I have recently been doing some CRF inference for an object recognition task and needed a good ol' Max-Product Loopy Belief Propagation. I revived my old MATLAB-based implementation that grew out of a Probabilistic Graphical Models homework. Even though I had vectorized the code and had tested it for correctness -- would my own code be good enough on problems involving thousands of nodes and arities as high as 200? It was the first time I ran my own code on such large problems and I wasn't surprised when it took several minutes for those messages to stop passing.
I tried using Talya Meltzer's MATLAB package for inference in Undiracted Graphical Models. It is a bunch of MATLAB interfaces to efficient C code. Talya is Yair Weiss's PhD student (so that basically makes her an inference expert).
It was nice to check my old homework-based code and see the same beliefs for a bunch of randomly generated binary planar-grid graphs. However, for medium sized graphs her code was running in ~1 second while my homework code was taking ~30 seconds. That was a sign that I had outgrown my homework-based code. While I was sad to see my own code go, it is a sign of maturity when your research problems mandate a better and more-efficient implementation of such a basic inference algorithm. Her package was easy to use, has plenty of documentation, and I would recommend it to anybody in need of CRF inference.