“Fast R-CNN”, Ross Girshick2015-04-30 (CNN; similar)⁠:

This paper proposes a Fast Region-based Convolutional Network method (Fast R-CNN) for object detection. Fast R-CNN builds on previous work to efficiently classify object proposals using deep convolutional networks. Compared to previous work, Fast R-CNN employs several innovations to improve training and testing speed while also increasing detection accuracy.

Fast R-CNN trains the very deep VGG-16 network 9× faster than R-CNN, is 213× faster at test-time, and achieves a higher mAP on PASCAL VOC 2012. Compared to SPPnet, Fast R-CNN trains VGG-16 3× faster, tests 10× faster, and is more accurate.

Fast R-CNN is implemented in Python and C++ (using Caffe) and is available under the open-source MIT License at Github.

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