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TheKernel01/Tiny-GenImage

Tiny GenImage Dataset 📝 Dataset Description Dataset Summary The Tiny GenImage Dataset is a curated, scaled-down collection of images and associated metadata designed to train, validate, and benchmark models for detecting and identifying artificially generated content. The dataset contains a mix of real-world images alongside those generated by prominent AI models, including various diffusion models (like Stable Diffusion 1.4/1.5, GLIDE, Midjourney… See the full description on the dataset page: https://huggingface.co/datasets/TheKernel01/Tiny-GenImage.

sourceHugging Facecc-by-nc-sa-4.0updated 6mo agoView on Hugging Face
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Dataset Card

Tiny GenImage Dataset

📝 Dataset Description

Dataset Summary

The Tiny GenImage Dataset is a curated, scaled-down collection of images and associated metadata designed to train, validate, and benchmark models for detecting and identifying artificially generated content. The dataset contains a mix of real-world images alongside those generated by prominent AI models, including various diffusion models (like Stable Diffusion 1.4/1.5, GLIDE, Midjourney, ADM, VQDM, Wukong) and GANs (BigGAN).

Each image is labeled under two categories, enabling researchers and developers to tackle two distinct, high-value computer vision tasks: binary real/fake classification and multi-class source model identification.

Supported Tasks and Leaderboards

This dataset directly supports two critical image classification tasks:

**Task ID****Task Name****Description****Output Classes**
Task ABinary Veracity ClassificationClassifying images as either real or fake.2 (real, fake)
Task BAI Model Source IdentificationIdentifying the specific AI generation model used for images labeled as AI-Generated.9 (Real, ADM, BigGAN, GLIDE, Midjourney, SD14, SD15, VQDM, Wukong)

Languages

The descriptive text, including all class labels and metadata, is in English (en).

🗂️ Data Splits

The dataset is divided into training and validation splits to facilitate standard machine learning workflows.

**Split****Number of Instances****Notes**
train28,000Used for model training and weight optimization.
validation7,000Used for hyperparameter tuning and intermediate model evaluation.

💾 Dataset Structure

Data Instances

A single data instance consists of an image file and two distinct labels detailing its source and authenticity.

**Field Name****Example Value****Description**
image<PIL.Image.Image object>The actual image content loaded into a PIL object.
label1Binary label for authenticity (Real vs. AI-Generated).
generator4Multi-class label for the specific generation model (or Real).

Data Fields

The dataset contains the following fields:

**Field Name****Data Type****Description**
imagedatasets.Image()The actual image content (e.g., .jpg, .png).
labeldatasets.ClassLabelTask A: Binary label for image veracity.
generatordatasets.ClassLabelTask B: Label specifying the generation source/model.

🏷️ Label Definitions

The two label fields use the following strict mappings:

`label` (Binary Veracity Classification)

**Label****Value****Description**
real0Image is a real photograph/non-AI generated.
fake1Image was created by an AI generation model.

`generator` (Model Source Identification)

**Label****Value****Description**
Real0Real image (no AI generation involved).
ADM1Generated by Ablated Diffusion Model (Guided Diffusion).
BigGAN2Generated by BigGAN.
GLIDE3Generated by GLIDE.
Midjourney4Generated by Midjourney.
SD145Generated by Stable Diffusion 1.4.
SD156Generated by Stable Diffusion 1.5.
VQDM7Generated by Vector Quantized Diffusion Model.
Wukong8Generated by the Wukong diffusion model.

🔗 Sources