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matconvnet-1.0-beta24\CONTRIBUTING.md (1562, 2017-03-24)
matconvnet-1.0-beta24\COPYING (741, 2017-03-24)
matconvnet-1.0-beta24\doc\blocks.tex (32022, 2017-03-24)
matconvnet-1.0-beta24\doc\figures\imnet.pdf (18884, 2017-03-24)
matconvnet-1.0-beta24\doc\figures\pepper.pdf (702358, 2017-03-24)
matconvnet-1.0-beta24\doc\figures\svg\conv.svg (68592, 2017-03-24)
matconvnet-1.0-beta24\doc\figures\svg\convt.svg (65347, 2017-03-24)
matconvnet-1.0-beta24\doc\figures\svg\matconvnet-blue.svg (6561, 2017-03-24)
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matconvnet-1.0-beta24\doc\fundamentals.tex (33879, 2017-03-24)
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# Fast-RCNN demo This folder contains an example implementation of Fast-RCNN [1] in MatConvNet. The example trains and test on the PASCAL VOC 2007 data. There are three entry-point scripts: * `fast_rcnn_demo.m`: runs the original Caffe model imported in MatConvNet. * `fast_rcnn_train.m`: trains a new model from scratch, using pre-computed proposals. * `fast_rcnn_evaluate.m`: evaluates the trained model. Note that the code does not ship with a proposal generation method, so proposals must be precomputed (using e.g. edge boxes or selective search windows). The `fast_rcnn_demo.m` code should run out of the box, downloading the model as needed. To test the training code using the first GPU on your system, use something like: run matlab/vl_setupnn addpath examples/fast_rcnn fast_rcnn_train('train',struct('gpus',1)) ; fast_rcnn_evaluate('gpu',1) ; ## References 1. *Fast R-CNN*, R. Girshick, International Conference on Computer Vision (ICCV), 2015.

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