Deep Learning models have amazing capacity, getting better with more data seemingly without limit. To get a well-functioning model, it is not enough to have large amounts of data, you also need accurate annotations. Although large amounts of data will help the model resolve data inconsistencies between different annotations, humans can still make repeated mistakes that become ingrained in the model.

We’ve had an incredible group of people join Scale—and we’re still hiring! We wanted to take a moment to follow up on this post and call out the latest team members that have joined the Engineering, Product and Design teams at Scale:

Scale AI is pleased to announce the full release of nuScenes by Aptiv —a large-scale open source dataset for autonomous driving. Scale AI was involved as Aptiv's data labeling partner on this initiative. On March 28, 2019, Aptiv announced the full dataset release of nuScenes, an initiative intended to broadly support research into computer vision and autonomous driving by AV innovators and academic researchers to further advance the mobility industry.

Here at Scale, one of the image annotation services we offer is Cuboid Annotation, which annotates your two-dimensional images with projections of cuboids enclosing objects such as cars, trucks, pedestrians, traffic cones, you name it. With some additional information, we can turn those two-dimensional box annotations into full, three-dimensional boxes, with height, width, depth, rotation, and relative positioning info.