Enriched Feature Guided Refinement Network for Object Detection

By Jing Nie1†, Rao Muhammad Anwer†, Hisham Cholakkal, Fahad Shahbaz Khan, Yanwei Pang1‡, Ling Shao
† denotes equal contribution，‡ Corresponding author

Introduction

We propose a single-stage detection framework that jointly tackles the problem of multi-scale object detection and class imbalance. Rather than designing deeper networks, we introduce a simple yet effective feature enrichment scheme to produce multi-scale contextual features. We further introduce a cascaded refinement scheme which first instills multi-scale contextual features into the prediction layers of the single-stage detector in order to enrich their discriminative power for multi-scale detection. Second, the cascaded refinement scheme counters the class im- balance problem by refining the anchors and enriched features to improve classification and regression.

Installation

Clone this repository. This repository is mainly based mainly based on SSD_Pytorch

    EFGR_ROOT=/path/to/clone/EFGR
    git clone https://github.com/Ranchentx/EFGRNet.git $EFGR_ROOT

The code was tested on Ubuntu 16.04, with Anaconda Python 3.6 and PyTorch v0.4.1. NVIDIA GPUs are needed for testing. After install Anaconda, create a new conda environment, activate the environment and install pytorch0.4.1.

    conda create -n EFGRNet python=3.6
    source activate EFGRNet
    conda install pytorch=0.4.1 torchvision -c pytorch

Install opencv and COCOAPI.

    pip install opencv-python
    pip install pycocotools

Compile NMS:

    cd $EFGR_ROOT/
    ./make.sh

Compile DCN:

    ./compile.sh

Download

To evaluate the performance reported in the paper, Pascal VOC and COCO dataset as well as our trained models need to be downloaded.

VOC Dataset

Directly download the images and annotations from the VOC website and put them into $LFIP_ROOT/data/VOCdevkit/.

Create the VOCdevkit folder and make the data(or create symlinks) folder like:

${$EFGR_ROOT}
|-- data
`-- |-- VOCdevkit
    `-- |-- VOC2007
        |   |-- annotations
        |   |-- ImageSets
        |   |-- JPEGImages
        |-- VOC2012
        |   |-- annotations
        |   |-- ImageSets
        |   |-- JPEGImages
        |-- results

COCO Dataset

Download the images and annotation files from coco website coco website.

Place the data (or create symlinks) to make the data folder like:

${$EFGR_ROOT}
|-- data
`-- |-- coco
    `-- |-- annotations
        |   |-- instances_train2014.json
        |   |-- instances_val2014.json
        |   |-- image_info_test-dev2015.json
        `-- images
        |   |-- train2014
        |   |-- val2014
        |   |-- test2015
        `-- cache

Training

python train_coco.py --cfg ./configs/EFGRNet_vgg_coco_dcn.yaml

Testing

Note: All testing configs are in EFGRNet_vgg_coco_dcn.yaml, you can change it by yourself.
To evaluate a trained network:

python eval_dcn.py --cfg ./configs/EFGRNet_vgg_coco_dcn.yaml --weights ./eval_weights

Models

COCO EFGRNet_VGG320; BaiduYun Driver
COCO EFGRNet_VGG512; BaiduYun Driver

Citation

Please cite our paper in your publications if it helps your research:

@article{Jing2019EFGR,
    title = {Enriched Feature Guided Refinement Network for Object Detection},
    author = {Jing Nie, Rao Muhammad Anwer, Hisham Cholakkal, Fahad Shahbaz Khan， Yanwei Pang, Ling Shao},
    booktitle = {ICCV},
    year = {2019}
}

ys0823/EFGRNet