CIFAR-10H is a new dataset of soft labels reflecting human perceptual uncertainty for the 10,000-image CIFAR-10 test set, first appearing in the paper:
Joshua C. Peterson*, Ruairidh M. Battleday*, Thomas L. Griffiths, & Olga Russakovsky (2019). Human uncertainty makes classification more robust. In Proceedings of the IEEE International Conference on Computer Vision. (arXiv paper)
10000 x 10 numpy matrix containing human classification counts (out of ~50) for each image and class.
10000 x 10 numpy matrix containing normalized human classification counts (probabilities) for each image and class. These are the labels used for training and evaluation in the above paper.
- Dataset statistics / summary
- Keras loading example
- PyTorch loading example
- Classifier evaluation comparison table
- Example training scripts
Krizhevsky, A., & Hinton, G. (2009). Learning multiple layers of features from tiny images (Vol. 1, No. 4, p. 7). Technical report, University of Toronto.