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