Florence-2: Advancing a Unified Representation for a Variety of Vision Tasks

Benchmark Model Rank Results
visual-grounding-on-refcoco-plus-test-bFlorence-2-large-ft#1Accuracy (%): 92.0
visual-grounding-on-refcoco-plus-testaFlorence-2-large-ft#1Accuracy (%): 95.3
visual-grounding-on-refcoco-plus-valFlorence-2-large-ft#1Accuracy (%): 93.4
image-captioning-on-nocaps-val-overallFlorence-2-L#2CIDEr: 124.9
visual-question-answering-on-vizwiz-2020-vqaFlorence-2-L#2overall: 72.6
image-captioning-on-textcapsFlorence-2-L#3CIDEr: 151.1
visual-question-answering-on-vqa-v2-test-devFlorence-2-L#9Accuracy: 81.7
instance-segmentation-on-coco-minivalDaViT-B + Mask R-CNN (Florence-2 pretrain)#52mask AP: 46.4
semantic-segmentation-on-ade20kDaViT-B + UperNet (Florence-2 pretrain)#56Validation mIoU: 54.9