  A key algorithm for understanding the world is material segmentation, which
assigns a label (metal, glass, etc.) to each pixel. We find that a model
trained on existing data underperforms in some settings and propose to address
this with a large-scale dataset of 3.2 million dense segments on 44,560 indoor
and outdoor images, which is 23x more segments than existing data. Our data
covers a more diverse set of scenes, objects, viewpoints and materials, and
contains a more fair distribution of skin types. We show that a model trained
on our data outperforms a state-of-the-art model across datasets and
viewpoints. We propose a large-scale scene parsing benchmark and baseline of
0.729 per-pixel accuracy, 0.585 mean class accuracy and 0.420 mean IoU across
46 materials.
