Fashion-MNIST is a dataset comprising of 28×28 grayscale images of 70,000 fashion products from 10 categories, with 7,000 images per category. The training set has 60,000 images and the test set has 10,000 images. Fashion-MNIST shares the same image size, data format and the structure of training and testing splits with the original MNIST.
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MVTec AD is a dataset for benchmarking anomaly detection methods with a focus on industrial inspection. It contains over 5000 high-resolution images divided into fifteen different object and texture categories. Each category comprises a set of defect-free training images and a test set of images with various kinds of defects as well as images without defects.
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ImageNet-O consists of images from classes that are not found in the ImageNet-1k dataset. It is used to test the robustness of vision models to out-of-distribution samples. It's reported using the AUPR metric.
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The Vistas-NP dataset is an out-of-distribution detection dataset based on the Mapillary Vistas dataset. The original Vistas dataset consists of 18,000 training images and 2,000 validation images with 66 classes. In Vistas-NP the human classes are used as outliers due to their dispersion across scenes and visual diversity from other objects. The dataset is created by excluding all images with class person and the three rider classes to the test subset. Consequently, the dataset has 8,003 train images and 830 validation images. The test set contains 11,167.
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ADFI Dataset is an image dataset for anomaly detection methods with a focus on industrial inspection. Each category sub dataset comprises a training set of images and a test set of images with various kinds of defects as well as images without defects.
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