This object-recognition dataset stumped the world's best computer vision models

Computer vision models have learned to identify objects in photos so accurately that some can outperform humans on some datasets. But when those same object detectors are turned loose in the real world, their performance noticeably drops, creating reliability concerns for self-driving cars and other safety-critical systems that use machine vision. In an effort to close this performance gap, a team of MIT and IBM researchers set out to create a very different kind of object-recognition dataset. It's called ObjectNet, a play on ImageNet, the crowdsourced database of photos responsible for launching much of the modern boom in artificial intelligence. Unlike ImageNet, which features photos taken from Flickr and other social media sites, ObjectNet features photos taken by paid freelancers. Objects are shown tipped on their side, shot at odd angles, and displayed in clutter-strewn rooms.