Looks like this is using training data from the PASCAL VOC object detection challenge [1], which is the standard benchmark for evaluating object detection performance in computer vision.

Object detection is an extremely tough problem (some would say it is the computer vision problem ;-)), and while we've made a lot of progress in the past decade, the best methods are still terrible [2] -- average detection precision between 30-50%. For reference, most consumer applications require an AP of 90+% to be considered usable.

So if this is a completely automated solution, it's not going to be able to do much better, unless the creators can make massive (I mean orders-of-magnitude) improvements on the state-of-the-art.

But that being said, there are some applications where lower performance is acceptable. And if you add some manual verification, you could conceivably make this much better (with an increase in latency, though). Another possibility is to specialize on a certain type of input image (e.g., if you're a company taking photos in your warehouse, where all your photos look very similar and/or you can control the lighting and environment).

Still, I'm excited to see companies attempting to take object detection out to the real world. All the best to these guys!

[1] http://pascallin.ecs.soton.ac.uk/challenges/VOC/

[2] http://pascallin.ecs.soton.ac.uk/challenges/VOC/voc2011/resu...