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---
license: apache-2.0
tags:
- computer-vision
- chess
- yolo
- segmentation
- instance-segmentation
model-index:
- name: chess_board_segmentation
results: []
---
# chess_board_segmentation
ChessBoard segmentation model trained with YOLO
## Model Details
- **Model Type**: YOLO Segmentation
- **Task**: instance-segmentation
- **License**: apache-2.0
## Usage
### Loading the Model
```python
from src.chess_board_detection.yolo.segmentation.segmentation_model import ChessBoardSegmentationModel
# Load the model
model = ChessBoardSegmentationModel(model_path="path/to/downloaded/model.pt")
# Get polygon coordinates for a chessboard
polygon_info, is_valid = model.get_polygon_coordinates("path/to/chessboard_image.jpg")
if is_valid:
print(f"Detected chessboard polygon: {polygon_info}")
# Extract corners from the segmentation
corners = model.extract_corners_from_segmentation(
"path/to/chessboard_image.jpg",
polygon_info
)
print(f"Extracted corners: {corners}")
# Visualize results
model.plot_eval("path/to/chessboard_image.jpg", show=True)
```
### Direct YOLO Usage
```python
from ultralytics import YOLO
# Load the model
model = YOLO("path/to/downloaded/model.pt")
# Run segmentation
results = model("path/to/chessboard_image.jpg")
# Get masks and polygons
for result in results:
if result.masks is not None:
for mask in result.masks:
polygon = mask.xy[0] # Polygon coordinates
print(f"Polygon points: {polygon}")
```
### Training Data Format
This model expects YOLO segmentation format with polygon annotations:
```yaml
# data.yaml
train: path/to/train/images
val: path/to/val/images
nc: 1
names: ['chessboard']
```
With corresponding label files containing polygon coordinates:
```
# labels/image.txt
0 x1 y1 x2 y2 x3 y3 x4 y4 ... # normalized coordinates
```
## Training
This model was trained using the ChessBoard Segmentation training pipeline:
```bash
python src/chess_board_detection/yolo/segmentation/train_segmentation.py \
--data data/chessboard_segmentation/chess-board-3/data.yaml \
--epochs 100 \
--batch 16 \
--pretrained-model yolov8s-seg.pt
```
## Model Performance
<!-- Add performance metrics here after training -->
## Citation
If you use this model in your research, please cite:
```bibtex
@misc{dopaul_chess_board_segmentation,
title={chess_board_segmentation},
author={dopaul},
year={2024},
publisher={Hugging Face},
url={https://huggingface.co/dopaul/chess_board_segmentation}
}
```
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