YOLOV8 attitude estimation frame rate remains stable at around 30 frames per second
- Network architecture only
Backbone+Neck - The activation function is RELU
- FP16 reasoning
I have uploaded the detailed tutorial of this project on CSDN, with the link provided：https://blog.csdn.net/gaoxukkk888/article/details/144105601?spm=1001.2014.3001.5502
Currently, n models have been trained, so there is a lack of s models in the warehouse
https://github.com/Tencent/ncnn/releases
- Download ncnn-YYYYMMDD-android-vulkan.zip or build ncnn for android yourself
- Extract ncnn-YYYYMMDD-android-vulkan.zip into app/src/main/jni and change the ncnn_DIR path to yours in app/src/main/jni/CMakeLists.txt
https://github.com/nihui/opencv-mobile
- Download opencv-mobile-XYZ-android.zip
- Extract opencv-mobile-XYZ-android.zip into app/src/main/jni and change the OpenCV_DIR path to yours in app/src/main/jni/CMakeLists.txt
- Open this project with Android Studio, build it and enjoy!
- Android ndk camera is used for best efficiency
- Crash may happen on very old devices for lacking HAL3 camera interface
- All models are manually modified to accept dynamic input shape
- Most small models run slower on GPU than on CPU, this is common
- FPS may be lower in dark environment because of longer camera exposure time
https://github.com/FeiGeChuanShu/ncnn-android-yolov8
https://github.com/eecn/ncnn-android-yolov8-pose
https://github.com/ultralytics/ultralytics/tree/a007668e1fa8d5d586e6daa3924d65cfb139b8ac/examples/YOLOv8-NCNN-Python-Det-Pose-Cls-Seg-Obb https://blog.csdn.net/Rachel321/article/details/130381788
https://github.com/Rachel-liuqr/yolov8s-pose-ncnn
https://github.com/triple-Mu/ncnn-examples/blob/main/cpp/yolov8/src/triplemu-yolov8.cpp