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, 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 A	Binary Veracity Classification	Classifying images as either real or fake.	2 (real, fake)
Task B	AI Model Source Identification	Identifying 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
train	28,000	Used for model training and weight optimization.
validation	7,000	Used 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.
label	1	Binary label for authenticity (Real vs. AI-Generated).
generator	4	Multi-class label for the specific generation model (or Real).

Data Fields

The dataset contains the following fields:

Field Name	Data Type	Description
image	datasets.Image()	The actual image content (e.g., .jpg, .png).
label	datasets.ClassLabel	Task A: Binary label for image veracity.
generator	datasets.ClassLabel	Task 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
real	0	Image is a real photograph/non-AI generated.
fake	1	Image was created by an AI generation model.

generator (Model Source Identification)

Label	Value	Description
Real	0	Real image (no AI generation involved).
ADM	1	Generated by Ablated Diffusion Model (Guided Diffusion).
BigGAN	2	Generated by BigGAN.
GLIDE	3	Generated by GLIDE.
Midjourney	4	Generated by Midjourney.
SD14	5	Generated by Stable Diffusion 1.4.
SD15	6	Generated by Stable Diffusion 1.5.
VQDM	7	Generated by Vector Quantized Diffusion Model.
Wukong	8	Generated by the Wukong diffusion model.

🔗 Sources

• Original dataset: yangsangtai/tiny-genimage (Kaggle)