Object Detection Benchmarks
Independent, reproducible benchmarks for YOLO, RT-DETR, and other computer vision models. Compare accuracy, speed, and efficiency across hardware — with complete end-to-end timing.
Accuracy vs Speed
mAP@50-95 on COCO val2017 vs throughput. Pareto-optimal models highlighted.
rtdetr
yolo-nas
yolov10
yolov11
yolov7
yolov8
yolov9
yolox
Pareto Frontier
Hardware:
Families:yolov8yolov9yolov10yolov11yolo-nasyoloxyolov7rtdetr
39 models
| # | Model | mAP | FPS | Latency | Params | mAP/GFLOP |
|---|---|---|---|---|---|---|
| 1 | yolov9e | 55.5% | 70.7 | 14.1ms | 58.1M | 0.3 |
| 2 | rtdetr-x | 54.9% | 61.5 | 16.3ms | 67.0M | 0.2 |
| 3 | yolov11x | 54.6% | 69.2 | 14.4ms | 56.9M | 0.3 |
| 4 | yolov10x | 54.3% | 92.6 | 10.8ms | 29.5M | 0.3 |
| 5 | rtdetr-r101 | 54.3% | 58.6 | 17.1ms | 76.0M | 0.2 |
| 6 | yolov8x | 53.8% | 63.3 | 15.8ms | 68.2M | 0.2 |
| 7 | yolov11l | 53.4% | 95.1 | 10.5ms | 25.3M | 0.6 |
| 8 | yolov10l | 53.3% | 120.9 | 8.3ms | 24.4M | 0.4 |
| 9 | yolov7-x | 53.2% | 66.5 | 15.0ms | 71.3M | 0.3 |
| 10 | rtdetr-r50 | 53.1% | 78.0 | 12.8ms | 42.0M | 0.4 |
| 11 | yolov9c | 53.0% | 98.5 | 10.1ms | 25.5M | 0.5 |
| 12 | yolov8l | 52.9% | 82.5 | 12.1ms | 43.7M | 0.3 |
| 13 | rtdetr-l | 52.9% | 92.9 | 10.8ms | 32.0M | 0.5 |
| 14 | yolov10b | 52.4% | 149.5 | 6.7ms | 19.1M | 0.6 |
| 15 | yolo-nas-l | 52.3% | 91.8 | 10.9ms | 44.5M | 0.4 |
| 16 | yolov11m | 51.6% | 115.0 | 8.7ms | 20.1M | 0.8 |
| 17 | yolov9m | 51.4% | 117.7 | 8.5ms | 20.1M | 0.7 |
| 18 | yolo-nas-m | 51.4% | 104.1 | 9.6ms | 31.9M | 0.6 |
| 19 | yolov7 | 51.3% | 96.3 | 10.4ms | 36.9M | 0.5 |
| 20 | yolox-x | 51.1% | 50.0 | 20.0ms | 99.1M | 0.2 |
| 21 | yolov10m | 51.0% | 185.2 | 5.4ms | 15.4M | 0.9 |
| 22 | yolov8m | 50.2% | 112.8 | 8.9ms | 25.9M | 0.6 |
| 23 | yolox-l | 49.6% | 77.0 | 13.0ms | 54.2M | 0.3 |
| 24 | rtdetr-r34 | 48.8% | 98.4 | 10.2ms | 31.0M | 0.5 |
| 25 | yolo-nas-s | 47.6% | 144.5 | 6.9ms | 12.2M | 1.5 |
| 26 | yolov11s | 46.9% | 171.6 | 5.8ms | 9.4M | 2.2 |
| 27 | yolox-m | 46.9% | 121.9 | 8.2ms | 25.3M | 0.6 |
| 28 | yolov9s | 46.7% | 164.3 | 6.1ms | 7.2M | 1.7 |
| 29 | rtdetr-r18 | 46.5% | 114.9 | 8.7ms | 20.0M | 0.8 |
| 30 | yolov10s | 46.2% | 281.8 | 3.5ms | 7.2M | 2.1 |
| 31 | yolov8s | 45.0% | 167.1 | 6.0ms | 11.2M | 1.6 |
| 32 | yolox-s | 40.4% | 160.7 | 6.2ms | 9.0M | 1.5 |
| 33 | yolov11n | 39.5% | 209.7 | 4.8ms | 2.6M | 6.1 |
| 34 | yolov9t | 38.4% | 194.4 | 5.1ms | 2.0M | 5.0 |
| 35 | yolov10n | 38.4% | 380.4 | 2.6ms | 2.3M | 5.7 |
| 36 | yolov8n | 37.4% | 193.3 | 5.2ms | 3.2M | 4.3 |
| 37 | yolov7-tiny | 37.4% | 184.8 | 5.4ms | 6.2M | 2.7 |
| 38 | yolox-tiny | 32.9% | 206.6 | 4.8ms | 5.1M | 5.1 |
| 39 | yolox-nano | 25.8% | 219.2 | 4.6ms | 0.9M | 23.5 |
Why End-to-End?
Most benchmarks only report inference time, hiding the cost of preprocessing and NMS. Real-world applications pay the full price.
YOLOv10: NMS-Free
YOLOv10 eliminates Non-Maximum Suppression during inference, providing consistent latency and often faster end-to-end performance.
RT-DETR Accuracy
Transformer-based RT-DETR often achieves higher mAP than YOLO models at similar speeds. It's worth considering for accuracy-critical applications.