Berkeley Vision and Learning Center
@bvlc • Autonomous Perception Research. Use this route to separate flagship concentration from portfolio breadth before you treat a publisher as broadly strong.
Portfolio concentration
100%
Top three share
Shows whether the organization is driven by one breakout repo or several visible projects.
Breadth
4 repos
Visible snapshot
0 repositories updated in the last 90 days.
Leading language
C++
Portfolio mix
C++ (2), Shell (1), Unknown (1)
Average size
8.7K
Stars per repository
Useful for distinguishing one flagship-heavy publisher from a repeatable portfolio.
Updated: 2015-07-21(4053d ago)GitHub API fallback4 repositories
100%
of the visible star count comes from this organization's top three repositories.
8.7K
stars per repository in this same snapshot.
C++
is the most common language here, with 0 repositories updated in the last 90 days.
Why this rank
This organization stands out because one flagship repo drives 99% of its visible star count.
Flagship share 99%Breakout repo: caffe
Organization pages work best when you separate portfolio breadth from flagship concentration. In Berkeley Vision and Learning Center's case, the visible top three repositories account for about 100% of total stars in this snapshot, which helps explain whether the organization is known for one breakout project or for a broader repeatable portfolio.
The dominant language mix here is C++ (2), Shell (1), Unknown (1). That makes this page useful not just for popularity checks, but also for seeing what technical shape an organization's public ecosystem actually has.
Top Repositories
| # | Repository | Language | Stars |
|---|---|---|---|
| 1 | bvlc/caffe Caffe: a fast open framework for deep learning. | C++ | 34.6K |
| 2 | bvlc/caffe-tutorial DIY Deep Learning for Vision: a Hands-On Tutorial with Caffe | Shell | 270 |
| 3 | bvlc/raptor Code basis for the Realtime adAPtative detecTOR (first published and referred to at IEEE ICRA 2014) | C++ | 60 |
| 4 | bvlc/DPD Deformable part descriptor project page | 30 |
Next step after the organization read
Open a flagship repository, compare a couple of portfolio leaders, or return to the organization map when you want a broader concentration read.
Learn and methodology
Keep trust-building context reachable, but behind the first data read instead of ahead of it.
How to read this organization snapshot
Total stars are useful as a discovery signal, but they do not tell you whether a team maintains every repository equally. Pair this page with release cadence, maintainer activity, and the flagship concentration shown above before making adoption decisions.
For broader background on GitStar's ranking logic and editorial guidance, see Methodology & Editorial Standards.