Object detection is the computer vision task of detecting instances (such as humans, buildings, or cars) in an image. Object detection models receive an image as input and output coordinates of the bounding boxes and associated labels of the detected objects.
D-FINE is a powerful real-time object detector that redefines the bounding box regression task in DETRs as Fine-grained Distribution Refinement (FDR) and introduces Global Optimal Localization Self-Distillation (GO-LSD), achieving outstanding performance without introducing additional inference and training costs.
📄 This is the official GitHub:
D-FINE
This is the official implementation of the paper:
D-FINE: Redefine Regression Task of DETRs as Fine-grained Distribution Refinement
This is the official dataset mentioned in the code:
cppe-5