datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
GlitchBench
GlitchBench
This repository contains the dataset for the paper GlitchBench: Can large multimodal models detect video game glitches?
by
Mohammad Reza Taesiri,
Tianjun Feng,
Anh Nguyen, and
Cor-Paul Bezemer
(CVPR 2024)
Abstract
Large multimodal models (LMMs) have evolved from large language models (LLMs) to integrate multiple input modalities, such as visual inputs. This integration augments the… See the full description on the dataset page: https://huggingface.co/datasets/glitchbench/GlitchBench.ROCOv2-radiology-minicityscapes-pseudo-labels
Cityscapes Unsupervised Panoptic Pseudo-Labels
Pseudo-labels for unsupervised panoptic segmentation on Cityscapes, generated using overclustered k-means semantics + depth-guided instance splitting.
Contents
Pseudo-Labels
Directory
Description
Files
Format
pseudo_semantic_raw_k80/
Overclustered k=80 semantic labels
~3.5K PNGs + centroids.npz
PNG (values 0-79), train/val split
cups_pseudo_labels_depthpro_tau020/
CUPS-format combined labels (DepthPro… See the full description on the dataset page: https://huggingface.co/datasets/qbit-glitch/cityscapes-pseudo-labels.pulsar-glitch-catalog
Jodrell Bank Pulsar Glitch Catalogue
Credit: NASA/JPL-Caltech
Part of a dataset collection on Hugging Face.
Dataset description
Comprehensive catalog of pulsar glitch events from the Jodrell Bank Centre for Astrophysics. Pulsar glitches are sudden spin-up events in neutron stars, thought to arise from angular momentum transfer between the superfluid interior and the solid crust.
During a glitch, the rotation frequency of the pulsar increases abruptly… See the full description on the dataset page: https://huggingface.co/datasets/juliensimon/pulsar-glitch-catalog.deepextractor-glitch-reconstructions
DeepExtractor Glitch Reconstructions
Time-domain reconstructions of seven gravitational-wave detector glitch classes from LIGO's third observing run (O3), produced using DeepExtractor. This dataset was used to train GlitchGAN, a class-conditional generative model for realistic glitch synthesis described in:
T. Dooney et al., Realistic Time-Domain Synthesis of Gravitational-Wave Detector Glitches using Class-Conditional Derivative Generative Adversarial Networks, 2026.… See the full description on the dataset page: https://huggingface.co/datasets/tomdooney/deepextractor-glitch-reconstructions.1nenseiglitch-llava-game-qa-dataset-wipwan_glitch_effectThis dataset contains videos generated using Wan 2.1 T2V 14B.
Glitch_transformation_dataGlitch_transf0rmation_wwansample_glitch_data2GlitchBenchv2-GlitchDetectionQwen3.5-Glitch-Tokenstestmethanol-trainingGlitch_transf0rmation_wanGlitch_transf0rmation_wwannmodel_datasetendocrine__nutritional_and_metabolic_diseasesGlitchBenchv2-GlitchDetectionVideoGlitch_transf0rmation_wanncityscapes-cups-pseudo-labelscityscapes_pseudo_labelsamazon-ml-challenge-trainGlitchBenchPrivateGlitchLabeler-data-with-user-annotationsThe_Glitch_Animated_SeriesGlitchBenchv2SteamScreenshots-GlitchURLsGlitchBenchv2-Parametric