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Fast R-CNN
Fast R-CNNRoss GirshickMicrosoft paper proposes a Fast Region-based ConvolutionalNetwork method(Fast R-CNN )for object detection. FastR-CNN builds on previous work to efficiently classify ob-ject proposals using deep convolutional networks. Com-pared to previous work, Fast R-CNN employs several in-novations to improve training and testing speed while alsoincreasing detection accuracy. Fast R-CNN trains the verydeep VGG16 network 9 faster than R-CNN , is 213 fasterat test-time, and achieves a higher mAP on PASCAL VOC2012. Compared to SPPnet, Fast R-CNN trains VGG16 3 faster, tests 10 faster, and is more accurate. Fast R-CNNis implemented in Python and C++ (using Caffe) and isavailable under the open-source MIT License IntroductionRecently, deep ConvNets [14,16] have significantly im-proved image classification [14] and object detection [9,19]accuracy.
Fast R-CNN Ross Girshick Microsoft Research rbg@microsoft.com Abstract 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 ob-
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