VLMEvalKit

Why we included this project

Comparing vision-language models used to mean juggling separate harnesses and reformatting datasets to match each benchmark. VLMEvalKit collapses that work into a single command, letting small teams run the same evaluation across dozens of benchmarks and a broad set of open-weight and commercial models. It scores responses with generation-based evaluation, using both exact matching and LLM-assisted answer extraction, so the numbers you get are consistent across models rather than artifacts of whichever framework you happened to use. The same runs feed the public OpenVLM leaderboard, which gives you a quick check on how a candidate model did under standard conditions before you commit compute. That makes it useful for picking a model, keeping an eye on regressions during fine-tuning, and producing results you can defend.