Yolov6's fast and accurate target detection framework is open source

First look at YOLOv6 precision ：

Model	Size	mAPval 0.5:0.95	SpeedV100 fp16 b32 (ms)	SpeedV100 fp32 b32 (ms)	SpeedT4 trt fp16 b1 (fps)	SpeedT4 trt fp16 b32 (fps)	Params (M)	Flops (G)
YOLOv6-n	416 640	30.8 35.0	0.3 0.5	0.4 0.7	1100 788	2716 1242	4.3 4.3	4.7 11.1
YOLOv6-tiny	640	41.3	0.9	1.5	425	602	15.0	36.7
YOLOv6-s	640	43.1	1.0	1.7	373	520	17.2	44.2

There is an obvious missed detection in the lower right corner , Than yoloe The recall rate should be low .

yoloe-s effect ：

yoloe Use the experience notes ：

yoloe Target detection usage notes _AI Visual netqi's blog -CSDN Blog

yolov5 precision ：

Model	size (pixels)	mAPval 0.5:0.95	mAPval 0.5	Speed CPU b1 (ms)	Speed V100 b1 (ms)	Speed V100 b32 (ms)	params (M)	FLOPs @640 (B)
YOLOv5n	640	28.0	45.7	45	6.3	0.6	1.9	4.5
YOLOv5s	640	37.4	56.8	98	6.4	0.9	7.2	16.5
YOLOv5m	640	45.4	64.1	224	8.2	1.7	21.2	49.0
YOLOv5l	640	49.0	67.3	430	10.1	2.7	46.5	109.1
YOLOv5x	640	50.7	68.9	766	12.1	4.8	86.7	205.7
YOLOv5n6	1280	36.0	54.4	153	8.1	2.1	3.2	4.6
YOLOv5s6	1280	44.8	63.7	385	8.2	3.6	12.6	16.8
YOLOv5m6	1280	51.3	69.3	887	11.1	6.8	35.7	50.0
YOLOv5l6	1280	53.7	71.3	1784	15.8	10.5	76.8	111.4
YOLOv5x6 + TTA

yoloe precision ：

PP-YOLOE-s

640

43.1%

Training...

Training...

7.93

17.36

baidu pan code:qfld

baidu pan code:mwjy

  The following is from ：

Explode ！YOLOv6 The fast and accurate target detection framework is open source （ With source code download ）

Code address ：https://github.com/meituan/YOLOv6

Accuracy comparison ：

chart 1-1 YOLOv6 Performance comparison between each size model and other models

chart 1-2 YOLOv6 Performance comparison with other models at different resolutions

02

Yolov6 key technology

Hardware-friendly Backbone network design

chart 2 Roofline Model Introduction chart

chart 3 Rep The fusion process of operators [4]

chart 4 EfficientRep Backbone chart

chart 5 Rep-PAN chart

More concise and efficient Decoupled Head

chart 6 Efficient Decoupled Head chart

More effective training strategies

03

Experimental results and visualization

After the above optimization strategies and improvements ,YOLOv6 The model has achieved excellent performance in many different sizes . The following table 1 It shows YOLOv6-nano Results of ablation experiments , It can be seen from the experimental results that , Our self-designed detection network has brought great gains in accuracy and speed .

surface 1 YOLOv6-nano Ablation results

The following table 2 It shows YOLOv6 And other current mainstream YOLO Experimental results of a series of algorithms . You can see from the table that ：

surface 2 YOLOv6 Comparison of the performance of each size model with other models

04

Summary and prospect