Noise-Aware Fully Webly Supervised Object Detection

By Yunhang Shen, Rongrong Ji, Zhiwei Chen, Xiaopeng Hong, Feng Zheng, Jianzhuang Liu, Mingliang Xu, Qi Tian.

CVPR 2020 Paper.

This project is based on Detectron.

Introduction

License

NA-fWebSOD is released under the Apache 2.0 license. See the NOTICE file for additional details.

Citing NA-fWebSOD

If you find NA-fWebSOD useful in your research, please consider citing:

@inproceedings{NA-fWebSOD_2020_CVPR,
	author = {Shen, Yunhang and Ji, Rongrong and Chen, Zhiwei and Hong, Xiaopeng and Zheng, Feng and Liu, Jianzhuang and Xu, Mingliang and Tian, Qi},
	title = {Noise-Aware Fully Webly Supervised Object Detection},
	booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
	year = {2020},
}   

Installation

Requirements:

• NVIDIA GPU, Linux, Python3.6
• Caffe2 in pytorch v1.3.0, various standard Python packages, and the COCO API; Instructions for installing these dependencies are found below

Caffe2

Clone the pytorch repository:

# pytorch=/path/to/clone/pytorch
git clone https://github.com/pytorch/pytorch.git $pytorch
cd $pytorch
git checkout v1.3.0
git submodule update --init --recursive

Install Python dependencies:

pip3 install -r $pytorch/requirements.txt

Build caffe2:

cd $pytorch
sudo USE_OPENCV=On USE_LMDB=On BUILD_BINARY=On python3 setup.py install

Other Dependencies

Install the COCO API:

pip3 install 'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI'

Install the pycococreator:

pip3 install git+git://github.com/waspinator/pycococreator.git@0.2.0

NA-fWebSOD

Clone the NA-fWebSOD repository:

# NA-fWebSOD=/path/to/clone/NA-fWebSOD
git clone https://github.com/shenyunhang/NA-fWebSOD.git $NA-fWebSOD
cd $NA-fWebSOD
git submodule update --init --recursive

Install Python dependencies:

pip3 install -r requirements.txt

Set up Python modules:

make

Build the custom C++ operators library:

./build_ops.sh

Dataset Preparation

Training Data

Download flickr_voc from this here and untar File:

tar xvf flickr_voc.tar
ln -s /path/to/clone/flickr_voc $NA-fWebSOD/detectron/datasets/data/flickr_voc

Download flickr_coco from this here and untar File:

tar xvf flickr_coco.tar
ln -s /path/to/clone/flickr_coco $NA-fWebSOD/detectron/datasets/data/flickr_coco

Download flickr_clean from this here and untar File:

tar xvf flickr_clean.tar
ln -s /path/to/clone/flickr_clean $NA-fWebSOD/detectron/datasets/data/flickr_clean

Testing Data

Please follow this to creating symlinks for PASCAL VOC.

Download MCG proposal from here to detectron/datasets/data, and transform it to pickle serialization format:

cd detectron/datasets/data
tar xvzf MCG-Pascal-Main_trainvaltest_2007-boxes.tgz
cd ../../../
python3 tools/convert_mcg.py voc_2007_train detectron/datasets/data/MCG-Pascal-Main_trainvaltest_2007-boxes detectron/datasets/data/proposals/mcg_voc_2007_train.pkl
python3 tools/convert_mcg.py voc_2007_val detectron/datasets/data/MCG-Pascal-Main_trainvaltest_2007-boxes detectron/datasets/data/proposals/mcg_voc_2007_val.pkl
python3 tools/convert_mcg.py voc_2007_test detectron/datasets/data/MCG-Pascal-Main_trainvaltest_2007-boxes detectron/datasets/data/proposals/mcg_voc_2007_test.pkl

Finnally, We have the following directory structure:

NA-fWebSOD
|_ detectron
|_ datasets
|_ data
|_ flickr_voc
|_ images
|_ images.json
|_ images.txt
|_ ...
|_ flickr_coco
|_ images
|_ images.json
|_ images.txt
|_ ...
|_ flickr_clean
|_ images
|_ images.json
|_ images.txt
|_ ...
|_ VOC2007
|_ coco
|_ ...

Model Preparation

Download models from this here and untar File:

tar xvf models.tar
mv models $NA-fWebSOD

Then We have the following directory structure:

NA-fWebSOD
|_ models
|  |_ VGG
|  |_ |_ VGG_ILSVRC_16_layers_v1.pkl
|_ ...

Quick Start: Using NA-fWebSOD

NA-fWebSOD

Flickr voc

./scripts/train_wsl.sh --cfg configs/flickr_voc/webly_wsddn_V-16-C5_1x.yaml OUTPUT_DIR experiments/webly_wsddn_v-16_flickr_voc_`date +'%Y-%m-%d_%H-%M-%S'`

Flickr clean

./scripts/train_wsl.sh --cfg configs/flickr_clean/webly_wsddn_V-16-C5_1x.yaml OUTPUT_DIR experiments/webly_wsddn_v-16_flickr_clean_`date +'%Y-%m-%d_%H-%M-%S'`

Flickr coco

./scripts/train_wsl.sh --cfg configs/flickr_coco/webly_wsddn_V-16-C5_1x.yaml OUTPUT_DIR experiments/webly_wsddn_v-16_flickr_coco_`date +'%Y-%m-%d_%H-%M-%S'`