Add dataset card (test split only)

README.md

@@ -1,36 +1,123 @@
 ---
-dataset_info:
-  features:
-  - name: image_id
-    dtype: int64
-  - name: file_name
-    dtype: string
-  - name: image
-    dtype: image
-  - name: width
-    dtype: int32
-  - name: height
-    dtype: int32
-  - name: bboxes
-    list:
-      list: float32
-  - name: areas
-    list: float32
-  - name: category_ids
-    list: int32
-  - name: category_names
-    list: string
-  - name: iscrowd
-    list: int32
-  splits:
+license: cc-by-nc-nd-4.0
+task_categories:
+  - object-detection
+  - image-segmentation
+language:
+  - en
+  - zh
+tags:
+  - document-layout-analysis
+  - document-understanding
+  - coco-format
+  - ocr
+pretty_name: M6Doc (Test Set)
+size_categories:
+  - 1K<n<10K
+splits:
   - name: test
-    num_bytes: 4490094812
     num_examples: 2724
-  download_size: 4055823520
-  dataset_size: 4490094812
-configs:
-- config_name: default
-  data_files:
-  - split: test
-    path: data/test-*
 ---
+
+# M6Doc — Test Set
+
+> **⚠️ This dataset currently contains the test split only.**
+> The training and validation splits require an approved application from the dataset authors (see [Access](#access) below).
+
+## Dataset Summary
+
+**M6Doc** is a large-scale, multi-format document layout analysis dataset presented at **CVPR 2023** by the Deep Learning and Visual Computing Lab of South China University of Technology (SCUT).
+
+It covers **7 document types**, **3 formats**, and **74 fine-grained annotation categories** — making it one of the most comprehensive datasets for modern document layout analysis.
+
+| Split | Images | Annotations | Status |
+|-------|--------|-------------|--------|
+| **test** | **2,724** | **~71K** | ✅ Available here |
+| train | ~5,450 | ~142K | 🔒 Requires application |
+| validation | ~910 | ~23K | 🔒 Requires application |
+
+## Dataset Features
+
+Each example contains:
+
+| Column | Type | Description |
+|--------|------|-------------|
+| `image` | `Image` | Document image (viewable in browser) |
+| `image_id` | `int64` | COCO image ID |
+| `file_name` | `string` | Original filename |
+| `width` | `int32` | Image width in pixels |
+| `height` | `int32` | Image height in pixels |
+| `bboxes` | `List[List[float]]` | Bounding boxes `[x, y, w, h]` per instance |
+| `areas` | `List[float]` | Area of each annotated instance |
+| `category_ids` | `List[int]` | Category ID per instance (1–74) |
+| `category_names` | `List[str]` | Category label per instance |
+| `iscrowd` | `List[int]` | Crowd flag per instance |
+
+## Document Categories
+
+The dataset spans **7 document types**:
+
+| Type | Proportion |
+|------|-----------|
+| Textbook | 23% |
+| Test Paper | 22% |
+| Magazine | 22% |
+| Scientific Article | 11% |
+| Newspaper | 11% |
+| Note (handwritten) | 5.5% |
+| Book (photographed) | 5.5% |
+
+And **3 document formats**: PDF (64%), Scanned (31%), Photographed (5%).
+
+## Annotation Categories
+
+74 detailed layout element categories including (but not limited to):
+
+`text`, `title`, `figure`, `figure_caption`, `table`, `table_caption`, `header`, `footer`, `page_number`, `footnote`, `formula`, `reference`, `list`, `abstract`, `author`, `affiliation`, `equation`, `sidebar`, `advertisement`, ...
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("tuandunghcmut/M6Doc")
+print(ds)
+# DatasetDict({ test: Dataset({features: [...], num_rows: 2724}) })
+
+# View a sample
+sample = ds["test"][0]
+sample["image"]          # PIL Image
+sample["category_names"] # list of label strings
+sample["bboxes"]         # list of [x, y, w, h]
+```
+
+## Access
+
+**Test set**: Freely available here.
+
+**Train & Validation sets**: Require submitting an application form to the original authors:
+1. Fill in the [Application Form](https://github.com/HCIILAB/M6Doc/blob/main/Application_Form/Application-Form-for-Using-M6Doc.docx)
+2. Email to [lianwen.jin@gmail.com](mailto:lianwen.jin@gmail.com) or [eelwjin@scut.edu.cn](mailto:eelwjin@scut.edu.cn)
+3. Include 1–2 recent publications in OCR / document analysis
+
+## License
+
+This dataset is released under [CC BY-NC-ND 4.0](https://creativecommons.org/licenses/by-nc-nd/4.0/) — **non-commercial research use only**.
+
+## Citation
+
+```bibtex
+@InProceedings{Cheng_2023_CVPR,
+    author    = {Cheng, Hiuyi and Zhang, Peirong and Wu, Sihang and Zhang, Jiaxin and Zhu, Qiyuan and Xie, Zecheng and Li, Jing and Ding, Kai and Jin, Lianwen},
+    title     = {M6Doc: A Large-Scale Multi-Format, Multi-Type, Multi-Layout, Multi-Language, Multi-Annotation Category Dataset for Modern Document Layout Analysis},
+    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
+    month     = {June},
+    year      = {2023},
+    pages     = {15138-15147}
+}
+```
+
+## Links
+
+- [Original GitHub Repository](https://github.com/HCIILAB/M6Doc)
+- [CVPR 2023 Paper](https://openaccess.thecvf.com/content/CVPR2023/html/Cheng_M6Doc_A_Large-Scale_Multi-Format_Multi-Type_Multi-Layout_Multi-Language_Multi-Annotation_Category_Dataset_CVPR_2023_paper.html)