其次,Se模块中包含一个全局平均池化操作(squeeze),两个全连接层(excitation)和一个Relu,以此特征提升敏感性。将特征信息导入Se-ResNet101网络进行池化、卷积等操作。最后,将全连接层的特征信息通过KNN算法進行预分类。最终得到准确率为98.83%,和ResNet系列网络对比,Se-ResNet101网络得到的准确率最高。 关键词:Se-...
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数据介绍 文件预览 相关论文 Code 分享讨论(0) 使用声明 启动Notebook开发 数据结构 ? 513.31M * 以上分析是由系统提取分析形成的结果,具体实际数据为准。 README.md ImageNet Pre-trained Weights for SE-ResNet-50, 101, and 152 These are converted for ChainerCV. Orignal weights are distributed at ...
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Explore and run machine learning code with Kaggle Notebooks | Using data from se_resnet101
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