Update README.md

README.md

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 ---
-license: mit
+annotations_creators:
+- expert-generated
+- machine-generated
+language_creators:
+- machine-generated
+languages:
+- en
+licenses:
+- mit
+multilinguality:
+- monolingual
+pretty_name: fashion-mnist-c
+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).