datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
trace-rx-eval-predictions
TRACE-RX Evaluation Predictions
Per-image detector scores from an independent evaluation of the two TechJam 2026 TRACE-RX
detectors, run 30 Aug – 1 Sep 2026.
No images here. Every file contains scores, labels, asset ids and transform names only — this is
derived evaluation metadata, not a redistribution of any source imagery. The underlying corpora
(Joshyxwa/data_draft, Joshyxwa/techjam2026, techjam-aigc/wildfake-eval-subset) keep their own
terms, and data_draft's WildFake rows… See the full description on the dataset page: https://huggingface.co/datasets/joelleoqiyi/trace-rx-eval-predictions.VisionEncoder-Eval-ReproDataA Strong Baseline for Evaluating Vision Encodersin Multimodal Large Language Models
Yilin Yang1,* ·
Jun-Tao Tang2,* ·
Kengyi Wang3 ·
Siyuan Su3 ·
Gaoyong Luo4 ·
Mingda Chen1,†
1School of Artificial Intelligence, Shanghai Jiao Tong University
2Nanjing University ·
3Fudan University ·
4Independent Researcher
*Equal contribution. †Corresponding author.… See the full description on the dataset page: https://huggingface.co/datasets/336labs/VisionEncoder-Eval-ReproData.mlcd-mteb-cifar-eval
MLCD vs CLIP on MTEB CIFAR-10/100: integration and evaluation
Evaluation results accompanying the MTEB integration of two MLCD image encoders
(PR #5406, resolving
issue #2571).
Two DeepGlint-AI MLCD encoders were integrated into MTEB, verified against the
reference implementation, and evaluated on the official MTEB CIFAR-10/CIFAR-100
image-classification tasks alongside size-matched OpenAI CLIP baselines.
What was measured
Official MTEB image classification: 5… See the full description on the dataset page: https://huggingface.co/datasets/b4ph/mlcd-mteb-cifar-eval.oceanguard-marine-debris-eval-1000
OceanGuard AI — Marine Debris Evaluation Hold-out (annotations only)
The held-out evaluation split used to report the LoRA adapter delta in the
OceanGuard AI Kaggle Gemma 4 Good Hackathon submission
(Global Resilience track + Unsloth bonus track).
Important — this repository contains only the annotations and metadata.
The 1 000 underwater / coastal images are not redistributed here. They
come from three pre-existing third-party datasets, each with its own
license. Reviewers and… See the full description on the dataset page: https://huggingface.co/datasets/asferrer/oceanguard-marine-debris-eval-1000.MUMU-Eval-6000
MUMU Eval 6000
This repository contains the 6,000-image source-data evaluation set used for
the Florence-2 and LFM2.5-VL-450M baselines in the MUMU evaluation repository.
It is an independently prepared research split, not an official MUMU Challenge
release.
Splits
Split
Images
Ground truth in manifest
validation
1,000
Yes
test
5,000
Yes
The split contains 2,001 Task A samples, 2,000 Task B samples, and 1,999 Task C
samples. All 6,000 image… See the full description on the dataset page: https://huggingface.co/datasets/JinyuLiu/MUMU-Eval-6000.evaluation
Skill-Aligned Annotation for Text-to-Image Evaluation
Companion dataset for the NeurIPS 2026 paper "Towards Objective Evaluation".
The dataset contains generated images from 7 text-to-image models, evaluated
by 6 human annotators (anonymized) plus an LLM judge across 9 skill-aligned
annotation strategies.
Configs
Config
Rows
Description
images
621
Generated images (621 WebP) with embedded bytes; one row per (prompt_id, generator).
prompts
179
Per-prompt… See the full description on the dataset page: https://huggingface.co/datasets/Skill-Aigned/evaluation.