Yolo v9使用教程全网首发!赶快学习吧!
论文链接: YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information 代码链接: https://github.com/WongKinYiu/yolov9/tree/main
同时推荐一下我的项目,正在更新最新的YOLOv9改进!
最新的YOLO系列模型,YOLOv9改进创新来啦!发论文必备!
本周末推出YOLOv9创新点项目,目前已有20+创新,预计项目推出时创新点可达到30+!后期更新包含模块、卷积、检测头、损失等改进!
⭐大家可以搜搜看,这是CSDN最具性价比的YOLO系列改进项目⭐
⭐四月底预计创新点可达80-100+!⭐
⭐现在入手仅$ 59.9,早入早发论文!⭐
⭐即将涨价,创新点越多,价格越贵!⭐
欢迎交流!联系QQ: 2668825911
本项目持续更新,不付费订阅也可关注等每周更新,每周开源1-2篇。
YOLOv9改进专栏|包含卷积、模块、主干等最新改进
使用教程
首先前往官网下载代码与权重文件。
将下载的代码解压,并将预训练权重拷贝到yolov9-main工程下,将权重文件路径和配置文件路径依次填入train脚本中。 创建一个数据集配置文件,包含数据集路径,种类数量,种类名称
# dataset path (改成你自己的数据集路径)
# 数据集格式与前几代yolo相同。
train: ./dataset/images/train
val: ./dataset/images/val
test: ./dataset/images/test
# number of classes
nc: 6
# class names
names: ['missing_hole', 'mouse_bite', 'open_circuit', 'short','spur', 'spurious_copper']
复制刚才创建的yaml文件的路径,粘贴到train脚本的data参数中。并将hyp参数的值改为data/hyps/hyp.scratch-high.yaml
data/hyps/hyp.scratch-high.yaml
2024.2.22日官网发布的代码存在bug,将utils工程下loss_tal脚本中的第167行中的p改为p[0]或p[1],改完能运行。(bug产生的原因是列表导致后面方法错误,具体原因正在读源码!) 运行成功!
配置文件,关注我,后续更新 yolov9结构解读及代码改进!
backbone:
[
[-1, 1, Silence, []],
# conv down
[-1, 1, Conv, [64, 3, 2]], # 1-P1/2
# conv down
[-1, 1, Conv, [128, 3, 2]], # 2-P2/4
# elan-1 block
[-1, 1, RepNCSPELAN4, [256, 128, 64, 1]], # 3
# avg-conv down
[-1, 1, ADown, [256]], # 4-P3/8
# elan-2 block
[-1, 1, RepNCSPELAN4, [512, 256, 128, 1]], # 5
# avg-conv down
[-1, 1, ADown, [512]], # 6-P4/16
# elan-2 block
[-1, 1, RepNCSPELAN4, [512, 512, 256, 1]], # 7
# avg-conv down
[-1, 1, ADown, [512]], # 8-P5/32
# elan-2 block
[-1, 1, RepNCSPELAN4, [512, 512, 256, 1]], # 9
]
# YOLOv9 head
head:
[
# elan-spp block
[-1, 1, SPPELAN, [512, 256]], # 10
# up-concat merge
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[[-1, 7], 1, Concat, [1]], # cat backbone P4
# elan-2 block
[-1, 1, RepNCSPELAN4, [512, 512, 256, 1]], # 13
# up-concat merge
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[[-1, 5], 1, Concat, [1]], # cat backbone P3
# elan-2 block
[-1, 1, RepNCSPELAN4, [256, 256, 128, 1]], # 16 (P3/8-small)
# avg-conv-down merge
[-1, 1, ADown, [256]],
[[-1, 13], 1, Concat, [1]], # cat head P4
# elan-2 block
[-1, 1, RepNCSPELAN4, [512, 512, 256, 1]], # 19 (P4/16-medium)
# avg-conv-down merge
[-1, 1, ADown, [512]],
[[-1, 10], 1, Concat, [1]], # cat head P5
# elan-2 block
[-1, 1, RepNCSPELAN4, [512, 512, 256, 1]], # 22 (P5/32-large)
# multi-level reversible auxiliary branch
# routing
[5, 1, CBLinear, [[256]]], # 23
[7, 1, CBLinear, [[256, 512]]], # 24
[9, 1, CBLinear, [[256, 512, 512]]], # 25
# conv down
[0, 1, Conv, [64, 3, 2]], # 26-P1/2
# conv down
[-1, 1, Conv, [128, 3, 2]], # 27-P2/4
# elan-1 block
[-1, 1, RepNCSPELAN4, [256, 128, 64, 1]], # 28
# avg-conv down fuse
[-1, 1, ADown, [256]], # 29-P3/8
[[23, 24, 25, -1], 1, CBFuse, [[0, 0, 0]]], # 30
# elan-2 block
[-1, 1, RepNCSPELAN4, [512, 256, 128, 1]], # 31
# avg-conv down fuse
[-1, 1, ADown, [512]], # 32-P4/16
[[24, 25, -1], 1, CBFuse, [[1, 1]]], # 33
# elan-2 block
[-1, 1, RepNCSPELAN4, [512, 512, 256, 1]], # 34
# avg-conv down fuse
[-1, 1, ADown, [512]], # 35-P5/32
[[25, -1], 1, CBFuse, [[2]]], # 36
# elan-2 block
[-1, 1, RepNCSPELAN4, [512, 512, 256, 1]], # 37
# detection head
# detect
[[31, 34, 37, 16, 19, 22], 1, DualDDetect, [nc]], # DualDDetect(A3, A4, A5, P3, P4, P5)
]
好文推荐
发表评论