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Dog-Breed-Identification-Gluon

Kaggle 120种狗分类比赛 Gluon实现代码
通过Gluon教程,边学边练。使用renset152_v1得到0.37900分数

复现

  • 在Kaggle上下载训练数据和测试数据,在reorg_dog_data设置相关路径并运行,整理数据集
  • model中设置好预训练网络和输出网络
  • 运行Pre_Training_Data生成预训练数据
  • train中调整参数并运行进行训练
  • 运行pretest生成结果csv文件,提交到kaggle

原理

杨培文的代码中学到的,如果使用imagenet网络进行预训练,锁住特征层的话,那可以先把所有数据都都过一边特征层网络,这样在后面进行分类输出网络的训练时会省去很多时间和显存,并且在训练的时候可以把bs开到很大,而且在过特征层网络的时候,可以把bs设的小一点,而且不用反向传播,速度也不会太慢,这样就解决了在低配电脑上无法训练的问题。而且用最新版mxnet输出的特征数据非常小,70M左右,后面的分类层即使用cpu也能跑很顺畅

Dog-Breed-Identification-Gluon

Dog-Breed-Identification-Gluon

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