datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
llbench-dataset
LL-Bench: Rethinking Low-Level Vision Evaluation in the Era of Large-Scale Generative Models via Human Preferences
Anonymous release prepared for NeurIPS 2026 review. Please do not redistribute.
LL-Bench is a large-scale, human-preference benchmark for evaluating low-level
vision restoration in the era of large generative models (LGMs). It compares
10 LGMs with 16 specilist and 5 all-in-one models across 16 low-level vision tasks, paired with dense human annotations:pairwise… See the full description on the dataset page: https://huggingface.co/datasets/anonymousllbench/llbench-dataset.openbrush-anonymous-masters
OpenBrush Anonymous Masters
Unattributed works from OpenBrush-75K — anonymous old masters across centuries and styles. Useful for broad-style training without artist-specific bias.
Curated subset of jaddai/openbrush. Same CC0 license, same caption schema, same VLM (Qwen3-VL-30B-A3B). This subset exists so you don't have to download 75,313 images to get to the 41,914 you actually want.
Why this subset
37% of the parent dataset is unattributed — a substantial… See the full description on the dataset page: https://huggingface.co/datasets/jaddai/openbrush-anonymous-masters.InSpect
InSpect
InSpect is a curated natural history collection dataset for visual insect specimen understanding. It contains digitized insect specimen images with aligned crops, hierarchical taxonomy, label-derived structured metadata, and fine-grained anatomical part annotations.
Files
specimen_benchmark_metadata.csv: main metadata table. Each row corresponds to one specimen image and includes split information, taxonomic labels, image/crop paths, and label-derived… See the full description on the dataset page: https://huggingface.co/datasets/anonymous-dataset/InSpect.GenScale
GenScale Benchmark (Anonymized Review Release)
This repository contains an anonymized, machine-readable release of the GenScale benchmark for double-blind review.
Files
GenScale_Benchmark_anonymous_clean.json: cleaned benchmark JSON with relative paths only.
data/genscale_entries.parquet: one row per benchmark entry.
data/genscale_pairs.parquet: one row per pairwise scale evaluation unit.
data/genscale_task3_edit_plans.parquet: precise scale-correction plans for S5.… See the full description on the dataset page: https://huggingface.co/datasets/anonymous2049/GenScale.