---
library_name: ultralytics
pipeline_tag: object-detection
tags:
- yolo11
- pytorch
- object-detection
- keypoint-detection
- chess
- computer-vision
license: agpl-3.0
language:
- en
base_model:
- Ultralytics/YOLO11
metrics:
- mAP
datasets:
- surawut/chessboard-dataset-yolo
- surawut/chess-pieces-dataset-yolo
---

# Chess Move Tracking Models (YOLO11)

This repository contains fine-tuned models for a Chess Move Tracking pipeline.

## Models

1.  **`models/yolo11s_pose_chessboard.pt`**:
    * **Architecture:** YOLO11s-pose
    * **Task:** Keypoint Detection (Chessboard Corner Localization).
    * **Classes:** 1 class (`chessboard`).
    * **Keypoints:** `a1`, `h1`, `a8`, `h8`.
    * **Input:** Raw video frame (rotated/occluded).

    
3.  **`models/yolo11m_pieces.pt`**:
    * **Architecture:** YOLO11m
    * **Task:** Object Detection (Chess Pieces Detection).
    * **Classes:** 13 classes (`Hand`, `bB`, `bK`, `bN`, `bP`, `bQ`, `bR`, `wB`, `wK`, `wN`, `wP`, `wQ`, `wR`).
    * **Input:** Warped top-down chessboard image (640x640).


## Usage
You can use these models directly with the [Ultralytics](https://github.com/ultralytics/ultralytics) library.

### 1. Installation

First, install the required library:

```bash
pip install -U ultralytics
```

### 2. Using the Chessboard Corner Localization Model
This model detects the 4 semantic corners of the chessboard (a1, h1, a8, h8) to help with perspective warping.

```python
from ultralytics import YOLO

# Load the model directly from Hugging Face
model = YOLO("https://huggingface.co/surawut/chess-move-tracking-yolo11/resolve/main/models/yolo11s_pose_chessboard.pt")
# Load an image (raw frame)
image_path = "path/to/raw_frame.jpg"
# Run inference
results = model(image_path)
```

### 3. Using the Chess Pieces Detection Model
This model detects the 12 chess pieces + hand on a warped (640x640) top-down view of the board.

```python
from ultralytics import YOLO

# Load the model directly from Hugging Face
model = YOLO("https://huggingface.co/surawut/chess-move-tracking-yolo11/resolve/main/models/yolo11m_pieces.pt")
# Load a chessboard image
warped_image_path = "path/to/warped_board.jpg"
# Run inference
results = model(warped_image_path)