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DETRs Beat YOLOs on Real-time Object Detection

📅 June 16, 2024 👤 Y. Zhao, Wenyu Lv, Shangliang Xu et al. 📖 Research Journal 📊 3,266 citations

🤖 Plain-English Summary

The YOLO series has become the most popular frame-work for real-time object detection due to its reasonable trade-off between speed and accuracy. After pre-training with Objects365, RT-DETR-R50 / R101 achieves 55.3% 156.2% AP.

🔑 Key Findings

  • However, we observe that the speed and accuracy of YOLOs are negatively affected by the NMS.
  • Recently, end-to-end Transformer-based detectors (DETRs) have provided an alternative to eliminating NMS.
  • Nevertheless, the high computational cost limits their practicality and hinders them from fully exploiting the advantage of excluding NMS.

💡 Why This Matters

This research advances how AI systems learn, reason, and solve problems — with direct implications for software, automation, and scientific discovery.

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📋 Article Details

Category 🤖 Artificial Intelligence
Published Jun 16, 2024
Journal Research Journal
Authors Y. Zhao, Wenyu Lv, Shangliang Xu, Jinman Wei, Guanzhong Wang
DOI 10.1109/cvpr52733.2024.01605
Citations 3,266
Source OpenAlex

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