What vision model would you build if you had infinite data?

Question

We've been building deep learning vision models in simulation, without any real labeled data... and it's working!Have you recently worked on a vision model for which data was very difficult to source?* Could be a rare event that happens once a week?* Could be too difficult to get precise labels? (6D-pose, cuboids)* Could be a budget & speed of iteration issue..Really looking to broaden our understanding of applications beyond robotics & manufacturing.Here is a recent example from our test bench (30 sec): https://youtu.be/D33acz5mI10* Mask R-CNN trained on 10,000 samples from SBX Quicksynth with 3D scans from YCB Benchmarks, running on RealSense SR305 RGB channel.* 3D scans from: https://ycbbenchmarks.com/* Data from: https://sbxrobotics.com, built on UE4

plaidfuji · Accepted Answer

Possibly not relevant to your current simulation environment, but if your tech is generally focused on extrapolating from simulation data, I think there&rsquo;s a market for this kind of thing in document parsing. Extracting tables, charts, text, etc from images, PDFs... paired with a document simulator could be powerful.

brianjunyinchan · Answer

Not involved in this area myself, but maybe some application of wildlife monitoring?