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2024年3月25日 · 但是，DETR大目标检测上性能是最好的，而小目标上稍差，而且基于match的loss导致学习很难收敛（即难以学习到最优的情况）。 DETR的总体框架如下，先通过CNN提取图像的特征；再送入到transformer encoder-decoder中，该编码器解码器的结构基本与transformer相同，主要是在输入部分和输出部分的修改；最后得到类别和bbox的预测，并通过二分匹配计算损失来优化网络。 2） 另一个embedding操作为positional encoding：即位置编码，即一组与输入 …

2021年10月15日 · Here are a few simple steps and strategies that you can use today to help reduce debt and, hopefully, become debt free. 1. Squeeze More Savings Out of Your Budget. Start by taking a close look at...

本文的损失函数分为两部分，和过去的检测方法一样，也是一部分为框的参数的损失，一部分为框的置信分数的损失。 不同的是，本文是先按照 集合预测的方法（set prediction），将两个集合中的元素进行一定关系的映射，再进行loss的计算。

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2021年9月21日 · 基于Transformer的目标检测器（比如：DETR）最开始也是没有Anchor的，这篇文章开始引入了Anchor Point机制（即Anchor-Free）来优化DETR，直接跳过了有Anchor（这里的Anchor指Anchor Box）阶段，之后会不会又有文章继续提出基于Anchor Box的Transformer目标检测器，重走一遍CNN目标 ...

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