faster-rcnn
This is an experimental implementation of Faster R-CNN using Chainer based on Ross Girshick's py-faster-rcnn codes.
Using anaconda is strongly recommended.
Python 2.7.6+, 3.4.3+, 3.5.1+
Chainer 1.9.1+
NumPy 1.9, 1.10, 1.11
Cython 0.23+
OpenCV 2.9+, 3.1+
pip install numpy pip install cython pip install chainer # for python3 conda install -c https://conda.binstar.org/menpo opencv3 # for python2 conda install opencv
There's a known problem in cpu_nms.pyx. But a workaround has been posted here (and see also the issue posted to the original py-faster-rcnn).
cd lib python setup.py build_ext -i cd ..
if [ ! -d data ]; then mkdir data; fi; cd data wget https://dl.dropboxusercontent.com/u/2498135/faster-rcnn/VGG16_faster_rcnn_final.model cd ..
NOTE: The model definition in faster_rcnn.py
has been changed, so if you already have the older pre-trained model file, please download it again to replace the older one with the new one.
wget http://vision.cs.utexas.edu/voc/VOC2007_test/JPEGImages/004545.jpg python forward.py --img_fn 004545.jpg --gpu 0
--gpu 0
turns on GPU. When you turn off GPU, use --gpu -1
or remove --gpu
option.
Summarization of Faster R-CNN layers used during inference
The region proposal layer (RPN) is consisted of AnchorTargetLayer
and ProposalLayer
. RPN takes feature maps from trunk network like VGG-16, and performs 3x3 convolution to it. Then, it applies two independent 1x1 convolutions to the output of the first 3x3 convolution. Resulting outputs are rpn_cls_score
and rpn_bbox_pred
.
The shape of rpn_cls_score
is (N, 2 * n_anchors, 14, 14)
because each pixel on the feature map has n_anchors
bboxes and each bbox should have 2 values that mean object/background.
The shape of rpn_bbox_pred
is (N, 4 * n_anchors, 14, 14)
because each pixel on the feature map has n_anchors
bboxes, and each bbox is represented with 4 values that mean left top x & y, width & height.
if [ ! -d data ]; then mkdir data; fi; cd data 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 tar xvf VOCtrainval_06-Nov-2007.tar tar xvf VOCtest_06-Nov-2007.tar tar xvf VOCdevkit_08-Jun-2007.tar rm -rf *.tar; cd ../
First, if you don't have docker and nvidia-docker, install them:
sudo apt-get update sudo apt-get install -y apt-transport-https ca-certificates sudo apt-key adv --keyserver hkp://p80.pool.sks-keyservers.net:80 --recv-keys 58118E89F3A912897C070ADBF76221572C52609D echo "deb https://apt.dockerproject.org/repo ubuntu-trusty main" | sudo tee /etc/apt/sources.list.d/docker.list sudo apt-get install -y linux-image-extra-$(uname -r) linux-image-extra-virtual sudo apt-get update sudo apt-get install -y docker-engine sudo service docker startand then build caffe docker image and run the converter to make a chainer model from the pre-trained caffe model.
cd docker bash install_caffe_docker.sh bash create_image.sh bash run_caffe_docker.sh cd ..It creates
data/VGG16.model
that is converted from pre-trained model in Caffe format. The pre-trained model is the one distributed in the official Model Zoo of Caffe wiki.
python train.py
Note that it is a visualization of the workflow DURING INFERENCE
下一篇:optnet
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