Comprehensive Attention Self-Distillation for Weakly-Supervised Object Detection

This is the official implementation of:

Zeyi Huang*, Yang Zou*, Vijayakumar Bhagavatula, and Dong Huang, Comprehensive Attention Self-Distillation for Weakly-Supervised Object Detection, NeurIPS 2020, arxiv version.

Citation:

@article{huang2020comprehensive,
  title={Comprehensive Attention Self-Distillation for Weakly-Supervised Object Detection},
  author={Huang, Zeyi and Zou, Yang and Kumar, BVK and Huang, Dong},
  journal={Advances in Neural Information Processing Systems},
  volume={33},
  year={2020}
}

Installation

Requirements

• Python == 3.7
• Pytorch == 1.1.0
• Torchvision == 0.3.0
• Cuda == 10.0
• cython
• scipy
• sklearn
• opencv
• GPU: TITAN RTX (24G of memory)

Note: To train with GPU of small memory, CASD_IW is partially parallelized. Fully parallelized version is coming soon. Thanks for your patience.

Preparation

1. Clone the repository

git clone https://github.com/DeLightCMU/CASD.git

2. Compile the CUDA code

cd CASD/lib
bash make_cuda.sh

3. Download the training, validation, test data and the VOCdevkit

mkdir data
cd data/
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

4. 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_08-Jun-2007.tar

5. Create symlinks for PASCAL VOC dataset

cd CASD/data
ln -s VOCdevkit VOCdevkit2007

6. Download pretrained ImageNet weights from here, and put it in the data/imagenet_weights/

7. Download selective search proposals from here, and put it in the data/selective_search_data/

Training and Testing

Train a vgg16 Network on VOC 2007 trainval

bash experiments/scripts/train_faster_rcnn.sh 0 pascal_voc vgg16

Test a vgg16 Network on VOC 2007 test

bash experiments/scripts/test_faster_rcnn.sh 0 pascal_voc vgg16

Acknowledgement

We borrowed code from MLEM, PCL, and Faster-RCNN.