What matters when building vision-language models?

Benchmark Model Rank Results
visual-question-answering-vqa-on-textvqa-valIdefics2 (w/ image split)#22Accuracy: 73.0
visual-question-answering-vqa-on-mmbench-enIdefics2 (w/o image split)#28Accuracy: 76.8
visual-question-answering-on-docvqa-testIdefics2 (w/ image split)#80ANLS: 0.740
multimodal-reasoning-on-mathvistaIdefics2 (w/o image split)#104Accuracy: 51.6
multimodal-reasoning-on-mmmuIdefics2 (w/o image split)#116Accuracy: 43.5
long-context-understanding-on-mmneedleIDEFICS2-8B–1 Image, 4*4 Stitching, Exact Accuracy: 7.8…
mmr-total-on-mrr-benchmarkIdefics-2-8B–Total Column Score: 256