Nick_J:

Yeah I’ll check that out - I was considering getting the coral dev board mini.


If you still need a Raspberry you can check also something like:

  
      

      gumstix.com
  

  
    

Gumstix Raspberry Pi CM4 PoE Smart Camera 1

  Whatever you're observing and categorizing, the CM4 PoE Smart Camera has the speed and power to do it with a single cable.  With the quad-core Raspberry Pi CM4 and Google Edge TPU, and power and Gigabit over Ethernet, the PoE Smart Camera is ready to...

  
    Price: USD 125
  

  

  
    
    
  

  



Might be worth adding that to the table footnote as to me (in my naivety) it seemed to imply that latency was achievable on CPU only.

I think that It is achievable on CPU as you can see in details from the benchmark section in:
https://tfhub.dev/tensorflow/lite-model/efficientdet/lite0/detection/metadata/1 1
I think that you problem on your MacOS is that the X86 experience Is generally optimized for the standard TF runtime but you can still try to achieve performance using TFlite with XNNPACK, if what your need for your model is already covered by what we have in the XNNPACK  delegate, as it has ops fusion and SSE/AVX X86 kernels.