Datasets:

TheKernel01
/

Tiny-GenImage

dataset_info:
  features:
    - name: image
      dtype: image
    - name: label
      dtype:
        class_label:
          names:
            '0': real
            '1': fake
    - name: generator
      dtype:
        class_label:
          names:
            '0': Real
            '1': ADM
            '2': BigGAN
            '3': GLIDE
            '4': Midjourney
            '5': SD14
            '6': SD15
            '7': VQDM
            '8': Wukong
  splits:
    - name: train
      num_bytes: 6558732103
      num_examples: 28000
    - name: validation
      num_bytes: 1748767328
      num_examples: 7000
  download_size: 8359198723
  dataset_size: 8307499431
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: validation
        path: data/validation-*
license: cc-by-nc-sa-4.0
task_categories:
  - image-classification
language:
  - en

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)