html拖拽代码生成器,seo快速优化技术,app网站开发培训,php网站开发程序填空题在我之前的一篇文章中有过生猪检测盒状态识别相关的项目实践#xff0c;如下#xff1a;
《Python基于yolov4实现生猪检测及状态识》
感兴趣的话可以自行移步阅读#xff0c;这里主要是基于同样的技术思想#xff0c;将原始体积较大的yolov4模型做无缝替换#xff0c;使…在我之前的一篇文章中有过生猪检测盒状态识别相关的项目实践如下
《Python基于yolov4实现生猪检测及状态识》
感兴趣的话可以自行移步阅读这里主要是基于同样的技术思想将原始体积较大的yolov4模型做无缝替换使用当下比较优秀的轻量级yolov5s模型来实现目标检测后续基于状态识别模型实现生猪状态的识别首先看下效果图如下所示 简单看下数据集 YOLO格式标注文件如下所示 实例标注内容如下所示
0 0.062744 0.558594 0.046387 0.16276
0 0.077637 0.701497 0.0625 0.126953
0 0.107422 0.805664 0.053711 0.087891
0 0.129883 0.798503 0.063477 0.138672
0 0.151367 0.811198 0.073242 0.123698
0 0.22876 0.842773 0.085449 0.115234
0 0.283936 0.794922 0.066895 0.227865
0 0.333496 0.773438 0.06543 0.197917
0 0.362793 0.812826 0.078125 0.166016
0 0.394043 0.848958 0.108398 0.167969
0 0.468994 0.878255 0.131348 0.105469
0 0.720459 0.733398 0.068848 0.19987
0 0.86499 0.628255 0.096191 0.091146
0 0.922607 0.434245 0.040527 0.164062
0 0.87915 0.301107 0.046387 0.146484
0 0.907715 0.297852 0.035156 0.120443
0 0.870117 0.166992 0.047852 0.108724
0 0.829102 0.145182 0.058594 0.097656
0 0.79126 0.264974 0.112793 0.135417
0 0.684326 0.127279 0.104004 0.078776
0 0.668213 0.068685 0.10498 0.064453
0 0.616699 0.142578 0.104492 0.174479
0 0.49292 0.151042 0.162598 0.098958
0 0.437256 0.417643 0.202637 0.212891
0 0.387207 0.329753 0.104492 0.210286
0 0.300049 0.403971 0.069824 0.222005
0 0.195312 0.514974 0.12207 0.227865
0 0.222168 0.451497 0.092773 0.133464VOC格式标注文件如下所示 实例标注数据如下所示
annotationfolderDATASET/folderfilenameimages/20190621141536.jpg/filenamesourcedatabaseThe DATASET Database/databaseannotationDATASET/annotationimageDATASET/image/sourceownernameYMGZS/name/owner sizewidth2048/widthheight1536/heightdepth3/depth/sizesegmented0/segmentedobject namepig/nameposeUnspecified/posetruncated0/truncateddifficult0/difficultbndboxxmin775/xminymin1268/yminxmax1072/xmaxymax1406/ymax/bndbox/objectobject namepig/nameposeUnspecified/posetruncated0/truncateddifficult0/difficultbndboxxmin507/xminymin1279/yminxmax785/xmaxymax1434/ymax/bndbox/objectobject namepig/nameposeUnspecified/posetruncated0/truncateddifficult0/difficultbndboxxmin464/xminymin1130/yminxmax728/xmaxymax1333/ymax/bndbox/objectobject namepig/nameposeUnspecified/posetruncated0/truncateddifficult0/difficultbndboxxmin361/xminymin1197/yminxmax507/xmaxymax1366/ymax/bndbox/objectobject namepig/nameposeUnspecified/posetruncated0/truncateddifficult0/difficultbndboxxmin226/xminymin1164/yminxmax399/xmaxymax1302/ymax/bndbox/objectobject namepig/nameposeUnspecified/posetruncated0/truncateddifficult0/difficultbndboxxmin161/xminymin1171/yminxmax321/xmaxymax1311/ymax/bndbox/objectobject namepig/nameposeUnspecified/posetruncated0/truncateddifficult0/difficultbndboxxmin168/xminymin1025/yminxmax314/xmaxymax1175/ymax/bndbox/objectobject namepig/nameposeUnspecified/posetruncated0/truncateddifficult0/difficultbndboxxmin104/xminymin973/yminxmax185/xmaxymax1161/ymax/bndbox/objectobject namepig/nameposeUnspecified/posetruncated0/truncateddifficult0/difficultbndboxxmin87/xminymin754/yminxmax166/xmaxymax987/ymax/bndbox/objectobject namepig/nameposeUnspecified/posetruncated0/truncateddifficult0/difficultbndboxxmin68/xminymin641/yminxmax178/xmaxymax736/ymax/bndbox/objectobject namepig/nameposeUnspecified/posetruncated0/truncateddifficult0/difficultbndboxxmin70/xminymin580/yminxmax179/xmaxymax656/ymax/bndbox/objectobject namepig/nameposeUnspecified/posetruncated0/truncateddifficult0/difficultbndboxxmin71/xminymin425/yminxmax218/xmaxymax592/ymax/bndbox/objectobject namepig/nameposeUnspecified/posetruncated0/truncateddifficult0/difficultbndboxxmin266/xminymin335/yminxmax487/xmaxymax440/ymax/bndbox/objectobject namepig/nameposeUnspecified/posetruncated0/truncateddifficult0/difficultbndboxxmin464/xminymin321/yminxmax673/xmaxymax454/ymax/bndbox/objectobject namepig/nameposeUnspecified/posetruncated0/truncateddifficult0/difficultbndboxxmin530/xminymin508/yminxmax768/xmaxymax717/ymax/bndbox/objectobject namepig/nameposeUnspecified/posetruncated0/truncateddifficult0/difficultbndboxxmin709/xminymin521/yminxmax909/xmaxymax847/ymax/bndbox/objectobject namepig/nameposeUnspecified/posetruncated0/truncateddifficult0/difficultbndboxxmin787/xminymin209/yminxmax1011/xmaxymax549/ymax/bndbox/objectobject namepig/nameposeUnspecified/posetruncated0/truncateddifficult0/difficultbndboxxmin949/xminymin64/yminxmax1261/xmaxymax233/ymax/bndbox/objectobject namepig/nameposeUnspecified/posetruncated0/truncateddifficult0/difficultbndboxxmin1045/xminymin237/yminxmax1387/xmaxymax387/ymax/bndbox/objectobject namepig/nameposeUnspecified/posetruncated0/truncateddifficult0/difficultbndboxxmin1254/xminymin66/yminxmax1476/xmaxymax218/ymax/bndbox/objectobject namepig/nameposeUnspecified/posetruncated0/truncateddifficult0/difficultbndboxxmin1295/xminymin135/yminxmax1495/xmaxymax235/ymax/bndbox/objectobject namepig/nameposeUnspecified/posetruncated0/truncateddifficult0/difficultbndboxxmin1480/xminymin104/yminxmax1661/xmaxymax197/ymax/bndbox/objectobject namepig/nameposeUnspecified/posetruncated0/truncateddifficult0/difficultbndboxxmin1649/xminymin142/yminxmax1740/xmaxymax264/ymax/bndbox/objectobject namepig/nameposeUnspecified/posetruncated0/truncateddifficult0/difficultbndboxxmin1772/xminymin341/yminxmax1891/xmaxymax560/ymax/bndbox/objectobject namepig/nameposeUnspecified/posetruncated0/truncateddifficult0/difficultbndboxxmin1828/xminymin553/yminxmax1933/xmaxymax772/ymax/bndbox/objectobject namepig/nameposeUnspecified/posetruncated0/truncateddifficult0/difficultbndboxxmin1810/xminymin782/yminxmax1939/xmaxymax977/ymax/bndbox/objectobject namepig/nameposeUnspecified/posetruncated0/truncateddifficult0/difficultbndboxxmin1364/xminymin902/yminxmax1576/xmaxymax1216/ymax/bndbox/objectobject namepig/nameposeUnspecified/posetruncated0/truncateddifficult0/difficultbndboxxmin1342/xminymin1016/yminxmax1514/xmaxymax1247/ymax/bndbox/object/annotation
默认使用轻量级的yolov5s模型来进行模型的开发默认训练100次epoch结果详情如下所示
【F1值曲线】 【PR曲线】 【Precision和Recall曲线】 数据可视化 Batch计算实例 可视化界面推理实例如下 目标检测状态识别在界面中做了集成实现。
