Feature Pyramid Network on caffe

This is the unoffical version Feature Pyramid Network for Feature Pyramid Networks for Object Detection https://arxiv.org/abs/1612.03144

results

FPN(resnet50)-end2end result is implemented without OHEM and train with pascal voc 2007 + 2012 test on 2007

[email protected]	aeroplane	bicycle	bird	boat	bottle	bus	car	cat	chair	cow
0.7833	0.8585	0.8001	0.7970	0.7174	0.6522	0.8668	0.8768	0.8929	0.5842	0.8658

diningtable	dog	horse	motorbike	person	pottedplant	sheep	sofa	train	tv
0.7022	0.8891	0.8680	0.7991	0.7944	0.5065	0.7896	0.7707	0.8697	0.7653

framework

the red and yellow are shared params

about the anchor size setting

In the paper the anchor setting is Ratios： [0.5,1,2],scales :[8,]

With the setting and P2~P6, all anchor sizes are [32,64,128,512,1024],but this setting is suit for COCO dataset which has so many small targets.

but the voc dataset has so many [128,256,512]targets.

So, we desgin the anchor setting:Ratios： [0.5,1,2],scales :[8,16], this is very import for voc dataset.

usage

download voc07,12 dataset ResNet50.caffemodel and rename to ResNet50.v2.caffemodel

cp ResNet50.v2.caffemodel data/pretrained_model/

• OneDrive download: link

In my expriments, the codes require ~10G GPU memory in training and ~6G in testing. your can design the suit image size, mimbatch size and rcnn batch size for your GPUS.

compile caffe & lib

cd caffe-fpn
mkdir build
cd build
cmake ..
make -j16 all
cd lib
make 

train & test

./experiments/scripts/FP_Net_end2end.sh 1 FPN pascal_voc
./test.sh 1 FPN pascal_voc

TODO List

• all tests passed
• evaluate object detection performance on voc

feature pyramid networks for object detection

Lin, T. Y., Dollár, P., Girshick, R., He, K., Hariharan, B., & Belongie, S. (2016). Feature pyramid networks for object detection. arXiv preprint arXiv:1612.03144.