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.onnxtool_versions.yamldata.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.
