YOLO 各系列结构整理

目录

2016 You Only Look Once: Unified, Real-Time Object Detection(CVPR,Joseph Redmon)

2017 YOLO9000: Better, Faster, Stronger (CVPR,Joseph Redmon)

2018 YOLOv3:AnIncrementalImprovemen (CVPR,Joseph Redmon)

2020 YOLOv4: Optimal Speed and Accuracy of Object Detection

2021 YOLOV5 (Ultralytics)

2022 YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications (2022)

2023 YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors

2024 YOLO V8 (Ultralytics)

2024 YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information

YOLO V10

YOLO11 (Ultralytics)

YOLO 12

 YOLO V3-SPP

2020 PP-YOLO: An Effective and Efficient Implementation of Object Detector

2021 PP-YOLOv2: A Practical Object Detector

2021 YOLOX: Exceeding YOLO Series in 2021

2021 YOLO-ReT: Towards High Accuracy Real-time Object Detection on Edge GPU


团队                

basebase

Joseph Redmon

v1,v2,v3

Alexey Bochkovskiy

v4,v7,v9

Ultralytics

v5,v8,v11

美团视觉智能部

v6

清华大学团队

v10

其他                        

V12v11

2016 You Only Look Once: Unified, Real-Time Object Detection(CVPR,Joseph Redmon)

输入:448x448 45FPS 63.4mAP

输出:(4+1+4+1+20)*[7x7]

预测 [x,y,w,h,confidence, x1,y2,w2,h2,confidence2,classScore*20], 非anchor形式

2017 YOLO9000: Better, Faster, Stronger (CVPR,Joseph Redmon)

使用anchor的预测形式

输出:(4+1+20)*[5*(13*13)]

[tx,ty,tw,th,confidence, class*20]*5 ,每个框5个anchor

2018 YOLOv3:AnIncrementalImprovemen (CVPR,Joseph Redmon)

多尺度输出

输出:(4+1+80)*[3*(13*13+26*26+52*52)] ,每个框3个anchor

N*N*[3*(1+4+80)] ,每个网格预测三个anchor box

2020 YOLOv4: Optimal Speed and Accuracy of Object Detection

对V3进行了优化

CSPDarknet53 主干,Mish 激活,Dropblock正则化

 SPP模块,FPN+PAN

输出:(4+1+80)*[3*(13*13+26*26+52*52)] ,每个框3个anchor

2021 YOLO V5 (Ultralytics)

输出:(4+1)*[3*(20*20+40*40+80*80)]

2022 YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications (2022)

2023 YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors

2024 YOLO V8 (Ultralytics)

2024 YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information

YOLO V10

YOLO V11 (Ultralytics)

YOLO V12

输出 (4+80)x8400


 YOLO V3-SPP

2020 PP-YOLO: An Effective and Efficient Implementation of Object Detector

2021 PP-YOLOv2: A Practical Object Detector

2021 YOLOX: Exceeding YOLO Series in 2021

2021 YOLO-ReT: Towards High Accuracy Real-time Object Detection on Edge GPU

评论
添加红包

请填写红包祝福语或标题

红包个数最小为10个

红包金额最低5元

当前余额3.43前往充值 >
需支付:10.00
成就一亿技术人!
领取后你会自动成为博主和红包主的粉丝 规则
hope_wisdom
发出的红包

打赏作者

blanklog

你的鼓励将是我创作的最大动力

¥1 ¥2 ¥4 ¥6 ¥10 ¥20
扫码支付:¥1
获取中
扫码支付

您的余额不足,请更换扫码支付或充值

打赏作者

实付
使用余额支付
点击重新获取
扫码支付
钱包余额 0

抵扣说明:

1.余额是钱包充值的虚拟货币,按照1:1的比例进行支付金额的抵扣。
2.余额无法直接购买下载,可以购买VIP、付费专栏及课程。

余额充值