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D-FINE: Redefine Regression Task in DETRs as Fine-grained Distribution Refinement

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arxiv 2410.13842 v1 pith:3LUDM6QH submitted 2024-10-17 cs.CV

Yansong Peng , Hebei Li , Peixi Wu , Yueyi Zhang , Xiaoyan Sun , Feng Wu This is my paper
classification cs.CV
keywords d-finelocalizationfine-grainedmodelsregressionaccuracyachievesd-fine-l
verification ladder T0 review T1 audit T2 compute T3 formal
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We introduce D-FINE, a powerful real-time object detector that achieves outstanding localization precision by redefining the bounding box regression task in DETR models. D-FINE comprises two key components: Fine-grained Distribution Refinement (FDR) and Global Optimal Localization Self-Distillation (GO-LSD). FDR transforms the regression process from predicting fixed coordinates to iteratively refining probability distributions, providing a fine-grained intermediate representation that significantly enhances localization accuracy. GO-LSD is a bidirectional optimization strategy that transfers localization knowledge from refined distributions to shallower layers through self-distillation, while also simplifying the residual prediction tasks for deeper layers. Additionally, D-FINE incorporates lightweight optimizations in computationally intensive modules and operations, achieving a better balance between speed and accuracy. Specifically, D-FINE-L / X achieves 54.0% / 55.8% AP on the COCO dataset at 124 / 78 FPS on an NVIDIA T4 GPU. When pretrained on Objects365, D-FINE-L / X attains 57.1% / 59.3% AP, surpassing all existing real-time detectors. Furthermore, our method significantly enhances the performance of a wide range of DETR models by up to 5.3% AP with negligible extra parameters and training costs. Our code and pretrained models: https://github.com/Peterande/D-FINE.

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