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README.md

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-license: cc-by-nc-4.0
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+---
+license: cc-by-nc-4.0
+---
+---
+license: cc-by-nc-4.0
+---
+# Hand Gesture YOLOv8 (ONNX INT8)
+## Overview
+This repository contains a YOLOv8 nano model trained for real-time hand gesture detection and optimized for edge and mobile devices.
+
+The model is exported to ONNX format and quantized to INT8 for efficient on-device inference.
+
+## Model Details
+- **Architecture:** YOLOv8n (Nano)
+- **Task:** Hand gesture detection
+- **Format:** ONNX
+- **Precision:** INT8 (W8A8)
+- **Input size:** 640 × 640
+- **Classes:** 
+  - hand-gestures  
+  - left  
+  - right  
+  - stop  
+  - mvefrd  
+
+## Included Assets
+- `hand_gesture_yolo_onnx_w8a8.zip`
+  - `model.onnx`
+  - `tool_versions.yaml`
+  - `data.yaml`
+
+Each ZIP follows the Qualcomm AI Hub Community contribution guidelines.
+
+## Training Summary
+- Dataset size: 330 images
+- Training platform: Kaggle (NVIDIA Tesla T4)
+- Framework: Ultralytics YOLOv8
+- Quantization: ONNX INT8
+
+## Intended Use
+- Edge AI applications
+- Gesture-based interfaces
+- Touchless controls
+- Academic and research purposes
+
+## Limitations
+- The model is trained on a small dataset and may not generalize to all lighting conditions or camera angles.
+- Performance may vary depending on device and runtime.
+
+## License
+This model is released under the **CC BY-NC 4.0** license.
+
+**Commercial use is not permitted.**
+
+## Disclaimer
+This is a community-contributed model and is not officially verified or supported by Qualcomm. Use at your own discretion.