论文：Speed/accuracy trade-offs for object detectors

时间 2021-01-12

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比较主流的目标检测网络，Faster R-CNN , R-FCN , SSD Introduction: 目标检测网络中， mPA。。。运行时间和占用内存也很重要，好的模型经常依赖于model ensemboing 和 multicrop 方法，这样对于应用来说too slow. 我们会探索现代检测系统 speed/accuracy trade-off。主要的贡献如下： 1.对现代卷积检测网络

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1. [论文笔记]Speed/accuracy trade-offs for modern convolutional object detectors
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4. Speed/accuracy trade-offs for modern convolutional object detectors
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6. Focal Loss for Dense Object Detection论文笔记
7. 【论文笔记 Detection】OHEM: Training Region-based Object Detectors with Online Hard Example Mining
8. 论文笔记：Training Region-based Object Detectors with Online Hard Example Mining
9. Training Region-based Object Detectors with Online Hard Example Mining论文讲解
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1. [论文笔记]Speed/accuracy trade-offs for modern convolutional object detectors
2. 【论文学习记录】Speed/accuracy trade-offs for modern convolutional object detectors
3. 【论文】cloud dbms:architectures and tradeoffs
4. Speed/accuracy trade-offs for modern convolutional object detectors
5. one-stage object detectors(1)
6. Focal Loss for Dense Object Detection论文笔记
7. 【论文笔记 Detection】OHEM: Training Region-based Object Detectors with Online Hard Example Mining
8. 论文笔记：Training Region-based Object Detectors with Online Hard Example Mining
9. Training Region-based Object Detectors with Online Hard Example Mining论文讲解
10. DetectoRS论文学习笔记

>>更多相关文章<<