  Visually-conditioned language models (VLMs) have seen growing adoption in
applications such as visual dialogue, scene understanding, and robotic task
planning; adoption that has fueled a wealth of new models such as LLaVa,
InstructBLIP, and PaLI-3. Despite the volume of new releases, key design
decisions around image preprocessing, architecture, and optimization are
under-explored, making it challenging to understand what factors account for
model performance $-$ a challenge further complicated by the lack of objective,
consistent evaluations. To address these gaps, we first compile a suite of
standardized evaluations spanning visual question answering, object
localization, and challenge sets that probe properties such as hallucination;
evaluations that provide fine-grained insight VLM capabilities. Second, we
rigorously investigate VLMs along key design axes, including pretrained visual
representations and training from base vs. instruct-tuned language models,
amongst others. We couple our analysis with three resource contributions: (1) a
unified framework for evaluating VLMs, (2) optimized, flexible training code,
and (3) checkpoints for all models, including a family of VLMs at the 7-13B
scale that strictly outperform InstructBLIP and LLaVa v1.5, the
state-of-the-art in open VLMs.
