Research map / Detection and segmentation / CNN features
You Only Look Once: Unified, Real-Time Object Detection
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Replace YOLO's 24-layer network with the 9-layer Fast YOLO network and fewer filters (ablation) | YOLO · Pascal VOC 2007 | mAP: 63.4 -> 52.7 | Hurt |
| Replace YOLO's 24-layer network with the 9-layer Fast YOLO network and fewer filters (ablation) | YOLO · Pascal VOC 2007 | FPS: 45 -> 155 | Helped |
| Add YOLO-based rescoring of Fast R-CNN detections | Fast R-CNN · Pascal VOC 2007 | mAP: 71.8 -> 75.0; 3.2% | Helped |
| Combine Fast R-CNN with another Fast R-CNN version instead of YOLO | Fast R-CNN · Pascal VOC 2007 | mAP: .3 to .6% | Helped |
| Apply non-maximal suppression to remove duplicate detections (ablation) | YOLO · Pascal VOC | mAP: 2-3% | Helped |
| Use YOLO rather than Fast R-CNN for detections | object detection · Pascal VOC 2007 | background false-positive rate: 13.6%; almost 3x | Helped |
| Use YOLO rather than Fast R-CNN for localization | object detection · Pascal VOC 2007 | localization errors | Hurt |
Closest papers by meaning
- YOLO-Master: MOE-Accelerated with Specialized Transformers for Enhanced Real-time Detection (CVPR 2026 · similarity 0.92)
- YOLO-RD: Introducing Relevant and Compact Explicit Knowledge to YOLO by Retriever-Dictionary (ICLR 2025 · similarity 0.92)
- YOLOv10: Real-Time End-to-End Object Detection (NeurIPS 2024 · similarity 0.91)
- Mamba YOLO: A Simple Baseline for Object Detection with State Space Model (AAAI 2025 · similarity 0.91)
- YOLO-World: Real-Time Open-Vocabulary Object Detection (CVPR 2024 · similarity 0.90)
- YolOOD: Utilizing Object Detection Concepts for Multi-Label Out-of-Distribution Detection (CVPR 2024 · similarity 0.87)
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