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---
license: cc-by-nc-4.0
pipeline_tag: object-detection
---
# 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.
## Disclaimer
Commercial use is not permitted. |