vision_transformer_tf VS coral-pi-rest-server

Hi r/learnmachinelearning, I am done implementing the paper AN IMAGE IS WORTH 16X16 WORDS: TRANSFORMERS FOR IMAGE RECOGNITION AT SCALE, popularly known as the Vision Transformer paper. Using my implementation any vision transformer model can be finetuned pretty easily with any custom dataset, Converting weights to TensorFlow Lite is also supported. My codebase is also very straightforward to understand and debug. One can learn how the vision transformer works internally by debugging the whole pipeline. Link to the GitHub Project: https://github.com/TheTensorDude/vision_transformer_tf

The GitHub link to the project can be found here.

My implementation: GitHub Link

My implementation: https://github.com/TheTensorDude/vision_transformer_tf

That said, you can always look at something like https://coral.ai/products/accelerator/ to help with the performance you need.

Should only be ~$60 https://coral.ai/products/accelerator/

The Coral USB accelerator might be of interest if you want to do some light ML with a low power budget.

However, a pi doesn't have the strength to run something like Llama.cpp, of course, so I've been considering using something like the Coral USB Accelerator (https://coral.ai/products/accelerator). As I've been learning more about it, it seems to be very geared towards TensorFlow Lite models. But whisper.cpp and Llama.cpp use ggml models.

Recently found this thing, though I haven't found a use case for me.