datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
COCOLogic-v2
COCOLogic-V2
COCOLogic-V2 is an object-centric dataset for visual inductive reasoning on real-world
images. Built on MSCOCO, it frames reasoning as a multilabel classification task over 10
compositional first-order-logic rules (object presence/absence, counting, and count
comparisons). Samples of each rule are divided into different positive variants, as well
as types of near-boundary (NB) negatives, and the typically easy far-from-boundary (FB)
negatives. These annotations… See the full description on the dataset page: https://huggingface.co/datasets/AIML-TUDA/COCOLogic-v2.deepfake-detection-dataset-v2
Deepfake Detection Dataset V2
This dataset contains images and detailed explanations for training and evaluating deepfake detection models. It includes original images, manipulated images, confidence scores, and comprehensive technical and non-technical explanations.
Dataset Structure
The dataset consists of:
Original images
CAM visualization images
CAM overlay images
Comparison images
Labels (real/fake)
Confidence scores
Image captions
Technical and non-technical… See the full description on the dataset page: https://huggingface.co/datasets/saakshigupta/deepfake-detection-dataset-v2.INVOICE_ANNOTATION_V2urban-scenes-v2
Urban Scenes – Preprocessing Utilities
This repository contains helper scripts used during preprocessing and validation
of the Urban Scenes v2 dataset.
Contents
preprocess.py – feature normalization utilities
stats.ipynb – exploratory statistics
label_map.json – class label mapping
Notes
Raw data is not included due to licensing constraints.