ncnn VS rife-ncnn-vulkan

ncnn uses Vulkan for GPU acceleration, I've seen it used in a few projects to get AMD hardware support.

https://github.com/Tencent/ncnn

They're using NCNN to package the model. Have a look. https://github.com/Tencent/NCNN

Hi. Here is my prefered android app for realtime objet detection: https://github.com/nihui/ncnn-android-nanodet ; https://github.com/Tencent/ncnn contains a lot of android demo app for a lot of models.

ncnn home page: https://github.com/Tencent/ncnn

ncnn

Yes, Android devices tend to have bigger RAMs, making running 1024x1024 possible (this is not possible at all on iPhones, which could peak around 5GiB memory with my current implementation, some serious engineering required to bring that down on iPhone devices). The problem is I am not sure about speed. I would likely switch to NCNN (https://github.com/Tencent/ncnn) as the backend which have a decent Vulkan computing kernel support. It is definitely a possibility and there is a path to do that.

As the leading open-source inference framework in China and in the world, what we like are its almost zero cost cross-platform capability, high inference speed, and minimal deployment volume. (Project address: https://github.com/Tencent/ncnn)

If you’re not planning on training the neural net on an embedded device and just do inference, this might interest you: https://github.com/Tencent/ncnn

flowframes is just an interface for interpolation models, you can still use rige-ncnn directly if you are familiar with the stuff. https://github.com/nihui/rife-ncnn-vulkan

Messing around with https://github.com/nihui/rife-ncnn-vulkan

Compressing the videos has become quite a project that involves the seed_travel script, a little imagemagick, upscaling with realSR, an absolute ton of interpolation with RIFE, and the swiss army knife of video tools, ffmpeg.

not SD based, but maybe this helps? https://github.com/nihui/dain-ncnn-vulkan https://github.com/nihui/rife-ncnn-vulkan