| # Palm detector from MediaPipe Handpose | |
| This model detects palm bounding boxes and palm landmarks, and is converted from Tensorflow-JS to ONNX using following tools: | |
| - tfjs to tf_saved_model: https://github.com/patlevin/tfjs-to-tf/ | |
| - tf_saved_model to ONNX: https://github.com/onnx/tensorflow-onnx | |
| - simplified by [onnx-simplifier](https://github.com/daquexian/onnx-simplifier) | |
| Also note that the model is quantized in per-channel mode with [Intel's neural compressor](https://github.com/intel/neural-compressor), which gives better accuracy but may lose some speed. | |
| ## Demo | |
| Run the following commands to try the demo: | |
| ```bash | |
| # detect on camera input | |
| python demo.py | |
| # detect on an image | |
| python demo.py -i /path/to/image | |
| ``` | |
| ### Example outputs | |
|  | |
| ## License | |
| All files in this directory are licensed under [Apache 2.0 License](./LICENSE). | |
| ## Reference | |
| - MediaPipe Handpose: https://github.com/tensorflow/tfjs-models/tree/master/handpose | |