| --- |
| 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)](https://www.kaggle.com/datasets/yangsangtai/tiny-genimage) |
