This Python implementation is built on a fork of Faster R-CNN. Please see the official README.md for more details. Faster R-CNN was initially described in an arXiv tech report and was subsequently published in NIPS 2015. Faster R-CNN is released under the MIT License.
- Basic installation
- Demo
- [Taining instrallation] (#installation for training)
- Train
- Clone the repository
# Make sure to clone with --recursive
git clone --recursive https://github.com/jorisgu/jg_pfr.git
-
Build the Cython modules
cd $FRCN_ROOT/lib make
-
Build Caffe and pycaffe
cd $FRCN_ROOT/caffe-fast-rcnn # Now follow the Caffe installation instructions here: # http://caffe.berkeleyvision.org/installation.html # If you're experienced with Caffe and have all of the requirements installed # and your Makefile.config in place, then simply do: make -j8 && make pycaffe
-
Download pre-computed Faster R-CNN detectors
cd $FRCN_ROOT ./data/scripts/fetch_faster_rcnn_models.sh
This will populate the
$FRCN_ROOT/data
folder withfaster_rcnn_models
. Seedata/README.md
for details. These models were trained on VOC 2007 trainval.
To run the demo
cd $FRCN_ROOT
./tools/demo.py
-
Download the training, validation, test data and VOCdevkit
wget http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtrainval_06-Nov-2007.tar wget http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtest_06-Nov-2007.tar wget http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCdevkit_08-Jun-2007.tar
-
Extract all of these tars into one directory named
VOCdevkit
tar xvf VOCtrainval_06-Nov-2007.tar tar xvf VOCtest_06-Nov-2007.tar tar xvf VOCdevkit_08-Jun-2007.tar
-
It should have this basic structure
$VOCdevkit/ # development kit $VOCdevkit/VOCcode/ # VOC utility code $VOCdevkit/VOC2007 # image sets, annotations, etc. # ... and several other directories ...
-
Create symlinks for the PASCAL VOC dataset
cd $FRCN_ROOT/data ln -s $VOCdevkit VOCdevkit2007
Pre-trained ImageNet models can be downloaded for the three networks described in the paper: ZF and VGG16.
cd $FRCN_ROOT
./data/scripts/fetch_imagenet_models.sh
VGG16 comes from the Caffe Model Zoo, but is provided here for your convenience. ZF was trained at MSRA.
experiments/scripts/faster_rcnn_alt_opt.sh
.
Output is written underneath $FRCN_ROOT/output
.
cd $FRCN_ROOT
./experiments/scripts/faster_rcnn_alt_opt.sh [GPU_ID] [NET] [--set ...]
# GPU_ID is the GPU you want to train on
# NET in {ZF, VGG_CNN_M_1024, VGG16} is the network arch to use
# --set ... allows you to specify fast_rcnn.config options, e.g.
# --set EXP_DIR seed_rng1701 RNG_SEED 1701