当前位置：首页 > news >正文

亚马逊用什么网站上传做新品好千锋教育郑州校区

news 2026/4/7 14:38:33

亚马逊用什么网站上传做新品好,千锋教育郑州校区,毕设做网站可以得高分吗,成都做小程序的公司在我前面的博文中对于农作物病虫害的检测识别已经做过了，不过那个主要是针对水稻的，文章如下：《基于yolov5的轻量级水稻虫害目标检测项目实践》感兴趣的话可以自行移步阅读。这里主要是针对酸枣常见的几种病虫害检测检测识别，首先…

在我前面的博文中对于农作物病虫害的检测识别已经做过了，不过那个主要是针对水稻的，文章如下：

《基于yolov5的轻量级水稻虫害目标检测项目实践》

感兴趣的话可以自行移步阅读。

这里主要是针对酸枣常见的几种病虫害检测检测识别，首先看下效果：

接下来看下数据集概况：

YOLO格式标注数据文件如下：

实例标注内容如下：

1 0.25 0.34507 0.036364 0.030986
1 0.211364 0.460563 0.027273 0.033803
1 0.132955 0.239437 0.029545 0.04507
1 0.15 0.149296 0.036364 0.033803
1 0.177841 0.103521 0.053409 0.057746
1 0.179545 0.026761 0.025 0.033803
1 0.239205 0.05 0.014773 0.038028
1 0.331818 0.039437 0.013636 0.033803
1 0.411932 0.011972 0.0375 0.015493
1 0.677273 0.052817 0.034091 0.043662
1 0.786364 0.057746 0.086364 0.112676
1 0.877273 0.069718 0.045455 0.026761
1 0.095455 0.505634 0.079545 0.129577
1 0.150568 0.63169 0.0625 0.05493
1 0.201136 0.708451 0.056818 0.061972
1 0.180114 0.546479 0.017045 0.033803
1 0.224432 0.543662 0.014773 0.033803
1 0.140341 0.39507 0.021591 0.023944
1 0.204545 0.391549 0.015909 0.030986
1 0.238636 0.391549 0.015909 0.025352
1 0.247159 0.449296 0.019318 0.033803
1 0.265909 0.551408 0.011364 0.023944
1 0.280114 0.725352 0.017045 0.039437
1 0.339773 0.452817 0.015909 0.023944
1 0.389773 0.530282 0.034091 0.043662
1 0.340909 0.685915 0.022727 0.033803
1 0.451136 0.580282 0.015909 0.028169
1 0.430682 0.51831 0.018182 0.03662
1 0.449432 0.207042 0.019318 0.019718
1 0.516477 0.217606 0.019318 0.021127
1 0.531818 0.159155 0.011364 0.022535
1 0.589773 0.125352 0.020455 0.028169
1 0.622159 0.11338 0.023864 0.026761
1 0.684091 0.116197 0.018182 0.015493
1 0.555114 0.225352 0.014773 0.042254
1 0.597159 0.380282 0.017045 0.039437
1 0.549432 0.628873 0.0375 0.111268
1 0.480682 0.756338 0.052273 0.090141
1 0.440341 0.926761 0.071591 0.073239
1 0.589773 0.825352 0.095455 0.222535
1 0.74375 0.70493 0.132955 0.153521
1 0.665909 0.55 0.022727 0.057746
1 0.626705 0.571831 0.023864 0.033803
1 0.728409 0.411268 0.061364 0.169014
1 0.781818 0.296479 0.036364 0.074648
1 0.736932 0.182394 0.044318 0.043662
1 0.807386 0.15493 0.019318 0.030986
1 0.836932 0.174648 0.019318 0.028169
1 0.901705 0.247183 0.017045 0.038028
1 0.849432 0.294366 0.014773 0.076056
1 0.829545 0.421127 0.059091 0.059155
1 0.803409 0.482394 0.022727 0.035211
1 0.788636 0.556338 0.034091 0.08169
1 0.186364 0.222535 0.027273 0.109859
1 0.238636 0.249296 0.043182 0.033803

VOC格式标注数据文件如下所示：

实例标注内容如下所示：

<annotation><folder>Data</folder><filename>1f4707fe-6d90-4115-80fd-b0e5e3d4b18f.jpg</filename>.<source><database>The Database</database><annotation>Data</annotation><image>Data</image></source><owner><name>YSHC</name></owner>    <size><width>495</width><height>372</height><depth>3</depth></size><segmented>0</segmented><object>        <name>G</name><pose>Unspecified</pose><truncated>0</truncated><difficult>0</difficult><bndbox><xmin>588</xmin><ymin>314</ymin><xmax>632</xmax><ymax>368</ymax></bndbox></object><object>        <name>G</name><pose>Unspecified</pose><truncated>0</truncated><difficult>0</difficult><bndbox><xmin>784</xmin><ymin>526</ymin><xmax>807</xmax><ymax>534</ymax></bndbox></object><object>        <name>G</name><pose>Unspecified</pose><truncated>0</truncated><difficult>0</difficult><bndbox><xmin>803</xmin><ymin>557</ymin><xmax>848</xmax><ymax>578</ymax></bndbox></object></annotation>

这里我使用的是yolov5s系列的模型，yaml文件如下所示：

#Parameters
nc: 3  # number of classes
depth_multiple: 0.33  # model depth multiple
width_multiple: 0.50  # layer channel multiple
anchors:- [10,13, 16,30, 33,23]  # P3/8- [30,61, 62,45, 59,119]  # P4/16- [116,90, 156,198, 373,326]  # P5/32#Backbone
backbone:# [from, number, module, args][[-1, 1, Conv, [64, 6, 2, 2]],  # 0-P1/2[-1, 1, Conv, [128, 3, 2]],  # 1-P2/4[-1, 3, C3, [128]],[-1, 1, Conv, [256, 3, 2]],  # 3-P3/8[-1, 6, C3, [256]],[-1, 1, Conv, [512, 3, 2]],  # 5-P4/16[-1, 9, C3, [512]],[-1, 1, Conv, [1024, 3, 2]],  # 7-P5/32[-1, 3, C3, [1024]],[-1, 1, SPPF, [1024, 5]],  # 9]#Head
head:[[-1, 1, Conv, [512, 1, 1]],[-1, 1, nn.Upsample, [None, 2, 'nearest']],[[-1, 6], 1, Concat, [1]],  # cat backbone P4[-1, 3, C3, [512, False]],  # 13[-1, 1, Conv, [256, 1, 1]],[-1, 1, nn.Upsample, [None, 2, 'nearest']],[[-1, 4], 1, Concat, [1]],  # cat backbone P3[-1, 3, C3, [256, False]],  # 17 (P3/8-small)[-1, 1, Conv, [256, 3, 2]],[[-1, 14], 1, Concat, [1]],  # cat head P4[-1, 3, C3, [512, False]],  # 20 (P4/16-medium)[-1, 1, Conv, [512, 3, 2]],[[-1, 10], 1, Concat, [1]],  # cat head P5[-1, 3, C3, [1024, False]],  # 23 (P5/32-large)[[17, 20, 23], 1, Detect, [nc, anchors]],  # Detect(P3, P4, P5)]

默认100epoch的迭代计算，结果详情如下：

混淆矩阵：