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
$ 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.pyusage: 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- https://github.com/Ascend-Research/HeadPoseEstimation-WHENet
- https://github.com/AlexeyAB/darknet
- https://github.com/jkjung-avt/yolov4_crowdhuman
- https://github.com/linghu8812/tensorrt_inference/tree/master/Yolov4
- https://github.com/Tianxiaomo/pytorch-YOLOv4
- https://github.com/PINTO0309/PINTO_model_zoo
- https://github.com/PINTO0309/openvino2tensorflow
- Exporting WHENet
- Darknet to ONNX to OpenVINO to TensorFlow to TFLite and Others
- 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
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