FPN

by unsky

unsky /FPN

Feature Pyramid Networks for Object Detection

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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

merged rcnn

|[email protected]|aeroplane|bicycle|bird|boat|bottle|bus|car|cat|chair|cow| |:--:|:-------:| -----:| --:| --:|-----:|--:|--:|--:|----:|--:| |0.788|0.8079| 0.8036| 0.8010| 0.7293|0.6743|0.8680|0.8766|0.8967|0.6122|0.8646|

|diningtable|dog |horse|motorbike|person |pottedplant|sheep|sofa|train|tv| |----------:|:--:|:---:| -------:| -----:| -------:|----:|---:|----:|--:| |0.7330|0.8855|0.8760| 0.8063| 0.7999| 0.5138|0.7905|0.7755|0.8637|0.7736|

shared rcnn

|[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

megred rcnn framework

Network overview: link

shared rcnn

Network overview: link

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 targets are range

[128,256,512]
.

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

shared rcnn

bash
./experiments/scripts/FP_Net_end2end.sh 1 FPN pascal_voc
./test.sh 1 FPN pascal_voc
megred rcnn
bash
 ./experiments/scripts/FP_Net_end2end_merge_rcnn.sh 0 FPN pascal_voc
 ./test_mergercnn.sh 0 FPN pascal_voc
0 1 is GPU id.

TODO List

  • [x] all tests passed
  • [x] evaluate object detection performance on voc
  • [x] evaluate merged rcnn version 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.

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