论文笔记:Bag of Freebies for Training Object Detection Neural Networks
这篇论文看起来像是一种总结类文章，文中并没有提出新的结构模型之类，但是对于以前的一些做法进行了整合并应用于baseline中。也就是文中一再强调的“Bag of Freebies”，即在不改变模型结构并且不改变损失函数的情况下，不牺牲前向传播时间，将mAP提升近五个点。 论文主要贡献如下： 首次系统地评估了不同目标检测流程中应用的多种训练启发式方法，为未来的研究提供了有价值的实践指导。 提出了一种
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相关文章
- 1. Bag of Freebies for Training Object Detection Neural Networks
- 2. 论文笔记:Bag of Freebies for Training Object Detection Neural Networks
- 3. Bag of Freebies for Training Object Detection Neural Networks 论文笔记
- 4. 论文解读-Bag of Freebies for Training Object Detection Neural Networks
- 5. 《Bag of Freebies for Training Object Detection Neural Networks》论文理解
- 6. 论文阅读：Bag of Freebies for Training Object Detection Neural Networks
- 7. Bag of Freebies for Training Object Detection Neural Networks学习笔记
- 8. 目标检测tricks:Bag of Freebies for Training Object Detection Neural Networks
- 9. 目标检测调优技巧：《Bag of Freebies for Training Object Detection Neural Networks》论文笔记
- 10. 目标检测训练优化Tricks：《Bag of Freebies for Training Object Detection Neural Networks》