datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
MLLM-Generated-Image-Detection-Dataset
MLLM-Generated Image Dataset
This dataset contains real and AI-generated image samples organized for binary MLLM-generated image detection.
Paper | Code
Dataset Summary
We construct an MLLM-generated image detection benchmark from GPT Image2 and Nano Banana2. This benchmark covers texture-dominated, structure-dominated, and hybrid-dominated. It is designed to evaluate detector performance under the new challenges introduced by large-scale image generation models.… See the full description on the dataset page: https://huggingface.co/datasets/zr-zhang/MLLM-Generated-Image-Detection-Dataset.SDXL-Generated-Stanford-Dogs
Dataset Card for Generated Dogs
10+ images for each class in the Stanford Dogs dataset, but all generated with SDXL. Images were filtered for CLIP score, and cartoonish images were removed.
This is a FiftyOne dataset with 1305 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… See the full description on the dataset page: https://huggingface.co/datasets/Voxel51/SDXL-Generated-Stanford-Dogs.real-fake-ai-generated-art-images
🎨 Real and Fake (AI-Generated) Art Images Dataset
21,642 balanced images — 10,821 real artworks and 10,821 AI-generated
images — for training models to distinguish authentic art from GAN-generated fakes.
🧭 Overview
This dataset is part of the FauxFinder project, designed to build
advanced models capable of distinguishing between authentic artworks
and AI-generated images. Ideal for binary classification, GAN research,
and computer vision benchmarking.… See the full description on the dataset page: https://huggingface.co/datasets/hmnshudhmn24/real-fake-ai-generated-art-images.ai-generated-ecommerce-images
AI-Generated E-Commerce Images
Overview
This dataset contains 6,031 AI-generated images depicting common e-commerce
after-sales scenarios across 12 categories. The dataset also ships with
companion annotations (annotations_ai.jsonl) describing each image in
chat-completion format.
Dataset Structure
Category
Description
damaged_electronics
Damaged electronics (keyboards, laptops, headphones, etc.)
damaged_phone_screen
Smartphones with cracked or… See the full description on the dataset page: https://huggingface.co/datasets/JoyCN/ai-generated-ecommerce-images.ai-generated-ecommerce-images
AI-Generated E-Commerce Images
Overview
This dataset contains 6,031 AI-generated images depicting common e-commerce
after-sales scenarios across 12 categories. The dataset also ships with
companion annotations (annotations_ai.jsonl) describing each image in
chat-completion format.
Dataset Structure
Category
Description
damaged_electronics
Damaged electronics (keyboards, laptops, headphones, etc.)
damaged_phone_screen
Smartphones with cracked or… See the full description on the dataset page: https://huggingface.co/datasets/prajwalkothwal/ai-generated-ecommerce-images.sdxl-generated-10k
SDXL Generated Images Dataset (10,000 images)
This dataset contains 10,000 AI-generated images created with Stable Diffusion XL for training an AI image detector.
Dataset Details
Model: Stable Diffusion XL Base 1.0
Total Images: 10,000
Resolution: 1024×1024 pixels
Format: JPEG (quality 95)
Inference Steps: 10
Guidance Scale: 7.0
Random Seeds: Unique per image for maximum diversity
Generation Date: 2025-12-30
Prompt Diversity
Images generated with diverse… See the full description on the dataset page: https://huggingface.co/datasets/ash12321/sdxl-generated-10k.AI_Generated_Ghibli
AI-Generated Ghibli Images Dataset
This repository contains a collection of AI-generated images in the style of Studio Ghibli. These images showcase various subjects, characters, and landscapes rendered in the distinctive artistic style associated with Studio Ghibli animation films.
Dataset Overview
Number of Images: 368 high-quality images
Image Format: Primarily PNG and JPG files
Resolution: Various resolutions (primarily high resolution)
Generation Tools: Created… See the full description on the dataset page: https://huggingface.co/datasets/filberthamijoyo/AI_Generated_Ghibli.generated-vietnamese-passeports-datasetData generation in machine learning involves creating or manipulating data to train
and evaluate machine learning models. The purpose of data generation is to provide
diverse and representative examples that cover a wide range of scenarios, ensuring the
model's robustness and generalization.
The dataset contains GENERATED Vietnamese passports, which are replicas of official
passports but with randomly generated details, such as name, date of birth etc.
The primary intention of generating these fake passports is to demonstrate the
structure and content of a typical passport document and to train the neural network to
identify this type of document.
Generated passports can assist in conducting research without accessing or compromising
real user data that is often sensitive and subject to privacy regulations. Synthetic
data generation allows researchers to *develop and refine models using simulated
passport data without risking privacy leaks*.generated-flowers-diffusion
Dataset Card for generated_flowers_with_embeddings
This is a FiftyOne dataset with 150 samples.
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 = load_from_hub("jkubuni/generated-flowers-diffusion")
# Launch the App
session = fo.launch_app(dataset)… See the full description on the dataset page: https://huggingface.co/datasets/jkubuni/generated-flowers-diffusion.CAS3120-2026-Project1_AI-Generated-Image-Detection
Project 1 — AI-Generated Image Detection
Course materials for CAS3120 · Introduction to Machine Learning · Spring 2026, Department of AI, Yonsei University.
Task
Binary image classification: distinguish real images from AI-generated images.
Dataset Summary
Image size: 128 × 128 RGB PNG
Splits:
train: 2,000 images (labeled)
val: 1,000 images (labeled)
test: 2,000 images (labels withheld)
Class balance: 50/50 in each labeled split
Test labels are withheld.… See the full description on the dataset page: https://huggingface.co/datasets/yonsei-dli/CAS3120-2026-Project1_AI-Generated-Image-Detection.generated_flowers
Dataset Card for generated_flowers
This is a FiftyOne dataset with 160 samples.
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 = load_from_hub("maxspeer/generated_flowers")
# Launch the App
session = fo.launch_app(dataset)
Dataset Details… See the full description on the dataset page: https://huggingface.co/datasets/maxspeer/generated_flowers.Project1-AI-Generated-Image-Detection-2026
Project 1 — AI-Generated Image Detection
Course materials for CAS3120 · Introduction to Machine Learning · Spring 2026, Department of AI, Yonsei University.
Task
Binary image classification: distinguish real images from AI-generated images.
Dataset Summary
Image size: 128 × 128 RGB PNG
Splits:
train: 2,000 images (labeled)
val: 1,000 images (labeled)
test: 2,000 images (labels withheld)
Class balance: 50/50 in each labeled split
Test labels are withheld.… See the full description on the dataset page: https://huggingface.co/datasets/legenduck/Project1-AI-Generated-Image-Detection-2026.Robustness-of-AI-Generated-Image-Detection-Against-Localized-Inpainting-Attacks
Robustness of AI-Generated Image Detection Against Localized Inpainting Attacks
This repository hosts the detector-ready datasets and mask packs used in the thesis:
Robustness of AI-Generated Image Detection Against Localized Inpainting AttacksOguz Akin, Saarland University, CISPA Helmholtz Center for Information Security (2025)
It provides standardized evaluation splits for six state-of-the-art AI-generated image (AIGI) detectors across watermarking, passive, and training-free… See the full description on the dataset page: https://huggingface.co/datasets/eoguzakin/Robustness-of-AI-Generated-Image-Detection-Against-Localized-Inpainting-Attacks.generated_flowers
Dataset Card for generated_flowers_with_embeddings
This is a FiftyOne dataset with 128 samples.
It contains images of flowers that are generated using a simple diffusion model trained on the TF_Flowers dataset.
The dataset was created using 16 different text prompts, each of which was used 8 times to generate an image.
Notebook: https://github.com/MatthiasCr/Diffusion-Models-Assignment
Installation
If you haven't already, install FiftyOne:
pip install -U fiftyone… See the full description on the dataset page: https://huggingface.co/datasets/MatthiasCr/generated_flowers.generated-flowers-cv-assignment3-joscha
Dataset Card for generated_flowers_with_embeddings
This is a FiftyOne dataset with 22 samples.
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 = load_from_hub("joscha-s/generated-flowers-cv-assignment3-joscha")
# Launch the App
session =… See the full description on the dataset page: https://huggingface.co/datasets/joscha-s/generated-flowers-cv-assignment3-joscha.graph_dataset_generated_v2
Graph Dataset - Image & LabelMe & OBB Annotation (Train/Val Split)
Dataset Overview
Comprehensive graph/chart detection dataset with ground truth LabelMe polygon annotations and OBB (Oriented Bounding Box) data, split into training and validation sets.
Total examples: 35561 image-annotation pairs
Train: 28448 (80.0%)
Validation: 7113 (20.0%)
Total size: 2134.30 MB
Language: Khmer (km)
Document types: Graph/Chart documents
Ground truth: LabelMe polygon annotations… See the full description on the dataset page: https://huggingface.co/datasets/vichetkao/graph_dataset_generated_v2.generated-stanford-dogs
Generated Stanford Dogs Dataset
Description
This repository contains the dataset used for the generative-data-augmentation project. The dataset is organized as follows:
Dataset Structure
analysis/: This directory contains analysis related to the dataset.
metadata/: This directory contains the list of file path used for the Synthetic (Noisy) and Synthetic (Clean) datasets.
synthetic/: This directory contains the image files. Each folder represents a class.… See the full description on the dataset page: https://huggingface.co/datasets/czl/generated-stanford-dogs.generated-imagenette
Generated Imagenette Dataset
Description
This repository contains the dataset used for the generative-data-augmentation project. The dataset is organized as follows:
Dataset Structure
analysis/: This directory contains analysis related to the dataset.
metadata/: This directory contains the list of file path used for the Synthetic (Noisy) and Synthetic (Clean) datasets.
synthetic/: This directory contains the image files. Each folder represents a class.… See the full description on the dataset page: https://huggingface.co/datasets/czl/generated-imagenette.generated-imagewoof
Generated Imagewoof Dataset
Description
This repository contains the dataset used for the generative-data-augmentation project. The dataset is organized as follows:
Dataset Structure
analysis/: This directory contains analysis related to the dataset.
metadata/: This directory contains the list of file path used for the Synthetic (Noisy) and Synthetic (Clean) datasets.
synthetic/: This directory contains the image files. Each folder represents a class.… See the full description on the dataset page: https://huggingface.co/datasets/czl/generated-imagewoof.