论文阅读--PVANET: Deep but Lightweight Neural Networks for Real-time Object Detection
论文地址：https://arxiv.org/abs/1608.08021
算法实现：https://github.com/CharlesShang/TFFRCNN
网络设计原则： “less channels with more layers” 。
网络性能：83.8% mAP (mean average precision) on VOC2007 and 82.5% mAP on VOC2012 (2nd place), while taking only 750ms/image on Intel i7-6700K CPU with a single core and 46ms/image on NVIDIA Titan X GPU. Theoretically, our network requires only 12.3% of the computational cost compared to ResNet-101, the winner on VOC2012.
算法有点：提出一个轻量级的的特征提取模型，减少计算资源的消耗，可以完成实时目标检测。
网络组件：
转载于:https://my.oschina.net/clgo/blog/1535253