mweiss/fashion_mnist_corrupted
收藏资源简介：
--- 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).
--- annotations_creators: - 专家生成 - 机器生成 language_creators: - 机器生成 language: - 英语（en） license: - MIT（mit） multilinguality: - 单语言 pretty_name: fashion-mnist-corrupted size_categories: - 10000 < 样本量 < 100000 source_datasets: - 扩展|Fashion-MNIST task_categories: - 图像分类（image-classification） task_ids: [] --- # Fashion-MNIST-C（损坏版Fashion-MNIST） 本数据集为带损坏的Fashion-MNIST基准测试集，用于测试在Fashion-MNIST上训练的计算机视觉模型的分布外（out-of-distribution, OOD）鲁棒性。 [Fashion-MNIST](https://github.com/zalandoresearch/fashion-mnist) 是MNIST的即插即用替代数据集，而Fashion-MNIST-C则是MNIST-C（MNIST-C）的对应即插即用替代数据集。 ## 损坏类型 我们对图像施加了以下损坏操作，与MNIST-C中的设置保持一致： - **噪声**（散粒噪声与脉冲噪声） - **模糊**（玻璃模糊与运动模糊） - **变换**（剪切、缩放、旋转、亮度调整、对比度调整、饱和度调整与色彩反转） 此外，我们还施加了多种**图像翻转与旋转变换**：对于时尚类图像而言，翻转操作不会改变其标签，且仍能生成合法的图像。但我们观察到，在原始Fashion-MNIST数据集中，绝大多数图像的朝向一致（例如多数鞋类图像的鞋头朝向左侧）。因此，翻转后的图像可作为有效的分布外输入样本。 多数损坏操作会随机选取**严重程度**参数，因此部分损坏后的图像极难分类，而另一些图像的损坏痕迹则较为隐蔽。 ## 示例 | 翻转图像 | 模糊图像 | 旋转图像 | 噪声图像 | 噪声图像 | 翻转图像 | | ------------- | ------------- | -------- | --------- | -------- | --------- | | <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"> | ## 引用 若您使用本数据集，请引用以下论文： @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} } 此外，您也可引用FMNIST与MNIST-C相关文献。 ## 致谢 - Fashion-MNIST-C的设计灵感源自谷歌的MNIST-C，本仓库本质上是其代码的复刻。您可参考其[论文](https://arxiv.org/abs/1906.02337)与[仓库](https://github.com/google-research/mnist-c)。 - 原始未损坏的Fashion-MNIST数据集可于[此处](https://github.com/zalandoresearch/fashion-mnist)获取。
Fashion-Mnist-C (Corrupted Fashion-Mnist)
概述
Fashion-Mnist-C 是一个用于测试计算机视觉模型在 Fashion-MNIST 数据集上训练后的分布外鲁棒性的基准数据集。它是 MNIST-C 的对应替代品。
数据集详情
- 标注创建者: 专家生成和机器生成
- 语言创建者: 机器生成
- 语言: 英语
- 许可证: MIT
- 多语言性: 单语种
- 数据集大小: 10K<n<100K
- 源数据集: 扩展自 Fashion-MNIST
- 任务类别: 图像分类
数据集特点
- 数据集名称: fashion-mnist-corrupted
- 数据集别名: Fashion-Mnist-C
数据集应用
- 测试目标: 测试计算机视觉模型在 Fashion-MNIST 数据集上训练后的分布外鲁棒性。
数据集修改
- 数据集修改: 对图像应用以下修改以生成分布外数据：
- 噪声: 包括 shot noise 和 impulse noise
- 模糊: 包括 glass blur 和 motion blur
- 变换: 包括 shear, scale, rotate, brightness, contrast, saturate, inverse
- 图像翻转和旋转: 对时尚图像进行翻转和旋转，以提供有效的分布外输入。
数据集示例
- 示例图像: 展示了经过不同修改（如旋转、模糊、噪声等）的图像示例。
引用
-
引用文献:
@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} }
致谢
- 灵感来源: 受 Google 的 MNIST-C 启发。
- 原始数据集: 非损坏的 Fashion-MNIST 数据集。
