D FINE Overview The D FINE model was proposed in D FINE: Redefine Regression Task in DETRs as Fine grained Distribution Refinement by Yansong Peng, Hebei Li, Peixi Wu, Yueyi Zhang, Xiaoyan Sun, Feng Wu This model was contributed by VladOS95 cyber with the help of @qubvel hf This is the HF transformers implementation for D FINE coco model trained on COCO obj365 model trained on Object365 obj2coco model trained on Object365 and then finetuned on COCO Performance 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). How to use Training D FINE is trained on COCO (Lin et al. [2014]) train2017 and validated on COCO val2017 dataset. We report the standard AP metrics (averaged over uniformly sampled IoU thresholds ranging from 0.50 − 0.95 with a step size of 0.05), and APval5000 commonly used in real scenarios. Applications D FINE is ideal for real time object detection in diverse applications such as autonomous driving , surveillance systems , roboti…
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