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基于深度学习Resnet网络实现多种植物识别分类源码(准确率98.5%)+数据集+模型+使用说明.zip 【资源介绍】 checkpoints为训练好的resnet18植物识别模型,迭代次数为100次,准确率达到98.5% config.py为配置文件,里面设置迭代次数、bachsize、优化器选择、损失函数选择、训练验证数据配置路径、学习率等等。可自定义修改改变 dataset.py 为数据预处理功能代码,包含训练数据预处理、数据增强等功能 focal_loss.py为焦点损失函数实现 model文件夹中的resnet.py为resnet算法实现,里面包含多种网络结构(含resnet18、34、50、101、152),可任意选择替换用来训练,该项目使用的resnet18网络训练测试的。 six_classes_plant_data文件夹内为植物识别分类数据集、内含有数据增强脚本。 test_img文件夹为测试图片,可自定义增加 cosine_lr_scheduler.py余弦退火实现 2、predict.py 预测识别代码 3、train.py 训练代码
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基于深度学习Resnet网络实现多种植物识别分类源码(准确率98.5%)+数据集+模型+使用说明.zip (1380个子文件)
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