Maybe I’m missing something but does this blog post conclude with a service to do inference off device? Why explain all of the steps to inference on device if you’re offering an API to do cloud inference?
You didn't answer this question. Your "sorta-answer" suggests "yes", but the title "How to easily Detect Objects with Deep Learning on Raspberry Pi" suggests that your answer should be "no".
The title wasn't "How to easily Detect Objects with Deep Learning on Raspberry Pi with cloud services".
How am I suggesting "yes"? And how the title is suggesting answer as "no"? There are pros and cons of both methods. If you are doing inference on a remote place with no access to internet, off-device is out of question. We are just trying to give a complete landscape so that if someone has a use case and trying to come up with solution, it might be helpful. Depending on use case, can pick on-device or off-device.
Squeezing a full blown ML tutorial into a blog post is a tall order. Of course it’s too thin to really do it yourself without a lot of further research, you can’t really expect anything else. But I think the title leads people to expect more detail, hence posts like this and the owl cartoon above.
Maybe add a breakdown of performance for doing this local on a pi vs using the api? Would make it easier for people to weigh pros and cons.