The YOLOv6 repository was published June 2022 by Meituan, and it claims new state-of-the-art performance on the COCO dataset benchmark. We'll leave it to the community to determine if this name is the best representation for the architecture.
https://blog.roboflow.com/yolov6/
In any case, it's clear MT-YOLOv6 (hereafter YOLOv6 for brevity) is popular. In a couple short weeks, the repo has attracted over 2,000+ stars and 300+ forks.
YOLOv6 claims to set a new state-of-the-art performance on the COCO dataset benchmark. As the authors detail, YOLOv6-s achieves 43.1 mAP on COCO val2017 dataset (with 520 FPS on T4 using TensorRT FP16 for bs32 inference).
(For point of comparison, YOLOv5-s achieves 37.4 mAP @ 0.95% on the same COCO benchmark.)
The YOLOv6 repository authors published the below evaluation graphic, demonstrating YOLOv6 outperforming YOLOv5 and YOLOX at similar sizes.
Model | Size | mAPval 0.5:0.95 |
SpeedT4 trt fp16 b1 (fps) |
SpeedT4 trt fp16 b32 (fps) |
Params (M) |
FLOPs (G) |
---|---|---|---|---|---|---|
YOLOv6-N | 640 | 35.9300e 36.3400e |
802 | 1234 | 4.3 | 11.1 |
YOLOv6-T | 640 | 40.3300e 41.1400e |
449 | 659 | 15.0 | 36.7 |
YOLOv6-S | 640 | 43.5300e 43.8400e |
358 | 495 | 17.2 | 44.2 |
YOLOv6-M | 640 | 49.5 | 179 | 233 | 34.3 | 82.2 |
YOLOv6-L-ReLU | 640 | 51.7 | 113 | 149 | 58.5 | 144.0 |
YOLOv6-L | 640 | 52.5 | 98 | 121 | 58.5 | 144.0 |