YOLOv10
Real-Time End-to-End Object Detection
Ao Wang, Hui Chen, Lihao Liu, Kai Chen, Zijia Lin, Jungong Han, Guiguang Ding
7
Concepts
Paper Overview
YOLOv10 (Tsinghua University, 2024) achieves true end-to-end real-time object detection by eliminating Non-Maximum Suppression (NMS) — the last non-differentiable post-processing step in the detection pipeline. It does this through consistent dual assignments: training with both one-to-many (rich supervision) and one-to-one (NMS-free) branches simultaneously, using only the one-to-one branch at inference.
Why NMS elimination matters: NMS adds 1-2ms latency per image on GPU (more on edge devices), varies with scene complexity (more objects → slower), is non-differentiable (can't be optimized during training), and complicates deployment (requires NMS library support). Previous end-to-end detectors (DETR, RT-DETR) eliminated NMS but used expensive Transformer decoders, making them slower overall.
Beyond NMS-free training, YOLOv10 introduces a holistic efficiency-accuracy design with multiple architectural innovations:
| Innovation | Savings | Where Applied |
|---|---|---|
| Lightweight classification head | 40% fewer head params | All models |
| Spatial-channel decoupled downsampling | Better info preservation | All models |
| Rank-guided block design | Stage-specific block selection | M/B/L/X |
| Large-kernel depthwise convolutions | Larger receptive field | L/X |
| Partial Self-Attention (PSA) | Global context at lower cost | S/M/B/L/X |
Key results on COCO val2017:
- YOLOv10-S: 46.3 mAP at 2.49ms — matches YOLOv8-S accuracy with 46% fewer parameters and 25% less compute
- YOLOv10-M: 51.1 mAP at 4.74ms — 57% faster than RT-DETR-R50 with similar accuracy
- YOLOv10-X: 54.4 mAP at 10.70ms — highest accuracy in the real-time regime
Chapter Roadmap
Click any topic to jump in
Holistic Design
Joint optimization of latency and accuracy across the entire architecture.
Dual Assignments
One-to-many head trains, one-to-one head deploys — sharing weights for consistency.
C2fCIB Block
Compact inverted bottleneck reducing FLOPs in the backbone.
PSA Module
Self-attention applied to half the channels for cheap global context.
NMS-Free Inference
Inference with no post-processing — predictions go straight to output.
N to X Scaling
Family of variants from nano to extra-large with compound scaling.
vs RT-DETR
Outperforms RT-DETR at same AP with lower latency on edge GPUs.
Concept by concept
7 sections, in reading order.
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- 01Consistent Dual Assignments for NMS-Free Training
- 02NMS-Free Object Detection
- 03Partial Self-Attention (PSA) Module
- 04C2fCIB: Compact Inverted Block Design
- 05Holistic Efficiency-Accuracy Design
- 06Model Scaling: N to X Variants
- 07Comparison with RT-DETR