Update readme.md

readme.md

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----
-license: apache-2.0
----
-# 🖼️ DiffSeg30k -- A multi-turn diffusion-editing dataset for localized AIGC detection
-
-A dataset for **segmenting diffusion-based edits** — ideal for training and evaluating models that localize edited regions and identify the underlying diffusion model
-
-## 📁 Dataset Usage
-- `xxxxxxxx.image.png`: Edited images. Each image may have undergone 1, 2, or 3 editing operations.
-- `xxxxxxxx.mask.png`: The corresponding mask indicating edited regions, where pixel values encode both the type of edit and the diffusion model used.
-
-Load images and masks as follows:
-
-```python
-from datasets import load_dataset
-dataset = load_dataset("Chaos2629/Diffseg30k", split="train")
-image, mask = dataset[0]['image'], dataset[0]['mask']
-```
-
-## 🧠 Mask Annotation
-
-Each mask is a grayscale image (PNG format), where pixel values correspond to a specific editing model. The mapping is as follows:
-
-| Mask Value | Editing Model                                        |
-|------------|------------------------------------------------------|
-| 0          | background                                           |
-| 1          | stabilityai/stable-diffusion-2-inpainting            |
-| 2          | kolors                                               |
-| 3          | stabilityai/stable-diffusion-3.5-medium              |
-| 4          | flux                                                 |
-| 5          | diffusers/stable-diffusion-xl-1.0-inpainting-0.1     |
-| 6          | glide                                                |
-| 7          | Tencent-Hunyuan/HunyuanDiT-Diffusers                 |
-| 8          | kandinsky-community/kandinsky-2-2-decoder-inpaint    |
-
-## 📌 Notes
-
-- Each edited image may be edited **multiple turns**, so the corresponding mask may contain several different **label values** ranging from 0 to 8.
-
-## 📄 License
-
-Apache-2.0
-
+---
+license: apache-2.0
+---
+# 🖼️ DiffSeg30k -- A multi-turn diffusion-editing dataset for localized AIGC detection
+
+A dataset for **segmenting diffusion-based edits** — ideal for training and evaluating models that localize edited regions and identify the underlying diffusion model
+
+## 📁 Dataset Usage
+- `xxxxxxxx.image.png`: Edited images. Each image may have undergone 1, 2, or 3 editing operations.
+- `xxxxxxxx.mask.png`: The corresponding mask indicating edited regions, where pixel values encode the diffusion model used.
+
+Load images and masks as follows:
+
+```python
+from datasets import load_dataset
+dataset = load_dataset("Chaos2629/Diffseg30k", split="train")
+image, mask = dataset[0]['image'], dataset[0]['mask']
+```
+
+## 🧠 Mask Annotation
+
+Each mask is a grayscale image (PNG format), where pixel values correspond to a specific editing model. The mapping is as follows:
+
+| Mask Value | Editing Model                                        |
+|------------|------------------------------------------------------|
+| 0          | background                                           |
+| 1          | stabilityai/stable-diffusion-2-inpainting            |
+| 2          | kolors                                               |
+| 3          | stabilityai/stable-diffusion-3.5-medium              |
+| 4          | flux                                                 |
+| 5          | diffusers/stable-diffusion-xl-1.0-inpainting-0.1     |
+| 6          | glide                                                |
+| 7          | Tencent-Hunyuan/HunyuanDiT-Diffusers                 |
+| 8          | kandinsky-community/kandinsky-2-2-decoder-inpaint    |
+
+## 📌 Notes
+
+- Each edited image may be edited **multiple turns**, so the corresponding mask may contain several different **label values** ranging from 0 to 8.
+
+## 📄 License
+
+Apache-2.0
+