The CIFAR-10 dataset (Canadian Institute for Advanced Research, 10 classes) is a subset of the Tiny Images dataset and consists of 60000 32x32 color images. The images are labelled with one of 10 mutually exclusive classes: airplane, automobile (but not truck or pickup truck), bird, cat, deer, dog, frog, horse, ship, and truck (but not pickup truck). There are 6000 images per class with 5000 training and 1000 testing images per class.
14,048 PAPERS • 100 BENCHMARKS
The Cifar10Mnist dataset is created using CIFAR-10 and MNIST data sources. Since the CIFAR-10 training set consists of 50000 images and the MNIST training set contains 60000 digits, the first 50000 digits from MNIST are padded on top of the CIFAR-10 images after making them slightly translucent. A first training dataset is then obtained (50000 images). Furthermore, the remaining 10000 MNIST digits are padded on top of 10000 random CIFAR10 images (with a fixed seed). This gives the possibility of having a second training dataset of 60000 images. For the test set, the 10000 CIFAR-10 images are padded over the 10000 MNIST digits.
1 PAPER • NO BENCHMARKS YET
REAP is a digital benchmark that allows the user to evaluate patch attacks on real images, and under real-world conditions. Built on top of the Mapillary Vistas dataset, the benchmark contains over 14,000 traffic signs. Each sign is augmented with a pair of geometric and lighting transformations, which can be used to apply a digitally generated patch realistically onto the sign.