datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
document-processing-benchmark
Document Processing Benchmark
8 public document datasets (receipts, invoices, forms, bank statements,
multi-page docs, contracts) normalized into one parquet schema. Each row
has the document, ground-truth annotations, and per-row token/latency/cost
numbers from real API calls to one or more reference models. You can
read off a target's cost/latency/quality without re-running it.
from datasets import load_dataset
ds = load_dataset("thoughtworks/document-processing-benchmark"… See the full description on the dataset page: https://huggingface.co/datasets/thoughtworks/document-processing-benchmark.Latent-Resonance-AI-Image-Forensics-Benchmark-N1000
Latent Resonance: SOTA Large-Scale AI Image Forensics Benchmark (N=1,000)
Author: Debdip Bandyopadhyay (Independent AI Researcher, Kolkata, India; M.Tech, IIT Jodhpur, AI & Data Science)Preprint & Paper: Latent Resonance: Zero-Shot Autoencoder Inversion and Azimuthal Spectral Forensics for Diffusion Image Attribution (IEEE Flagship / CERN Zenodo 2026)
1. Executive Summary & Diagnostic Suite
This repository contains the complete empirical evaluation records… See the full description on the dataset page: https://huggingface.co/datasets/DebdipCS/Latent-Resonance-AI-Image-Forensics-Benchmark-N1000.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.multi-species-benchmark
multi-species benchmark
Photographs where 2+ species appear in the same frame. Designed to evaluate
multi-label species identification and steering capabilities of biological
vision-language models. Two sources, unified into one parquet schema.
Sources
inat21_multilabel (299 rows, 147 images)
In-distribution: drawn from iNat21
validation images that already carry an iNat-supplied primary label. We use
InternVL3-AWQ to surface
images that also… See the full description on the dataset page: https://huggingface.co/datasets/dcher95/multi-species-benchmark.