Fine-tuned YOLOv8 model for real-time chess piece detection in 2D images. Trained on 50,000+ images over 120 epochs with enhanced generalization for real board images, videos, and app screenshots.

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Model Details

Base Model: YOLOv8 (Ultralytics)
Task: Object Detection (Chess Pieces, Chess Board)
Training Data: 50,000+ annotated chess board images
Training Epochs: 120
Class Set:
- 0: board
- 1: white_king
- 2: white_queen
- 3: white_rook
- 4: white_bishop
- 5: white_knight
- 6: white_pawn
- 7: black_king
- 8: black_queen
- 9: black_rook
- 10: black_bishop
- 11: black_knight
- 12: black_pawn

Input Requirements

Image Format: RGB (colored), JPG/PNG/BMP
Resolution: Exactly 640 × 640 pixels
Preprocessing: Images must be frame-padded to reach 640 × 640 resolution, not stretched/distorted. Preserve board aspect ratio in center frame.
Order: Always RGB, batch shape [N, 640, 640, 3]
Strict input: Other sizes or non-padded frames may result in inaccurate detections.

Usage Example

Python (Ultralytics)

python

from ultralytics import YOLO
from PIL import Image

model = YOLO('NAKSTStudio/yolov8-chess-piece-detection')

img = Image.open('chess_board.jpg').convert('RGB')
img640 = img.resize((640, 640), Image.LANCZOS)  # manually pad if needed
results = model(img640)
for result in results:
    for box in result.boxes:
        cid = int(box.cls[0])
        print(f"{cid}: {model.names[cid]}", box.xyxy[0].tolist())

Classes

Class ID	Name
0	board
1	white_king
2	white_queen
3	white_rook
4	white_bishop
5	white_knight
6	white_pawn
7	black_king
8	black_queen
9	black_rook
10	black_bishop
11	black_knight
12	black_pawn

Real-World Application: Chess Lab

Used in Chess Lab App by NAKST Studio for:

🎥 Video-to-board analysis (video frames to FEN)
🤖 Offline Stockfish chess engine board analysis

See assets/images/chess_lab_screenshot.png for visual demo

Example Detection Results

Training Configuration

Image size: 640 × 640, frame padded
Augmentation: rotation, blur, brightness, mosaic
Split: 80% train, 10% val, 10% test
Classes: 13 (includes board)

Export Formats

PyTorch (best.pt)
TFLite (FP16 for mobile)
- TFLite (FP32 for better precision)
- ONNX (Cross Platform)

License

AGPL-3.0 — free/open for research & open apps, commercial usage with source sharing.

Citation

bibtex

@model{yolov8_chess_detection_2025,
  title={YOLOv8 Chess Piece Detection: Real-Time Chess Vision},
  author={NAKST Studio},
  year={2025},
  howpublished={Hugging Face Hub},
  url={https://huggingface.co/NAKSTStudio/yolov8-chess-piece-detection}
}

Last updated 10 November 2025. Powered by 💙️ from NAKST Studio.

NAKSTStudio/yolov8m-chess-piece-detection

YOLOv8 Chess Piece Detection ♟️🔍

By NAKST Studio