MKZuziak/cifar10h

--- dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': airplane '1': automobile '2': bird '3': cat '4': deer '5': dog '6': frog '7': horse '8': ship '9': truck - name: expert_probs list: float32 length: 10 - name: expert_counts list: int32 length: 10 - name: expert_argmax list: int32 length: 10 splits: - name: train num_bytes: 23931580 num_examples: 10000 download_size: 24078418 dataset_size: 23931580 configs: - config_name: default data_files: - split: train path: data/train-* license: mit task_categories: - image-classification tags: - CIFAR10 - CIFAR10H - L2D - LearningtoDefer - LearningtoAsk - HumanLabels pretty_name: CIFAR10H size_categories: - 1K<n<10K --- # CIFAR-10H Hugging Face Dataset This repository contains a Hugging Face dataset build of CIFAR-10H, an extension of the CIFAR-10 test set with human-annotated label distributions. ## Dataset Description CIFAR-10H adds human uncertainty information to the CIFAR-10 test images by providing: - `expert_probs`: probability distributions over the 10 CIFAR-10 classes - `expert_counts`: raw human vote counts for each class - `expert_argmax`: one-hot encoded labels from the human-majority choice The dataset is constructed from the original CIFAR-10 test images together with the CIFAR-10H annotation files. ## Original Citation Please credit the original CIFAR-10H authors when using this dataset: Peterson, Joshua C.; Battleday, Ruairidh M.; Griffiths, Thomas L.; Russakovsky, Olga. "Human uncertainty makes classification more robust." arXiv preprint arXiv:1908.07086, 2019. Source repository: https://github.com/jcpeterson/cifar-10h This HuggingFace conversion was done Zuziak, Maciej K.