深度学习【51】物体检测：PVANET: Deep but Lightweight Neural Networks for Real-time Object Detection
这篇论文在coco数据集中取得了第二名的成绩。其目标是在取得state-of-the-art的正确率情况下，最小化计算量。主要工作是优化前面的特征抽取网络，设计原则是通道数要少，但是网络更深。该论文的网络设计主要点为：1、concatencated ReLU；2、inception模块；3、HyperNet，加入不同尺度特征图融合。 C.ReLU C.ReLU的结构如下： 其前向传播过程为：输入数
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- 1. 论文阅读--PVANET: Deep but Lightweight Neural Networks for Real-time Object Detection
- 2. PVANET: Deep but Lightweight Neural Networks for Real-time Object Detection
- 3. PVANET: Deep but Lightweight Neural Networks for Real-time Object Detection - arxiv 2016.08
- 4. 【PVANet】《PVANET：Deep but Lightweight Neural Networks for Real-time Object Detection》
- 5. 【目标检测】PVANET:Deep but Lightweight Neural Networks for Real-time Object Detection
- 6. 论文笔记: PVANet: Lightweight Deep Neural Networks for Real-time Object Detection
- 7. Deep Neural Networks for Object Detection
- 8. 深度学习【53】物体检测：Feature Pyramid Networks for Object Detection
- 9. Deep Neural Networks for Object Detection论文翻译
- 10. (37)[NIPS13] Deep Neural Networks for Object Detection
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相关文章
- 1. 论文阅读--PVANET: Deep but Lightweight Neural Networks for Real-time Object Detection
- 2. PVANET: Deep but Lightweight Neural Networks for Real-time Object Detection
- 3. PVANET: Deep but Lightweight Neural Networks for Real-time Object Detection - arxiv 2016.08
- 4. 【PVANet】《PVANET：Deep but Lightweight Neural Networks for Real-time Object Detection》
- 5. 【目标检测】PVANET:Deep but Lightweight Neural Networks for Real-time Object Detection
- 6. 论文笔记: PVANet: Lightweight Deep Neural Networks for Real-time Object Detection
- 7. Deep Neural Networks for Object Detection
- 8. 深度学习【53】物体检测：Feature Pyramid Networks for Object Detection
- 9. Deep Neural Networks for Object Detection论文翻译
- 10. (37)[NIPS13] Deep Neural Networks for Object Detection