Improved Baselines with Visual Instruction Tuning
Large multimodal models (LMM) have recently shown encouraging progress with visual instruction tuning. In this note, we show that the fully-connected vision-language cross-modal connector in LLaVA is surprisingly powerful and data-efficient. With simple modifications to LLaVA, namely, using CLIP-ViT-L-336px with an MLP projection and adding academic-task-oriented VQA data with simple response formatting prompts, we establish stronger baselines that achieve state-of-the-art across 11 benchmarks. Our final 13B checkpoint uses merely 1.2M publicly available data, and finishes full training in ~1 day on a single 8-A100 node. We hope this can make state-of-the-art LMM research more accessible. Code and model will be publicly available.
PDF Abstract
Spaces
Replicate
MS COCO
Visual Genome
GQA
RefCOCO
OK-VQA
TextVQA
VizWiz
A-OKVQA
MMBench
MM-Vet
SEED-Bench
InfiMM-Eval
ViP-Bench
BenchLMM
CHOCOLATE
