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blood_cell_classifier's Introduction

血细胞分类

探索数据

对数据进行基本探索,分有四个文件夹,每个文件夹2800张左右图片,每张图片大小为240*320,彩色。 图片大多有倾斜,有黑色背景,可能是干扰项,但是因为有些目标白细胞在边缘处,也不方便做切割。 由于只有训练集,故将训练集分为训练集和测试集,测试集跟训练集是相同分布,故不作处理效果可能更好。

三种方案

图片分类,首先想到的是用经典CNN模型做迁移学习,

  • 通过经典CNN模型提取特征向量,在用简单神经网络训练新的分类器。测试集acc:0.993975903614457
  • 通过经典CNN模型fine-tuning,去掉分类层加上4分类器,锁定原始模型所有参数训练分类器参数,再放开一些层的参数进行微调,(经典CNN模型大多都很大,参数很多,调参太慢,放弃了)
  • 除了迁移学习,也可以自己搭一个CNN模型从头训练 -测试集acc:0.9728915662650602

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