw/main/generated/png-examples/single_0.png" width="100" height="100"> | <img src="https://github.com/testingautomated-usi/fashion-mnist-c/raw/main/generated/png-examples/single_1.png" width="100" height="100"> | <img src="https://github.com/testingautomated-usi/fashion-mnist-c/raw/main/generated/png-examples/single_6.png" width="100" height="100"> | <img src="https://github.com/testingautomated-usi/fashion-mnist-c/raw/main/generated/png-examples/single_3.png" width="100" height="100"> | <img src="https://github.com/testingautomated-usi/fashion-mnist-c/raw/main/generated/png-examples/single_4.png" width="100" height="100"> | <img src="https://github.com/testingautomated-usi/fashion-mnist-c/raw/main/generated/png-examples/single_5.png" width="100" height="100"> | | |
## Citation | |
If you use this dataset, please cite the following paper: | |
``` | |
@inproceedings{Weiss2022SimpleTechniques, | |
title={Simple Techniques Work Surprisingly Well for Neural Network Test Prioritization and Active Learning}, | |
author={Weiss, Michael and Tonella, Paolo}, | |
booktitle={Proceedings of the 31th ACM SIGSOFT International Symposium on Software Testing and Analysis}, | |
year={2022} | |
} | |
``` | |
Also, you may want to cite FMNIST and MNIST-C. | |
## Credits | |
- Fashion-Mnist-C is inspired by Googles MNIST-C and our repository is essentially a clone of theirs. See their [paper](https://arxiv.org/abs/1906.02337) and [repo](https://github.com/google-research/mnist-c). | |
- Find the nominal (i.e., non-corrupted) Fashion-MNIST dataset [here](https://github.com/zalandoresearch/fashion-mnist). | |