Overview

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.

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.

For further reading check out this blog.

Performance

Model	Size	mAP^val 0.5:0.95	Speed^{T4 trt fp16 b1 (fps)}	Speed^{T4 trt fp16 b32 (fps)}	Params ^(M)	FLOPs ^(G)
YOLOv6-N	640	35.9^300e 36.3^400e	802	1234	4.3	11.1
YOLOv6-T	640	40.3^300e 41.1^400e	449	659	15.0	36.7
YOLOv6-S	640	43.5^300e 43.8^400e	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

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YOLOv6 comes in a variety of models. Its largest, YOLOv6-L achieves highest mAP, however it is also the slowest model of the few. In contrast, YOLOv6-N is by far the fastest and smallest model, but sacrifices accuracy for those reasons.

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Use This Model

Label Data Automatically with MT-YOLOv6

You can automatically label a dataset using MT-YOLOv6 with help from Autodistill, an open source package for training computer vision models. You can label a folder of images automatically with only a few lines of code. Below, see our tutorials that demonstrate how to use MT-YOLOv6 to train a computer vision model.

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