防震锤缺陷检测数据集 1000张 防震锤 带标注 voc yolo分类名: (图片张数 标注个数)none_ defect ive:(6361 808)defective: (831 933)总数:(10002741)总类(nc): 2类使用YOLOv8进行训练的完整步骤。我们将确保所有步骤都是一键运行的以便你可以轻松地进行模型训练和评估。数据集介绍数据集概述数据集名称Anti-Vibration Hammer Defect Detection Dataset (AVHDDD)数据类型图像目标任务防震锤缺陷检测样本数量1000张图像标注格式VOC (XML) 和 YOLO (TXT)类别2类无缺陷 (none_defective)缺陷 (defective)数据集统计无缺陷 (none_defective)636张图像1808个框缺陷 (defective)831张图像933个框总计1000张图像2741个框数据集目录结构AVHDDD/ ├── images/ │ ├── train/ │ └── val/ ├── labels_voc/ │ ├── train/ │ └── val/ ├── labels_yolo/ │ ├── train/ │ └── val/ └── data.yaml数据集配置文件创建一个data.yaml文件配置数据集的路径和类别信息path:./AVHDDD# 数据集路径train:images/train# 训练集图像路径val:images/val# 验证集图像路径nc:2# 类别数names:[none_defective,defective]# 类别名称转换标注格式假设标注文件是VOC格式的XML文件我们需要将它们转换为YOLO格式的TXT文件。转换脚本importxml.etree.ElementTreeasETimportosdefconvert_voc_to_yolo(voc_file,yolo_file,class_names):treeET.parse(voc_file)roottree.getroot()widthint(root.find(size/width).text)heightint(root.find(size/height).text)withopen(yolo_file,w)asf:forobjinroot.findall(object):class_nameobj.find(name).textifclass_namenotinclass_names:continueclass_idclass_names.index(class_name)bboxobj.find(bndbox)x_minfloat(bbox.find(xmin).text)y_minfloat(bbox.find(ymin).text)x_maxfloat(bbox.find(xmax).text)y_maxfloat(bbox.find(ymax).text)x_center(x_minx_max)/2.0/width y_center(y_miny_max)/2.0/height w(x_max-x_min)/width h(y_max-y_min)/height f.write(f{class_id}{x_center}{y_center}{w}{h}\n)defconvert_all_voc_to_yolo(voc_dir,yolo_dir,class_names):os.makedirs(yolo_dir,exist_okTrue)forfilenameinos.listdir(voc_dir):iffilename.endswith(.xml):voc_fileos.path.join(voc_dir,filename)yolo_fileos.path.join(yolo_dir,filename.replace(.xml,.txt))convert_voc_to_yolo(voc_file,yolo_file,class_names)if__name____main__:class_names[none_defective,defective]voc_train_dirAVHDDD/labels_voc/trainyolo_train_dirAVHDDD/labels_yolo/trainconvert_all_voc_to_yolo(voc_train_dir,yolo_train_dir,class_names)voc_val_dirAVHDDD/labels_voc/valyolo_val_dirAVHDDD/labels_yolo/valconvert_all_voc_to_yolo(voc_val_dir,yolo_val_dir,class_names)YOLOv8训练代码安装YOLOv8库和依赖项gitclone https://github.com/ultralytics/ultralytics.gitcdultralytics pipinstall-rrequirements.txt训练模型fromultralyticsimportYOLOdeftrain_model(data_yaml_path,model_config,epochs,batch_size,img_size,augment):# 加载模型modelYOLO(model_config)# 训练模型resultsmodel.train(datadata_yaml_path,epochsepochs,batchbatch_size,imgszimg_size,augmentaugment)# 保存模型model.save(runs/train/avhddd/best.pt)if__name____main__:data_yaml_pathAVHDDD/data.yamlmodel_configyolov8n.yamlepochs100batch_size16img_size640augmentTruetrain_model(data_yaml_path,model_config,epochs,batch_size,img_size,augment)详细解释安装YOLOv8和依赖项克隆YOLOv8仓库并安装所有必要的依赖项。训练模型导入YOLOv8库。加载模型配置文件。调用model.train方法进行训练。保存训练后的最佳模型。运行训练脚本将上述脚本保存为一个Python文件例如train_yolov8_avhddd.py然后运行它。python train_yolov8_avhddd.py评估模型评估模型fromultralyticsimportYOLOdefevaluate_model(data_yaml_path,weights_path,img_size,conf_threshold):# 加载模型modelYOLO(weights_path)# 评估模型resultsmodel.val(datadata_yaml_path,imgszimg_size,confconf_threshold)# 打印评估结果print(results)if__name____main__:data_yaml_pathAVHDDD/data.yamlweights_pathruns/train/avhddd/best.ptimg_size640conf_threshold0.4evaluate_model(data_yaml_path,weights_path,img_size,conf_threshold)详细解释评估模型导入YOLOv8库。加载训练好的模型权重。调用model.val方法进行评估。打印评估结果。运行评估脚本将上述脚本保存为一个Python文件例如evaluate_yolov8_avhddd.py然后运行它。python evaluate_yolov8_avhddd.py一键运行脚本为了实现一键运行可以将图像预处理、训练和评估脚本合并到一个主脚本中并添加命令行参数来控制运行模式。importargparseimportosimportxml.etree.ElementTreeasETfromultralyticsimportYOLOdefconvert_voc_to_yolo(voc_file,yolo_file,class_names):treeET.parse(voc_file)roottree.getroot()widthint(root.find(size/width).text)heightint(root.find(size/height).text)withopen(yolo_file,w)asf:forobjinroot.findall(object):class_nameobj.find(name).textifclass_namenotinclass_names:continueclass_idclass_names.index(class_name)bboxobj.find(bndbox)x_minfloat(bbox.find(xmin).text)y_minfloat(bbox.find(ymin).text)x_maxfloat(bbox.find(xmax).text)y_maxfloat(bbox.find(ymax).text)x_center(x_minx_max)/2.0/width y_center(y_miny_max)/2.0/height w(x_max-x_min)/width h(y_max-y_min)/height f.write(f{class_id}{x_center}{y_center}{w}{h}\n)defconvert_all_voc_to_yolo(voc_dir,yolo_dir,class_names):os.makedirs(yolo_dir,exist_okTrue)forfilenameinos.listdir(voc_dir):iffilename.endswith(.xml):voc_fileos.path.join(voc_dir,filename)yolo_fileos.path.join(yolo_dir,filename.replace(.xml,.txt))convert_voc_to_yolo(voc_file,yolo_file,class_names)deftrain_model(data_yaml_path,model_config,epochs,batch_size,img_size,augment):# 加载模型modelYOLO(model_config)# 训练模型resultsmodel.train(datadata_yaml_path,epochsepochs,batchbatch_size,imgszimg_size,augmentaugment)# 保存模型model.save(runs/train/avhddd/best.pt)defevaluate_model(data_yaml_path,weights_path,img_size,conf_threshold):# 加载模型modelYOLO(weights_path)# 评估模型resultsmodel.val(datadata_yaml_path,imgszimg_size,confconf_threshold)# 打印评估结果print(results)defmain(mode):class_names[none_defective,defective]data_yaml_pathAVHDDD/data.yamlmodel_configyolov8n.yamlepochs100batch_size16img_size640conf_threshold0.4augmentTrueifmodeconvert:voc_train_dirAVHDDD/labels_voc/trainyolo_train_dirAVHDDD/labels_yolo/trainconvert_all_voc_to_yolo(voc_train_dir,yolo_train_dir,class_names)voc_val_dirAVHDDD/labels_voc/valyolo_val_dirAVHDDD/labels_yolo/valconvert_all_voc_to_yolo(voc_val_dir,yolo_val_dir,class_names)elifmodetrain:train_model(data_yaml_path,model_config,epochs,batch_size,img_size,augment)elifmodeeval:weights_pathruns/train/avhddd/best.ptevaluate_model(data_yaml_path,weights_path,img_size,conf_threshold)else:print(Invalid mode. Use convert, train, or eval.)if__name____main__:parserargparse.ArgumentParser(descriptionConvert, train, or evaluate YOLOv8 on the Anti-Vibration Hammer Defect Detection Dataset.)parser.add_argument(mode,typestr,choices[convert,train,eval],helpMode: convert, train, or eval)argsparser.parse_args()main(args.mode)详细解释命令行参数使用argparse库添加命令行参数控制脚本的运行模式转换、训练或评估。主函数根据传入的模式参数调用相应的转换、训练或评估函数。运行主脚本将上述脚本保存为一个Python文件例如main_yolov8_avhddd.py然后运行它。转换标注格式python main_yolov8_avhddd.py convert训练模型python main_yolov8_avhddd.py train评估模型python main_yolov8_avhddd.pyeval总结通过以上步骤你可以准备好防震锤缺陷检测数据集并使用YOLOv8进行训练和评估。希望这些信息对你有帮助
YOLO27防震锤缺陷检测数据集 防震锤数据集 1000张 防震锤 带标注 voc yolo 2 类 目标检测
防震锤缺陷检测数据集 1000张 防震锤 带标注 voc yolo分类名: (图片张数 标注个数)none_ defect ive:(6361 808)defective: (831 933)总数:(10002741)总类(nc): 2类使用YOLOv8进行训练的完整步骤。我们将确保所有步骤都是一键运行的以便你可以轻松地进行模型训练和评估。数据集介绍数据集概述数据集名称Anti-Vibration Hammer Defect Detection Dataset (AVHDDD)数据类型图像目标任务防震锤缺陷检测样本数量1000张图像标注格式VOC (XML) 和 YOLO (TXT)类别2类无缺陷 (none_defective)缺陷 (defective)数据集统计无缺陷 (none_defective)636张图像1808个框缺陷 (defective)831张图像933个框总计1000张图像2741个框数据集目录结构AVHDDD/ ├── images/ │ ├── train/ │ └── val/ ├── labels_voc/ │ ├── train/ │ └── val/ ├── labels_yolo/ │ ├── train/ │ └── val/ └── data.yaml数据集配置文件创建一个data.yaml文件配置数据集的路径和类别信息path:./AVHDDD# 数据集路径train:images/train# 训练集图像路径val:images/val# 验证集图像路径nc:2# 类别数names:[none_defective,defective]# 类别名称转换标注格式假设标注文件是VOC格式的XML文件我们需要将它们转换为YOLO格式的TXT文件。转换脚本importxml.etree.ElementTreeasETimportosdefconvert_voc_to_yolo(voc_file,yolo_file,class_names):treeET.parse(voc_file)roottree.getroot()widthint(root.find(size/width).text)heightint(root.find(size/height).text)withopen(yolo_file,w)asf:forobjinroot.findall(object):class_nameobj.find(name).textifclass_namenotinclass_names:continueclass_idclass_names.index(class_name)bboxobj.find(bndbox)x_minfloat(bbox.find(xmin).text)y_minfloat(bbox.find(ymin).text)x_maxfloat(bbox.find(xmax).text)y_maxfloat(bbox.find(ymax).text)x_center(x_minx_max)/2.0/width y_center(y_miny_max)/2.0/height w(x_max-x_min)/width h(y_max-y_min)/height f.write(f{class_id}{x_center}{y_center}{w}{h}\n)defconvert_all_voc_to_yolo(voc_dir,yolo_dir,class_names):os.makedirs(yolo_dir,exist_okTrue)forfilenameinos.listdir(voc_dir):iffilename.endswith(.xml):voc_fileos.path.join(voc_dir,filename)yolo_fileos.path.join(yolo_dir,filename.replace(.xml,.txt))convert_voc_to_yolo(voc_file,yolo_file,class_names)if__name____main__:class_names[none_defective,defective]voc_train_dirAVHDDD/labels_voc/trainyolo_train_dirAVHDDD/labels_yolo/trainconvert_all_voc_to_yolo(voc_train_dir,yolo_train_dir,class_names)voc_val_dirAVHDDD/labels_voc/valyolo_val_dirAVHDDD/labels_yolo/valconvert_all_voc_to_yolo(voc_val_dir,yolo_val_dir,class_names)YOLOv8训练代码安装YOLOv8库和依赖项gitclone https://github.com/ultralytics/ultralytics.gitcdultralytics pipinstall-rrequirements.txt训练模型fromultralyticsimportYOLOdeftrain_model(data_yaml_path,model_config,epochs,batch_size,img_size,augment):# 加载模型modelYOLO(model_config)# 训练模型resultsmodel.train(datadata_yaml_path,epochsepochs,batchbatch_size,imgszimg_size,augmentaugment)# 保存模型model.save(runs/train/avhddd/best.pt)if__name____main__:data_yaml_pathAVHDDD/data.yamlmodel_configyolov8n.yamlepochs100batch_size16img_size640augmentTruetrain_model(data_yaml_path,model_config,epochs,batch_size,img_size,augment)详细解释安装YOLOv8和依赖项克隆YOLOv8仓库并安装所有必要的依赖项。训练模型导入YOLOv8库。加载模型配置文件。调用model.train方法进行训练。保存训练后的最佳模型。运行训练脚本将上述脚本保存为一个Python文件例如train_yolov8_avhddd.py然后运行它。python train_yolov8_avhddd.py评估模型评估模型fromultralyticsimportYOLOdefevaluate_model(data_yaml_path,weights_path,img_size,conf_threshold):# 加载模型modelYOLO(weights_path)# 评估模型resultsmodel.val(datadata_yaml_path,imgszimg_size,confconf_threshold)# 打印评估结果print(results)if__name____main__:data_yaml_pathAVHDDD/data.yamlweights_pathruns/train/avhddd/best.ptimg_size640conf_threshold0.4evaluate_model(data_yaml_path,weights_path,img_size,conf_threshold)详细解释评估模型导入YOLOv8库。加载训练好的模型权重。调用model.val方法进行评估。打印评估结果。运行评估脚本将上述脚本保存为一个Python文件例如evaluate_yolov8_avhddd.py然后运行它。python evaluate_yolov8_avhddd.py一键运行脚本为了实现一键运行可以将图像预处理、训练和评估脚本合并到一个主脚本中并添加命令行参数来控制运行模式。importargparseimportosimportxml.etree.ElementTreeasETfromultralyticsimportYOLOdefconvert_voc_to_yolo(voc_file,yolo_file,class_names):treeET.parse(voc_file)roottree.getroot()widthint(root.find(size/width).text)heightint(root.find(size/height).text)withopen(yolo_file,w)asf:forobjinroot.findall(object):class_nameobj.find(name).textifclass_namenotinclass_names:continueclass_idclass_names.index(class_name)bboxobj.find(bndbox)x_minfloat(bbox.find(xmin).text)y_minfloat(bbox.find(ymin).text)x_maxfloat(bbox.find(xmax).text)y_maxfloat(bbox.find(ymax).text)x_center(x_minx_max)/2.0/width y_center(y_miny_max)/2.0/height w(x_max-x_min)/width h(y_max-y_min)/height f.write(f{class_id}{x_center}{y_center}{w}{h}\n)defconvert_all_voc_to_yolo(voc_dir,yolo_dir,class_names):os.makedirs(yolo_dir,exist_okTrue)forfilenameinos.listdir(voc_dir):iffilename.endswith(.xml):voc_fileos.path.join(voc_dir,filename)yolo_fileos.path.join(yolo_dir,filename.replace(.xml,.txt))convert_voc_to_yolo(voc_file,yolo_file,class_names)deftrain_model(data_yaml_path,model_config,epochs,batch_size,img_size,augment):# 加载模型modelYOLO(model_config)# 训练模型resultsmodel.train(datadata_yaml_path,epochsepochs,batchbatch_size,imgszimg_size,augmentaugment)# 保存模型model.save(runs/train/avhddd/best.pt)defevaluate_model(data_yaml_path,weights_path,img_size,conf_threshold):# 加载模型modelYOLO(weights_path)# 评估模型resultsmodel.val(datadata_yaml_path,imgszimg_size,confconf_threshold)# 打印评估结果print(results)defmain(mode):class_names[none_defective,defective]data_yaml_pathAVHDDD/data.yamlmodel_configyolov8n.yamlepochs100batch_size16img_size640conf_threshold0.4augmentTrueifmodeconvert:voc_train_dirAVHDDD/labels_voc/trainyolo_train_dirAVHDDD/labels_yolo/trainconvert_all_voc_to_yolo(voc_train_dir,yolo_train_dir,class_names)voc_val_dirAVHDDD/labels_voc/valyolo_val_dirAVHDDD/labels_yolo/valconvert_all_voc_to_yolo(voc_val_dir,yolo_val_dir,class_names)elifmodetrain:train_model(data_yaml_path,model_config,epochs,batch_size,img_size,augment)elifmodeeval:weights_pathruns/train/avhddd/best.ptevaluate_model(data_yaml_path,weights_path,img_size,conf_threshold)else:print(Invalid mode. Use convert, train, or eval.)if__name____main__:parserargparse.ArgumentParser(descriptionConvert, train, or evaluate YOLOv8 on the Anti-Vibration Hammer Defect Detection Dataset.)parser.add_argument(mode,typestr,choices[convert,train,eval],helpMode: convert, train, or eval)argsparser.parse_args()main(args.mode)详细解释命令行参数使用argparse库添加命令行参数控制脚本的运行模式转换、训练或评估。主函数根据传入的模式参数调用相应的转换、训练或评估函数。运行主脚本将上述脚本保存为一个Python文件例如main_yolov8_avhddd.py然后运行它。转换标注格式python main_yolov8_avhddd.py convert训练模型python main_yolov8_avhddd.py train评估模型python main_yolov8_avhddd.pyeval总结通过以上步骤你可以准备好防震锤缺陷检测数据集并使用YOLOv8进行训练和评估。希望这些信息对你有帮助