Low Shot Box Correction for Weakly Supervised Object Detection

This code repo is built on faster-rcnn.pytorch.

The final camera-ready paper is now available at IJCAI proceeding

Installation and Preparation

Firstly, clone the code

git clone https://github.com/ptx9363/BCNet.git

and then follow faster-rcnn.pytorch 's preparation to install the environment and dependency. This repo's specific dependencies are shown below:

• Python 3.5.6
• Torch 0.4.1
• Torchvision 0.2.1
• Numpy 1.15.4

Dataset

We use VOC2007 dataset in our most experiments. We have run weakly-supervised method, OICR, to provide pseudo bounding boxes for images in VOC2007. Some of our experiments are trained from weakly pre-trained models. In general, we provide all of pretrained models and generated labels here.

• VOC2007 dataset with pseudo labels, data
• Pretrained models, models
• Edge boxes proposals, data

the final data folder should be placed like:

BCNet/data/pretrained_model
      data/VOCdevkit/VOC2007
      data/edge_boxes_data

Train and Test

Before training, the cuda libs are required to compiled by:

pip install cython cffi

cd libs

./setup.sh

From now, we have provided train&test code for BCNet with multi-stage and image-level regularization. Just run:

./train_test_vgg16.sh

All of the model modules are avaiable now while more train&test scripts will be released soon.

Citation

@article{jjfaster2rcnn,
    Author = {Jianwei Yang and Jiasen Lu and Dhruv Batra and Devi Parikh},
    Title = {A Faster Pytorch Implementation of Faster R-CNN},
    Journal = {https://github.com/jwyang/faster-rcnn.pytorch},
    Year = {2017}
}

@inproceedings{renNIPS15fasterrcnn,
    Author = {Shaoqing Ren and Kaiming He and Ross Girshick and Jian Sun},
    Title = {Faster {R-CNN}: Towards Real-Time Object Detection
             with Region Proposal Networks},
    Booktitle = {Advances in Neural Information Processing Systems ({NIPS})},
    Year = {2015}
}