Hot! Yolov6's fast and accurate target detection framework is open source (with source code download)

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Institute of computer vision

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

from 《 Meituan technical team 》

Computer Vision Institute column

author ：Edison_G

In recent days, , Meituan vision intelligence department has developed a target detection framework dedicated to industrial applications YOLOv6, Be able to focus on both detection accuracy and reasoning efficiency . In the process of R & D , The visual intelligence department has continuously explored and optimized , At the same time, it draws on some cutting-edge progress and scientific research achievements of academia and industry . In the target detection authoritative data set COCO The experimental results on ,YOLOv6 In terms of detection accuracy and speed, it surpasses other algorithms with the same volume , At the same time, it supports the deployment of many different platforms , It greatly simplifies the adaptation work during project deployment . Hereby open source , I hope I can help more students .

01

summary

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

05

reference

[1] YOLOv5, https://github.com/ultralytics/yolov5

[2] YOLOX: Exceeding YOLO Series in 2021, https://arxiv.org/abs/2107.08430

[3] PP-YOLOE: An evolved version of YOLO, https://arxiv.org/abs/2203.16250

[4] RepVGG: Making VGG-style ConvNets Great Again, https://arxiv.org/pdf/2101.03697

[5] CSPNet: A New Backbone that can Enhance Learning Capability of CNN, https://arxiv.org/abs/1911.11929

[6] Path aggregation network for instance segmentation, https://arxiv.org/abs/1803.01534

[7] OTA: Optimal Transport Assignment for Object Detection, https://arxiv.org/abs/2103.14259

[8] Computer Architecture: A Quantitative Approach

[9] SIoU Loss: More Powerful Learning for Bounding Box Regression, https://arxiv.org/abs/2205.12740

06

Author's brief introduction

Chu Yi 、 Kaiheng 、 nor 、 Chengmeng 、 Qin Hao 、 Yiming 、 Hongliang 、 Lin Yuan et al , All from meituan basic R & D platform / Visual intelligence department .

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Institute of computer vision

The Institute of computer vision is mainly involved in the field of deep learning , Mainly devoted to face detection 、 Face recognition , Multi target detection 、 Target tracking 、 Image segmentation and other research directions . The Research Institute will continue to share the latest paper algorithm new framework , The difference of our reform this time is , We need to focus on ” Research “. After that, we will share the practice process for the corresponding fields , Let us really experience the real scene of getting rid of the theory , Develop the habit of hands-on programming and brain thinking ！

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