PINTO0309/HeadPoseEstimation-WHENet-yolov4-onnx-openvino

WHENet - ONNX, OpenVINO, TFLite, TensorRT, EdgeTPU, CoreML, TFJS, YOLOv4/YOLOv4-tiny-3L

HeadPoseEstimation-WHENet-yolov4-onnx-openvino

ONNX, OpenVINO, TFLite, TensorRT, EdgeTPU, CoreML, TFJS, YOLOv4/YOLOv4-tiny-3L

• PINTO Special Custom Model https://github.com/PINTO0309/DMHead

  184784102-089a82b9-765a-4431-bf33-43370b5c8174.mp4

1. Usage

$ git clone https://github.com/PINTO0309/HeadPoseEstimation-WHENet-yolov4-onnx-openvino
$ cd HeadPoseEstimation-WHENet-yolov4-onnx-openvino
$ wget https://github.com/PINTO0309/HeadPoseEstimation-WHENet-yolov4-onnx-openvino/releases/download/v1.0.3/saved_model_224x224.tar.gz
$ tar -zxvf saved_model_224x224.tar.gz && rm saved_model_224x224.tar.gz
$ wget https://github.com/PINTO0309/HeadPoseEstimation-WHENet-yolov4-onnx-openvino/releases/download/v1.0.4/whenet_1x3x224x224_prepost.onnx
$ mv whenet_1x3x224x224_prepost.onnx saved_model_224x224/

$ python3 demo_video.py

usage: demo_video.py \
[-h] \
[--whenet_mode {onnx,openvino}] \
[--device DEVICE] \
[--height_width HEIGHT_WIDTH]

optional arguments:
  -h, --help
      show this help message and exit
  --whenet_mode {onnx,openvino}
      Choose whether to infer WHENet with ONNX or OpenVINO. Default: onnx
  --device DEVICE
      Path of the mp4 file or device number of the USB camera. Default: 0
  --height_width HEIGHT_WIDTH
      {H}x{W} Default: 480x640

2. Reference

1. https://github.com/Ascend-Research/HeadPoseEstimation-WHENet
2. https://github.com/AlexeyAB/darknet
3. https://github.com/jkjung-avt/yolov4_crowdhuman
4. https://github.com/linghu8812/tensorrt_inference/tree/master/Yolov4
5. https://github.com/Tianxiaomo/pytorch-YOLOv4
6. https://github.com/PINTO0309/PINTO_model_zoo
7. https://github.com/PINTO0309/openvino2tensorflow
8. Exporting WHENet
9. Darknet to ONNX to OpenVINO to TensorFlow to TFLite and Others
10. Dual model head pose estimation. Fusion of SOTA models. 360° 6D HeadPose detection. All pre-processing and post-processing are fused together, allowing end-to-end processing in a single inference. 6DRepNet+WHENet

3. Special Custom Model Structure