datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
SID_Set
Dataset Card for SID_Set
Dataset Summary
We provide Social media Image Detection dataSet (SID-Set), which offers three key advantages:
Extensive volume: Featuring 300K AI-generated/tampered and authentic images with comprehensive annotations.
Broad diversity: Encompassing fully synthetic and tampered images across various classes.
Elevated realism: Including images that are predominantly indistinguishable from genuine ones through mere visual inspection.
Please check… See the full description on the dataset page: https://huggingface.co/datasets/saberzl/SID_Set.image_training_set自用的训练集合集，用于 Stable Diffusion 模型微调。
该仓库仅用于存档，不提供任何技术支持。
scannet_mini_val_set_suitecolpali_train_set
Dataset Description
This dataset is the training set of ColPali it includes 127,460 query-image pairs from both openly available academic datasets (63%) and a synthetic dataset made up
of pages from web-crawled PDF documents and augmented with VLM-generated (Claude-3 Sonnet) pseudo-questions (37%).
Our training set is fully English by design, enabling us to study zero-shot generalization to non-English languages.
Dataset
#examples (query-page pairs)
Language
DocVQA
39… See the full description on the dataset page: https://huggingface.co/datasets/vidore/colpali_train_set.So-Fake-Set
Dataset Card for So-Fake-Set
Dataset Summary
We provide So-Fake-Set, A large-scale, diverse dataset tailored for social media image forgery detection!
Please check our website to explore more visual results.
Dataset Structure
"image" (Image): Input images, including real, full_synthetic, and tampered images.
"mask" (Image): Binary mask highlighting manipulated regions in tampered images.
"label" (str): Classification category.
"generator" (str): The… See the full description on the dataset page: https://huggingface.co/datasets/saberzl/So-Fake-Set.Omni-Fake-SET
Omni-Fake-SET
Omni-Fake-SET is the in-distribution split of Omni-Fake, a unified multimodal deepfake dataset for social-media forensics. It covers image, audio, video, and audio–video talking-head (AV-TH) modalities. Each modality uses the same three-way label space: real, fully synthetic, and tampered. Pair with the held-out benchmark Omni-Fake-OOD for out-of-distribution evaluation.
Paper: arXiv:2605.01638
Project page: Omni-Fake
License: CC-BY-4.0
Video (hybrid… See the full description on the dataset page: https://huggingface.co/datasets/JamalLee/Omni-Fake-SET.diffusion-pretrain-set-ft1
diffusion-pretrain-set-ft1
A multi-source image-caption pretraining dataset assembled from ten upstream
sources via a uniform ingest pipeline. Designed for a full pretrain or finetune
pipeline meant to curate for any major diffusion model preliminary, with the sole
intent to create a more powerful baseline preliminary train and a baseline
for synthesizing images to train the next generation of the VLM model.
This is a lot like the snake eating it's own tail, so it must be… See the full description on the dataset page: https://huggingface.co/datasets/AbstractPhil/diffusion-pretrain-set-ft1.dronescapes2_annotated_train_set
Dataset Card for DroneScapes2 (annotated train set)
This is a FiftyOne dataset with 218 samples. It's a subset of this split from the original repo.
Installation
If you haven't already, install FiftyOne:
pip install -U fiftyone
Usage
import fiftyone as fo
from fiftyone.utils.huggingface import load_from_hub
# Load the dataset
# Note: other available arguments include 'max_samples', etc
dataset =… See the full description on the dataset page: https://huggingface.co/datasets/Voxel51/dronescapes2_annotated_train_set.Omni-Sets
Omni-Sets
A large-scale, multi-modal instruction-tuning dataset spanning six modalities (audio, speech, image, video, visual documents, and cross-modal omni) with both single-turn dense captions and multi-turn instruction-following conversations. Designed for training omni-modal language models that can perceive and reason across all modalities.
590,858 total samples | 5,635 hours of audio/video | 6 configs | 17 source datasets
Overview
Config
Modality… See the full description on the dataset page: https://huggingface.co/datasets/MBZUAI/Omni-Sets.Set5
Dataset Card for Set5
This is a FiftyOne dataset with 135 samples.
Installation
If you haven't already, install FiftyOne:
pip install -U fiftyone
Usage
import fiftyone as fo
import fiftyone.utils.huggingface as fouh
# Load the dataset
# Note: other available arguments include 'max_samples', etc
dataset = fouh.load_from_hub("Voxel51/Set5")
# Launch the App
session = fo.launch_app(dataset)
Dataset Details
Dataset Description… See the full description on the dataset page: https://huggingface.co/datasets/Voxel51/Set5.tft-set17-unit-detector-yolo
TFT Set17 Unit Detector YOLO
YOLO-format unit detector dataset for TFT Set 17 experiments.
This package combines:
synthetic board screenshots made from arena textures and modelviewer unit renders
clean multi-angle modelviewer unit reference images
The dataset is intended for training a single-class unit object detector.
Structure
images/train/*.jpg
images/val/*.jpg
labels/train/*.txt
labels/val/*.txt
data.yaml
classes.txt
manifest.json
Counts… See the full description on the dataset page: https://huggingface.co/datasets/Ashen0li/tft-set17-unit-detector-yolo.Set14
Dataset Card for Set14
This is a FiftyOne dataset with 378 samples.
Installation
If you haven't already, install FiftyOne:
pip install -U fiftyone
Usage
import fiftyone as fo
import fiftyone.utils.huggingface as fouh
# Load the dataset
# Note: other available arguments include 'max_samples', etc
dataset = fouh.load_from_hub("Voxel51/Set14")
# Launch the App
session = fo.launch_app(dataset)
Dataset Details
Dataset Description… See the full description on the dataset page: https://huggingface.co/datasets/Voxel51/Set14.BToks-vidore_colpali_train_set
BToks ViDoRe ColPali Train Set
This dataset repository contains Lance-format converted data used by the open-source reproduction code for Bottleneck Tokens for Unified Multimodal Retrieval (arXiv:2604.11095).
Source
Converted from vidore/colpali_train_set. This repository does not change upstream ownership, licensing, citation requirements, or usage restrictions.
Format
The data is stored as Lance tables for the BToks/VLM2Emb training and evaluation… See the full description on the dataset page: https://huggingface.co/datasets/siyrus/BToks-vidore_colpali_train_set.diffbir-mixed-setsobjaverse_rendering_setdiffusion-pretrain-set-ft1-1024
diffusion-pretrain-set-ft1-1024
1024px (2x) upscale of AbstractPhil/diffusion-pretrain-set-ft1.
WARNING
MUCH OF THIS DATA WAS MODEL UPSCALED USING RAPID UPSCALERS.
THIS IS NOT CONSISTENTLY HIGH FIDELITY NOR IS IT EVEN CLOSE TO FAIR FIDELITY AT TIMES.
PLEASE use this ONLY for pretraining, new concepts, and simple design purposes ONLY. HEAVILY PRUNE FOR FINETUNING.
Thank you, good luck my friends.
Details
Model: realesr-general-x4v3 (SRVGG Compact… See the full description on the dataset page: https://huggingface.co/datasets/AbstractPhil/diffusion-pretrain-set-ft1-1024.lebanese_aug_setSID_Set
Dataset Card for SID_Set
Dataset Summary
We provide Social media Image Detection dataSet (SID-Set), which offers three key advantages:
Extensive volume: Featuring 300K AI-generated/tampered and authentic images with comprehensive annotations.
Broad diversity: Encompassing fully synthetic and tampered images across various classes.
Elevated realism: Including images that are predominantly indistinguishable from genuine ones through mere visual inspection.
Please… See the full description on the dataset page: https://huggingface.co/datasets/RAID-techjam/SID_Set.emu_edit_test_set
Dataset Card for the Emu Edit Test Set
Dataset Summary
To create a benchmark for image editing we first define seven different categories of potential image editing operations: background alteration (background), comprehensive image changes (global), style alteration (style), object removal (remove), object addition (add), localized modifications (local), and color/texture alterations (texture).
Then, we utilize the diverse set of input images from the MagicBrush… See the full description on the dataset page: https://huggingface.co/datasets/facebook/emu_edit_test_set.So-Fake-Set-Resized-224veri_seti_adiSID_Set
Dataset Card for SID_Set
Dataset Summary
We provide Social media Image Detection dataSet (SID-Set), which offers three key advantages:
Extensive volume: Featuring 300K AI-generated/tampered and authentic images with comprehensive annotations.
Broad diversity: Encompassing fully synthetic and tampered images across various classes.
Elevated realism: Including images that are predominantly indistinguishable from genuine ones through mere visual inspection.
Please… See the full description on the dataset page: https://huggingface.co/datasets/Beastarz/SID_Set.climbing-holds
[!IMPORTANT]
This dataset is in construction. The current files are raw scans intended for establishing the structure.
Using them? Help us clean them up or identify the brands by consulting the CONTRIBUTING.md guide.
GUI for contributions
https://setrsoft.github.io/holds-dataset-hub/
Or send your files here
Climbing Holds 3D dataset (SetRsoft)
📋 Project Overview
This dataset is a community-driven open-source dataset of 3D-scanned climbing holds… See the full description on the dataset page: https://huggingface.co/datasets/setrsoft/climbing-holds.colpali_train_set_split_by_sourcesroie_data_setNuosuBburma-OCR-Evaluation-Set
NuosuBburma OCR Evaluation Set
规范彝文 OCR 评估集
用于规范彝文（NuosuBburma）的模型性能评估与错误分析。
内容涵盖纯彝文、彝汉混排及少量含拉丁字母的混排文本；场景覆盖书籍、手写、屏幕和实拍。
真实性声明：全部评估样本来自真实扫描或实际拍摄，不含合成评估数据。
评估任务
输入为单张包含规范彝文、彝汉混排及少量拉丁字母、数字或标点的图像。模型按照图像中的视觉阅读顺序转写可见文字，保留必要的行结构和原有字符，不进行翻译、改写或文本补全。评估重点是复杂版式、混排文字和真实拍摄干扰条件下的文字识别能力。
在线可视化
查看评估集分布与图像—标准答案对照
内容统计
项目
数量
样本 / 图片
1030 / 1030
文档页 / OCR 实例
519 / 511
图像类别
类别
数据形态
主要干扰
评估重点… See the full description on the dataset page: https://huggingface.co/datasets/nanxidajun/NuosuBburma-OCR-Evaluation-Set.fast-food-floor-waste-grasping-training-set-next-pack-9f7b7681-1106dcde
Fast-Food Cleaning Robot — Floor Mess Dataset
Training dataset for a cleaning robot operating in fast-food-style food-service spaces (break areas / dining). Scenes are staged in break-area environments cluttered with food-service furnishings and food items (pizza, grocery food, cups, spoons) so the robot learns to perceive and act on mess. Covers detection, grasping, navigation, obstacle avoidance and pick-and-place. Renders are 1024x1024 with RGB plus albedo, metric depth and… See the full description on the dataset page: https://huggingface.co/datasets/physicl-community/fast-food-floor-waste-grasping-training-set-next-pack-9f7b7681-1106dcde.set_fcos_runsDCVAI-Challenge-Public-Eval-SetThis is a FiftyOne dataset with 7,436 samples.
Installation
If you haven''t already, install FiftyOne:
pip install -U fiftyone
Usage
import fiftyone as fo
import fiftyone.utils.huggingface as fouh
# Load the dataset
# Note: other available arguments include ''max_samples'', etc
dataset = fouh.load_from_hub("Voxel51/DCVAI-Challenge-Public-Eval-Set")
# Launch the App
session = fo.launch_app(dataset)
Dataset Card for Public Evaluation set for the Data… See the full description on the dataset page: https://huggingface.co/datasets/Voxel51/DCVAI-Challenge-Public-Eval-Set.Data-Centric-Visual-AI-Challenge-Train-Set
Dataset Card for Data-Centric-Visual-AI-Train-Set
This is a FiftyOne dataset with 30,000 samples.
Installation
If you haven't already, install FiftyOne:
pip install -U fiftyone
Usage
import fiftyone as fo
import fiftyone.utils.huggingface as fouh
# Load the dataset
# Note: other available arguments include 'max_samples', etc
dataset = fouh.load_from_hub("Voxel51/Data-Centric-Visual-AI-Challenge-Train-Set")
# Launch the App
session = fo.launch_app(dataset)… See the full description on the dataset page: https://huggingface.co/datasets/Voxel51/Data-Centric-Visual-AI-Challenge-Train-Set.