model可以自己任意改全连接
主要做一些数据更新的一些事情，背景是实际nsfw数据集移植到业务时，很容易存在某些类型的大量的误识别
基于新的数据集查看clip结果，通过标注新增数据（主要是看正常图片是不是被分类为黄色）
通过逐步标注训练来提高准确率
先去小数据集，大概百万左右找出badcase（3k左右量级即可），更新数据集
再增加数据集大小，再找出对应大小的badcase，再增加数据集训练
最后再在一个自己的数据集上测试，可以千万级别左右的，可以下定一个准确率的结论
这样可以快速有效的提升针对业务内容下，误识别率高的问题
初步的话大概误识别可以从0.2%降到0.02%，让模型可用性提高
React
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