Update README.md

README.md

@@ -42,4 +42,100 @@ dataset_info:
     num_examples: 125026
   download_size: 32032878953
   dataset_size: 29870625563
+license: apache-2.0
+task_categories:
+- image-classification
+language:
+- en
 ---
+
+# AIGC Detection Benchmark Dataset
+
+## 📝 Dataset Description
+
+**Dataset Summary**
+
+The AIGC Detection Benchmark Dataset is a high-quality collection of images and associated metadata designed to benchmark models for detecting and identifying the source of artificially generated content. The dataset contains a mix of real-world images and images generated by a wide array of prominent AI models, including diffusion models (like Stable Diffusion, DALL-E 2, Midjourney, ADM) and GANs (like BigGAN, StyleGAN, ProGAN).
+
+Each image is meticulously labeled under two categories, enabling researchers to tackle two distinct, high-value computer vision tasks: binary real/fake classification and multi-class source model identification. **Note: This specific version of the dataset is designed exclusively for testing and evaluation purposes, with all data consolidated into a single test split.**
+
+**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.| 18 (Real, ADM, BigGAN, CycleGAN, DALLE2, GauGAN, GLIDE, Midjourney, ProGAN, SD14, SD15, SDXL, StarGAN, StyleGAN, StyleGAN2, VQDM, WhichFaceIsReal, Wukong) |
+
+**Languages**
+
+The descriptive text, including all captions, is in English (en).
+
+## 🗂️ Data Splits
+
+All instances have been merged into a single test split to serve strictly as an evaluation benchmark.
+
+|**Split**|**Number of Instances**|**Notes**|
+|---|---|---|
+|**test**|125,026|Used exclusively for final, unbiased model evaluation and benchmarking.|
+
+## 💾 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**|`3`|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 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.                                      |
+| **CycleGAN**        | `3`       | Generated by CycleGAN.                                    |
+| **DALLE2**          | `4`       | Generated by OpenAI's DALL-E 2.                           |
+| **GauGAN**          | `5`       | Generated by GauGAN (SPADE).                              |
+| **GLIDE**           | `6`       | Generated by GLIDE.                                       |
+| **Midjourney**      | `7`       | Generated by Midjourney.                                  |
+| **ProGAN**          | `8`       | Generated by ProGAN (Progressive GAN).                    |
+| **SD14**            | `9`       | Generated by Stable Diffusion 1.4.                        |
+| **SD15**            | `10`      | Generated by Stable Diffusion 1.5.                        |
+| **SDXL**            | `11`      | Generated by Stable Diffusion XL.                         |
+| **StarGAN**         | `12`      | Generated by StarGAN.                                     |
+| **StyleGAN**        | `13`      | Generated by StyleGAN.                                    |
+| **StyleGAN2**       | `14`      | Generated by StyleGAN2.                                   |
+| **VQDM**            | `15`      | Generated by Vector Quantized Diffusion Model.            |
+| **WhichFaceIsReal** | `16`      | Real human face sourced from the WhichFaceIsReal dataset. |
+| **Wukong**          | `17`      | Generated by the Wukong diffusion model.                  |
+
+## 🔗 Sources
+
+- **Original dataset**: [Ekko-zn/AIGCDetectBenchmark](https://github.com/Ekko-zn/AIGCDetectBenchmark).