Upload yolo segmentation model

README.md

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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}
+}
+```

model.pt

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+size 23849524