datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
xAI_Aurora_t2i_human_preferences
Rapidata Aurora Preference
This T2I dataset contains over 400k human responses from over 86k individual annotators, collected in just ~2 Days using the Rapidata Python API, accessible to anyone and ideal for large scale evaluation.
Evaluating Aurora across three categories: preference, coherence, and alignment.
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If you get value from this dataset and would like to see more in the future, please consider liking it.… See the full description on the dataset page: https://huggingface.co/datasets/Rapidata/xAI_Aurora_t2i_human_preferences.xai-attack-detection-cifar10
XAI Attack Detection — CIFAR-10 PGD
This private research dataset contains balanced, paired clean and adversarial images for
studying whether an attack can be detected from a classifier explanation map.
Dataset construction
The source is the CIFAR-10 test split. A fine-tuned OpenCLIP ViT-B/16 classifies each
clean image. Clean-correct examples are attacked with untargeted L-infinity PGD using
epsilon 8/255, step size 2/255, 10 steps, and deterministic random… See the full description on the dataset page: https://huggingface.co/datasets/nimaeb/xai-attack-detection-cifar10.xai-attack-detection-imagenette
XAI Attack Detection: Imagenette targeted BIM/PGD on ViT-B/16
Private research dataset of paired clean and targeted adversarial Imagenette images. It is
built to study how adversarial attacks change a Vision Transformer's explanation maps and to
support later work on attack detection. Each row is one source image with its clean and its
attacked version.
Summary
Pairs
12,420 (train 8,690 · validation 1,860 · test 1,870)
Source images
Imagenette v2… See the full description on the dataset page: https://huggingface.co/datasets/nimaeb/xai-attack-detection-imagenette.