This project forked from yatenglg/focal-loss-pytorch
全中文注释.(The loss function of retinanet based on pytorch).(You can use it on one-stage detection task or classifical task, to solve data imbalance influence).用于one-stage目标检测算法,提升检测效果.你也可以在分类任务中使用该损失函数,解决数据不平衡问题.
Home Page: https://ptorch.com/news/253.html
GIthub使用指北:
1.想将项目拷贝到自己帐号下就fork一下.
2.持续关注项目更新就star一下
3.watch是设置接收邮件提醒的.
jupyter-notebook用法例子 请见:由于Github是国外网站,加载会稍慢
retainnet的实现请见:Retinanet-pytorch
retinanet论文损失函数
实现过程简易明了,全中文备注.
阿尔法α 参数用于调整类别权重
伽马γ 参数用于调整不同检测难易样本的权重,让模型快速关注于困难样本
加入 (1-pt)γ 平衡难易样本的权重,通过γ缩放因子调整,retainnet默认γ=2
论文中最终为带平衡因子的focal loss, 本项目实现的也是这个版本
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