Chenhao Lin, Siwen Wang, Dongqi Xu, Yu Lu*, Wayne Zhang.
SenseTime Research, SenseTime.
- Note that * means the corresponding author.
Object Instance Mining (OIM) for weakly supervised object detection that effectively mines all possible instances with only image-level annotation using spatial and appearance graphs.
The paper has been accepted by AAAI 2020. For more details, please refer to our paper.
- Requirements for
Caffeandpycaffe(see: Caffe installation instructions)
Note: Caffe must be built with support for Python layers!
# In your Makefile.config, make sure to have this line uncommented
WITH_PYTHON_LAYER := 1- Python packages you might not have:
cython,python-opencv,easydict - MATLAB
- Clone the OIM repository
# Make sure to clone with --recursive
git clone --recursive https://github.com/XXXXX- Build the Cython modules
cd $OIM_ROOT/lib
make- Build Caffe and pycaffe
cd $OIM_ROOT/caffe-oim
# Following the Caffe installation instructions here:
# http://caffe.berkeleyvision.org/installation.html
make all -j
make pycaffe- Download VOCdevkit datasets
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/voc2012/VOCdevkit_18-May-2011.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_18-May-2011.tar- Get pre-computed Selective Search object proposals and put into $OIM_ROOT/data/selective_search_data.
cd $OIM_ROOT
./data/scripts/fetch_selective_search_data.sh- Download ImageNet pre-trained models available in the Caffe Model Zoo.
cd $OICR_ROOT
./data/scripts/fetch_imagenet_models.sh- Train on VOC 2007 trainval:
./tools/train_oim.sh oim_train models/VGG16/solver.prototxt 0.2 0.2 5.0 data/imagenet_models/$VGG16_model_name- Test and evaluate on VOC 2007 trainval and test:
./tools/val_oim.sh oim_trainval models/VGG16/test.prototxt output/oim_train_st2/voc_2007_trainval/vgg16_oim_st2_iter_20000.caffemodel./tools/test_oim.sh oim_test models/VGG16/test.prototxt output/oim_train_st2/voc_2007_trainval/vgg16_oim_st2_iter_20000.caffemodelFor mAP, run the python code tools/reval.py
./tools/reval.py $output_dir --imdb voc_2007_test --matlabFor CorLoc, run the python code tools/reval_discovery.py
./tools/reval_discovery.py $output_dir --imdb voc_2007_trainval
@inproceedings{lin2020object,
title={Object Instance Mining for Weakly Supervised Object Detection},
author={Chenhao Lin, Siwen Wang, Dongqi Xu, Yu Lu, Wayne Zhang},
journal={arXiv preprint arXiv:2002.01087},
year={2020}
}
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