Meta's open vision models cut a month of scientific image labeling to 15 minutes at Berkeley Lab

In a July 21, 2026 post, Meta detailed how its open-source SAM 3 and DINOv3 vision models power SYNAPS-I, a Department of Energy project led by Lawrence Berkeley National Laboratory that automates segmentation of the petabytes of scientific imaging data DOE facilities generate. In one measured case, annotating grapevine drought-resilience micro-CT scans dropped from roughly a month of expert manual work per dataset to about 15 minutes, running on 300 A100 GPUs on secure government infrastructure rather than external cloud services.

What it means A real, measured deployment — not a benchmark claim — showing open vision models replacing weeks of expert labeling with minutes, a concrete reference for any team weighing open models against paid annotation.