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WanFang13 / C-MIL

Code for C-MIL: Continuation Multiple Instance Learning for Weakly Supervised Object Detection
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C-MIL

Code for C-MIL: Continuation Multiple Instance Learning for Weakly Supervised Object Detection.

New: Pytorch version of C-MIL is avalable at here. Thanks for the contribution of shenyunhang.

Environments

Detection Samples

Train and Test

  1. Install the dependencies

    cd ./C-MIL
export DIR=$(pwd) 

luarocks install hdf5 matio protobuf rapidjson loadcaffe xml

cd $DIR/libs/functions
sh install.sh

cd $DIR/layers
luarocks make

  2. Download dataset, proposals and ImageNet pre-trained model

    Download VOC2007 from: http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtrainval_06-Nov-2007.tar http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtest_06-Nov-2007.tar

    Download proposals from: https://dl.dropboxusercontent.com/s/orrt7o6bp6ae0tc/selective_search_data.tgz

    Download VGGF from: http://www.robots.ox.ac.uk/~vgg/software/deep_eval/releases/bvlc/VGG_CNN_F.caffemodel https://gist.githubusercontent.com/ksimonyan/a32c9063ec8e1118221a/raw/6a3b8af023bae65669a4ceccd7331a5e7767aa4e/VGG_CNN_F_deploy.prototxt

    mkdir $DIR/data    
mkdir $DIR/output

    The data folder has the following structure:

    $C-MIL/data/datasets/VOCdevkit_2007/
$C-MIL/data/datasets/VOCdevkit_2007/VOCcode
$C-MIL/data/datasets/VOCdevkit_2007/VOC2007
$C-MIL/data/datasets/VOCdevkit_2007/...
$C-MIL/data/datasets/proposals/
$C-MIL/data/models/
$C-MIL/data/results/

  3. Train, test and evaluate

    cd $DIR
# train
th train_cmil.lua 0 SSW
# test
th test_cmil.lua 0 SSW
# evaluate
th detection_mAP.lua 0 SSW output/path/to/scorefiles/score_test_epoch20.h5 2

Acknowledgements

Acknowledgements will be added later.