datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
Omni-Fake-SET
Omni-Fake-SET
Omni-Fake-SET is the in-distribution split of Omni-Fake, a unified multimodal deepfake dataset for social-media forensics. It covers image, audio, video, and audio–video talking-head (AV-TH) modalities. Each modality uses the same three-way label space: real, fully synthetic, and tampered. Pair with the held-out benchmark Omni-Fake-OOD for out-of-distribution evaluation.
Paper: arXiv:2605.01638
Project page: Omni-Fake
License: CC-BY-4.0
Video (hybrid… See the full description on the dataset page: https://huggingface.co/datasets/JamalLee/Omni-Fake-SET.winml-test-set
WinML Test Set
Dataset Summary
WinML Test Set is an evaluation‑only collection for validating model accuracy and stability on Windows ML / DirectML / ONNX Runtime pipelines. It aggregates several permissively‑licensed sources and harmonizes schema for reproducible, regression‑grade testing across backends and versions. Not intended for training.
Intended Use
Accuracy and regression benchmarking of Windows ML / DirectML / ONNX Runtime pipelines.… See the full description on the dataset page: https://huggingface.co/datasets/Futuremark/winml-test-set.medical_records_parsing_validation_set
Medical Records Parsing Validation Set
Dataset Composition and Clinical Relevance
The Eka Medical Records Parsing Dataset empowers evaluation of AI systems designed to extract structured information from unstructured medical documents, enabling true digitisation of healthcare data while maintaining clinical accuracy.
The dataset comprise 288 carefully selected images of laboratory reports and prescriptions representing diverse formats and templates encountered in Indian… See the full description on the dataset page: https://huggingface.co/datasets/ekacare/medical_records_parsing_validation_set.augmented_lebanese_set
augmented_lebanese_set
Split: train with 12400 rows.
Schema
image_url (string)
camera_or_phone_prob (float)
food_prob (float)
dish_name (string)
food_type (string)
ingredients (string)
portion_size (string)
cooking_method (string)
Note: image_url should point to reachable image locations (http(s) or hf:// paths).