[train hyper-parameters: Namespace(batch_size=16, lr=0.001, lrf=0.01)]
[epoch: 1]
train loss:0.0333 test accuracy:0.8855
[epoch: 2]
train loss:0.0116 test accuracy:0.9771
[epoch: 3]
train loss:0.0071 test accuracy:0.9771
[epoch: 4]
train loss:0.0044 test accuracy:0.9771
[epoch: 5]
train loss:0.0021 test accuracy:0.9809
[epoch: 6]
train loss:0.0044 test accuracy:0.9656
[epoch: 7]
train loss:0.0018 test accuracy:0.9656
[epoch: 8]
train loss:0.0017 test accuracy:0.9809
[epoch: 9]
train loss:0.0006 test accuracy:0.9733
[epoch: 10]
train loss:0.0004 test accuracy:0.9885
[epoch: 11]
train loss:0.0010 test accuracy:0.9809
[epoch: 12]
train loss:0.0005 test accuracy:0.9542
[epoch: 13]
train loss:0.0003 test accuracy:0.9847
[epoch: 14]
train loss:0.0020 test accuracy:0.9542
[epoch: 15]
train loss:0.0011 test accuracy:0.9847
[epoch: 16]
train loss:0.0003 test accuracy:0.9809
[epoch: 17]
train loss:0.0001 test accuracy:0.9847
[epoch: 18]
train loss:0.0001 test accuracy:0.9771
[epoch: 19]
train loss:0.0000 test accuracy:0.9847
[epoch: 20]
train loss:0.0000 test accuracy:0.9847
[epoch: 21]
train loss:0.0001 test accuracy:0.9885
[epoch: 22]
train loss:0.0001 test accuracy:0.9809
[epoch: 23]
train loss:0.0000 test accuracy:0.9809
[epoch: 24]
train loss:0.0000 test accuracy:0.9847
[epoch: 25]
train loss:0.0000 test accuracy:0.9847
[epoch: 26]
train loss:0.0000 test accuracy:0.9847
[epoch: 27]
train loss:0.0000 test accuracy:0.9847
[epoch: 28]
train loss:0.0000 test accuracy:0.9847
[epoch: 29]
train loss:0.0000 test accuracy:0.9847
[epoch: 30]
train loss:0.0000 test accuracy:0.9847
[epoch: 31]
train loss:0.0000 test accuracy:0.9847
[epoch: 32]
train loss:0.0000 test accuracy:0.9847
[epoch: 33]
train loss:0.0000 test accuracy:0.9847
[epoch: 34]
train loss:0.0000 test accuracy:0.9847
[epoch: 35]
train loss:0.0000 test accuracy:0.9847
[epoch: 36]
train loss:0.0000 test accuracy:0.9847
[epoch: 37]
train loss:0.0000 test accuracy:0.9847
[epoch: 38]
train loss:0.0000 test accuracy:0.9847
[epoch: 39]
train loss:0.0000 test accuracy:0.9847
[epoch: 40]
train loss:0.0000 test accuracy:0.9847
[epoch: 41]
train loss:0.0000 test accuracy:0.9847
[epoch: 42]
train loss:0.0000 test accuracy:0.9847
[epoch: 43]
train loss:0.0000 test accuracy:0.9847
[epoch: 44]
train loss:0.0000 test accuracy:0.9847
[epoch: 45]
train loss:0.0000 test accuracy:0.9847
[epoch: 46]
train loss:0.0000 test accuracy:0.9847
[epoch: 47]
train loss:0.0000 test accuracy:0.9847
[epoch: 48]
train loss:0.0000 test accuracy:0.9847
[epoch: 49]
train loss:0.0000 test accuracy:0.9847
[epoch: 50]
train loss:0.0000 test accuracy:0.9847
没有合适的资源?快使用搜索试试~ 我知道了~
温馨提示
【包含项目的完整代码、数据集和训练好的权重文件等等,代码经过多次测试可以直接使用】 项目总大小:751MB 数据集为三种咖啡豆图像,Arabica、Debris_Shell、Endosperm,共1300张图像,数据划分好了训练集和测试集 【代码介绍,使用vgg16,参数量为大约为6kw左右】 网络训练的时候采用cos 学习率自动衰减,训练了50个epoch。模型在测试集最好的表现达到98%精度,加大epoch可以增加精度。在run_results 目录下存有最好的权重文件,以及训练日志和loss、精度曲线等等 预测的时候,只需要运行predict即可,代码会自动将inference下所有图片推理,并取前三个概率最大类别的绘制在左上角 【训练自己的数据参考readme文件,不需要更改参数,只需要摆放好数据集即可。超参数代码会自动生成,例如分类类别个数等等】
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基于VGG网络对3种咖啡豆图像分类的迁移学习项目 (1325个子文件)
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