ncnn-android-yolov8-pose

YOLOV8 attitude estimation frame rate remains stable at around 30 frames per second

optimization

• Network architecture only Backbone + Neck
• The activation function is RELU
• FP16 reasoning

Detailed Tutorial

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

how to build and run

step1

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

step2

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

step3

• Open this project with Android Studio, build it and enjoy!

some notes

• 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

screenshot

reference

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

https://zhuanlan.zhihu.com/p/622596922