Rich feature hierachies for accurate object detection and semantic segmentation

这篇是经典的RCNN，用来处理object detection的。

先采用selective search的算法，找出2K左右的bounding box,经过一步warp,然后丢进CNN中，提取出4096的feature vector，再用200个SVM分别分类。

其中selective search使用fast mode，训练CNN采用了Supervised pre-training + Domain-specific fine-tuning的模式。分完类之后，还做了一步Bounding-box regression。input 是x,y,w,h，输出是groundtruth box。整个过程参数很多，需要注意。

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