[morsanu]

For most of our clients, we get to 100% accuracy. For some more difficult scenarios, a lower accuracy is ok for estimations (e.g. estimating the crop in agriculture).

It's also super easy to correct mistakes or to add items that are not visible in the image.

[BorisTheBrave]

You don't mean 100% accuracy, that means mistakes are impossible. Perhaps your error rate is very rare, but it's a bit concerning you don't attempt to quantify it.

[jmmcd]

I disagree. If we consider a single customer with a particular application, we probably imagine an iterative process like this: the customer supplies some sample images, the system gets a few wrong, they add special cases, fine-tune the hyperparameters, whatever, and after all this they literally get 100% on the customer's holdout data. If that happens for several customers then the GP statement is justified (no other number is possible for these customers).

Possibly you're thinking of a single error rate across all customers? For other customers, as stated, it's not 100%. But taking an average across multiple customers is not meaningful when some are counting pearls and some are counting crops.

[leucineleprec0n]

Suppose it depends on which part of the chain he attributes error, plausible lighting or poor positioning - anything that is an employee duty - does throw it off and then they scrub it. But yeah. Still.