Better than YOLOv10 & RT-DETR? Meet D-FINE Object Detection

Описание: In this video, we dive deep into D-FINE, a powerful new real-time object detector that is pushing the boundaries of computer vision and surpassing existing models like YOLO and RT-DETR
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Traditional object detectors predict bounding boxes using rigid, fixed coordinates, which struggle to handle localisation uncertainty
. D-FINE completely redefines this task using Fine-grained Distribution Refinement (FDR) to iteratively refine probability distributions for much higher accuracy
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We also explore D-FINE's innovative Global Optimal Localisation Self-Distillation (GO-LSD), a highly efficient strategy that transfers localisation knowledge from deeper layers back to shallower layers, boosting performance with negligible extra training costs
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🏆 Key Benchmarks on COCO dataset (NVIDIA T4 GPU):
D-FINE-L: 54.0% AP at 124 FPS
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D-FINE-X: 55.8% AP at 78 FPS
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With Objects365 Pretraining: Reaches up to 59.3% AP, outperforming state-of-the-art end-to-end models like YOLOv10, YOLO11, and RT-DETR
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🔗 Resources & Links:
Official D-FINE GitHub Repository & Pretrained Models: https://github.com/Peterande/D-FINE
Paper: D-FINE: Redefine Regression Task in DETRs as Fine-Grained Distribution Refinement
Tags (Comma Separated) Object Detection, Computer Vision, D-FINE, Artificial Intelligence, Machine Learning, Deep Learning, YOLOv10, YOLO11, RT-DETR, DETR, Bounding Box Regression, Neural Networks, Real-Time Detection, FDR, GO-LSD

Chapters / Timestamps
0:00 - Introduction to Real-Time Object Detectors (YOLO & DETR)
1:30 - The Problem with Traditional Bounding Box Regression
3:15 - Introducing D-FINE: A New Era for DETRs
4:30 - How Fine-grained Distribution Refinement (FDR) Works
6:45 - Explaining Global Optimal Localisation Self-Distillation (GO-LSD)
8:20 - Benchmark Results: D-FINE vs. YOLO & RT-DETR
10:00 - Conclusion & How to Access the Code