datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
Wake-Vision
Dataset Card for Wake Vision
Dataset Description
"Wake Vision" is a large, high-quality dataset featuring over 6 million images, significantly exceeding the scale and diversity of
current tinyML datasets (100x). This dataset includes images with annotations of whether each image contains a person. Additionally,
it incorporates a comprehensive fine-grained benchmark to assess fairness and robustness, covering perceived gender, perceived age,
subject distance, lighting… See the full description on the dataset page: https://huggingface.co/datasets/Harvard-Edge/Wake-Vision.EpiBench-NeurIPS2026
EpiBench
Anonymous release for NeurIPS 2026 Evaluations & Datasets Track review (paper ID 1899). All methodology, ablations, and analyses are in the companion paper; this card lists only what reviewers and downstream users need to load the data.
A 25,737-patient ILAE-aligned multimodal epilepsy benchmark derived from PubMed Central case reports + 192 EpiRAG textbook vignettes.
6 tasks: epilepsy_type, seizure_type, ez_localization, aed_response, surgery_outcome, status_epilepticus… See the full description on the dataset page: https://huggingface.co/datasets/NeurIPS-1899-ED-2026/EpiBench-NeurIPS2026.edugraph-exercises
EduGraph Exercises Dataset
EduGraph Exercises is a synthetic ML dataset of math-related visual problems, precisely labeled for training AI models in the education sector.
Every image in this dataset is programmatically generated using the EduGraph Ontology to ensure that visual features are mathematically bound to their pedagogical labels.
Quick Links
Generation Engine: GitHub Repository (Contribute new generators or views!)
Ontology: EduGraph Ontology (Semantic… See the full description on the dataset page: https://huggingface.co/datasets/christian-bick/edugraph-exercises.edgeimpulse-test-image-classification
Edgeimpulse Test Image Classification
This dataset is an integration-test fixture for Edge Impulse's "Import from Hugging Face" flow.
Structure
Splits: train, validation, test
Main fields: image, label
Extra metadata columns (from metadata.csv):
source_split
source_file
source_stem
source_path
Important note
Label source mode: source-metadata.
celebahq_512_id_clusters
celebahq_512 with SRK identity labels
Summary
This dataset is a derived version of jxie/celeba-hq. It keeps the original image set and adds automatically generated identity-group labels derived from face-embedding clustering.
As explained in our experimental setup, we use CelebA-HQ from Karras et al. (2018), specifically the Hugging Face snapshot at revision 7ecc6a45edfb5483ccf2f7df1035d298ffe7c76b. The referenced CelebA-HQ version provides gender labels but no identity… See the full description on the dataset page: https://huggingface.co/datasets/edgarcancinoe/celebahq_512_id_clusters.benchmark
EditJudge-Bench
EditJudge-Bench is a synthetic benchmark for auditing vision-language models used as
automated judges for image-edit verification. Each row contains a source image,
an edited image, a factual edit instruction, counterfactual instructions, and
ground-truth scene parameters produced by a controlled Blender/Infinigen
generation pipeline.
This repository is an anonymous review release for a NeurIPS Evaluations and
Datasets submission.
Dataset Contents
1… See the full description on the dataset page: https://huggingface.co/datasets/EDAnonSubmission/benchmark.birdsnap_liteThis is a version of BirdSnap that will be easier on your free Google Colab quota.