MM Grounding DINO (large variant) MM Grounding DINO model was proposed in An Open and Comprehensive Pipeline for Unified Object Grounding and Detection by Xiangyu Zhao, Yicheng Chen, Shilin Xu, Xiangtai Li, Xinjiang Wang, Yining Li, Haian Huang. MM Grounding DINO improves upon the Grounding DINO by improving the contrastive class head and removing the parameter sharing in the decoder, improving zero shot detection performance on both COCO (50.6(+2.2) AP) and LVIS (31.9(+11.8) val AP and 41.4(+12.6) minival AP). You can find all the original MM Grounding DINO checkpoints under the MM Grounding DINO collection. Intended uses You can use the raw model for zero shot object detection. Here's how to use the model for zero shot object detection: Training Data This model was trained on: Objects365v1 Open Images v6 GOLD G V3Det COCO 2017 LVIS v1.0 COCO 2014 GRIT RefCOCO RefCOCO+ RefCOCOg gRefCOCO Evaluation results Here's a table of models and their object detection performance results on COCO (results from official repo): Model Backbone Pre Train Data Style COCO mAP mm grounding dino tiny o365v1 goldg Swin T O365,GoldG Zero shot 50.4(+2.3) mm grounding dino tiny o365v1 goldg grit Swin T O3…
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