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README.md

Fast R-CNN

Installation

  1. Extract the source code: $ tar zxvf fast-rcnn.tgz; call the source directory $FRCNN
  2. Build Cython modules: $ cd $FRCNN && make (there will probably be some benign warnings that you can ignore)
  3. Build Caffe: $ cd $FRCNN/caffe-master and follow the Caffe installation instructions
  4. Establish symlinks for VOCdevkits
  5. Symlink $FRCNN/datasets/VOCdevkit2007 to where you have the PASCAL VOC 2007 devkit and data installed
  6. And similiarly for other PASCAL VOC 20xy datasets
  7. Symlink $FRCNN/datasets/cache to somewhere that will store cached dataset files

Usage

Train a Fast R-CNN detector. For example, train a VGG 16 network on VOC 2007 trainval:

./experiments/train_net.py --gpu 0 --solver models/VGG_16/solver.prototxt --weights /data/reference_caffe_nets/VGG_ILSVRC_16_layers.v2.caffemodel

Test a Fast R-CNN detector. For example, test the VGG 16 network on VOC 2007 test:

./experiments/test_net.py --gpu 1 --def models/VGG_16/test.prototxt --net snapshots/vgg16_fast_rcnn_iter_40000.caffemodel

Test output is written underneath $FRCNN/output.