| --- |
| pretty_name: ChessAI Data — Chinese Chess Piece Detection |
| language: |
| - vi |
| size_categories: |
| - 1K<n<10K |
| task_categories: |
| - object-detection |
| tags: |
| - chess |
| - chinese-chess |
| - xiangqi |
| - co-tuong |
| - object-detection |
| - bounding-box |
| - anylabeling |
| authors: |
| - Viet-Anh Nguyen |
| --- |
| |
| # ChessAI Data — Chinese Chess Piece Detection |
|
|
| A bounding-box detection dataset for **Chinese Chess** (Cờ tướng / 象棋), labeled with [AnyLabeling](https://github.com/vietanhdev/anylabeling). Built to train piece-recognition models for chessboard-state extraction from photos. |
|
|
| ## Dataset summary |
|
|
| - **Total annotated images:** 1,747 (per-image AnyLabeling JSON in `data/combined_data/`) |
| - **COCO-format split:** 872 images, 18,790 bounding boxes (`data/annotations.json`) |
| - **Classes:** 7 (the standard Xiangqi piece set) |
| - **Total size:** ~280 MB (images + annotations) |
|
|
| ## Classes |
|
|
| Vietnamese piece names are used throughout. Counts below are from the COCO split. |
|
|
| | ID | Label (VN) | Piece | Boxes | |
| |---|---|---|---| |
| | 1 | `xe` | Chariot (rook) | 2,264 | |
| | 2 | `ma` | Horse (knight) | 2,357 | |
| | 3 | `tuong` | Elephant (bishop-like)| 2,350 | |
| | 4 | `si` | Advisor (palace guard)| 2,375 | |
| | 5 | `vua` | General (king) | 1,244 | |
| | 6 | `phao` | Cannon | 2,369 | |
| | 7 | `tot` | Soldier (pawn) | 5,831 | |
|
|
| ## Files |
|
|
| - **`data/combined_data/`** — paired `.jpg` + `.json` files in [labelme](https://github.com/wkentaro/labelme) / AnyLabeling format. Each `.json` has a `shapes[]` array with `label`, `points` (top-left and bottom-right corners), and `shape_type: "rectangle"`. |
| - **`data/annotations.json`** — COCO-format export covering 872 images and 18,790 boxes, ready for use with detection libraries that expect COCO. |
| - **`data/data_01/`, `data/data_02/`** — raw images grouped by capture session. |
| - **`make_data.sh`** — pipeline that produces the COCO export from the raw + per-image annotation pairs. |
| |
| ## Quick start — load the COCO split |
| |
| ```python |
| from huggingface_hub import hf_hub_download |
| import json |
| |
| path = hf_hub_download( |
| repo_id="vietanhdev/chessai-data", |
| filename="data/annotations.json", |
| repo_type="dataset", |
| ) |
| with open(path) as f: |
| coco = json.load(f) |
| |
| print(len(coco["images"]), "images,", len(coco["annotations"]), "boxes") |
| print("classes:", [c["name"] for c in coco["categories"]]) |
| ``` |
| |
| To download images alongside, use `snapshot_download` with `allow_patterns=["data/combined_data/*"]`. |
| |
| ## Reproducing the dataset |
| |
| ```bash |
| conda create -n chessai-dataprep python=3.9 |
| conda activate chessai-dataprep |
| pip install -r requirements.txt |
| # After labeling raw images in data/data_01 and data/data_02 with AnyLabeling: |
| bash make_data.sh |
| ``` |
| |
| ## Source code |
| |
| Upstream repo with preprocessing scripts: <https://github.com/nrl-ai/chessai-data> |
| |
| ## Citation |
| |
| ```bibtex |
| @misc{nguyen2024chessai, |
| author = {{Viet-Anh NGUYEN (Andrew)}}, |
| title = {ChessAI Data — Chinese Chess piece detection dataset}, |
| year = {2024}, |
| publisher = {Hugging Face}, |
| doi = {10.57967/hf/2812}, |
| url = {https://huggingface.co/datasets/vietanhdev/chessai-data} |
| } |
| ``` |
| |
