Update model weights (CC-BY-4.0)

LICENSE

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+CC-BY-4.0

README.md

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----
-license: cc-by-4.0
----
+---
+license: cc-by-4.0
+library_name: ultralytics
+pipeline_tag: object-detection
+tags:
+  - yolo
+  - object-detection
+  - tic-tac-toe
+---
+
+# Tic-tac-toe Cell Detector (YOLOv8n)
+
+## Overview
+- YOLOv8n model that detects occupancy per 3x3 cell (empty / white_circle / black_cross).
+- Output: bounding boxes and classes for 9 cells. The included script can overlay cell indices and labels on the image.
+- Intended input: top-down tic-tac-toe board images (matching this repo's synthetic/real data distribution).
+
+## License
+- Model weights: CC-BY-4.0
+- Code: AGPL-3.0 (per Ultralytics dependency)
+- Source code for data generation/training/inference: https://github.com/guren-kaina/AMD_Robotics_Hackathon_2025_ProjectTemplate/tree/main/mission2/code/tic_tac_toe_overlay
+
+## Usage
+```bash
+pip install ultralytics
+python - <<'PY'
+from ultralytics import YOLO
+model = YOLO("models/train/weights/best.pt")  # weights from this repo
+res = model("your_input.jpg", imgsz=640, conf=0.25)
+print(res[0].boxes.cls, res[0].boxes.xyxy)
+res[0].save(filename="overlay.jpg")  # visualization
+PY
+```
+- Class IDs: 0=empty_cell, 1=white_circle_cell, 2=black_cross_cell
+- The included `main.py` runs preprocessing, cell index drawing, and JSON export.
+
+## Training data
+- Synthetic: gray background + white grid, includes low contrast/blur/noise and O/X distractors. Default generation train 1000 / val 200.
+- Real: `real/images` and YOLO-format labels `real/labels` (no PII).
+
+## Training setup
+- Base: Ultralytics YOLOv8n
+- Image size: default 640
+- Epochs: default 20
+- Options: `--preprocess-train` for contrast augmentation, `--real-data` to mix real data into training
+- Weights saved to `models/train/weights/best.pt`
+
+## Limitations and notes
+- Accuracy may drop with oblique views or extreme lighting.
+- Only 3 classes; out-of-board objects or different token shapes are unsupported.
+- For new domains, re-label and retrain.

best.pt

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+size 6232810