Efficient Featurized Image Pyramid Network for Single Shot Detector

By Yanwei Pang†, Tiancai Wang†, Rao Muhammad Anwer, Fahad Shahbaz Khan, Ling Shao

† denotes equal contribution

Introduction

Single-stage object detectors have recently gained popularity due to their combined advantage of high detection accuracy and real-time speed. However, while promising results have been achieved by these detectors on standard-sized objects, their performance on small objects is far from satisfactory. To detect very small/large objects, classical pyramid representation can be exploited, where an image pyramid is used to build a feature pyramid (featurized image pyramid), enabling detection across a range of scales. Existing single-stage detectors avoid such a featurized image pyramid representation due to its memory and time complexity. In this paper, we introduce a light-weight architecture to efficiently produce featurized image pyramid in a single-stage detection framework.

Installation

Clone this repository. This repository is mainly based on ssd.pytorch and RFBNet.

    LFIP_ROOT=/path/to/clone/LFIP
    git clone https://github.com/vaesl/LFIP $LFIP_ROOT

The code was tested on Ubuntu 16.04, with Anaconda Python 3.5/6 and PyTorch v0.3.1. NVIDIA GPUs are needed for testing. After install Anaconda, create a new conda environment, activate the environment and install pytorch0.3.1.

    conda create -n LFIP python=3.5
    source activate LFIP
    conda install pytorch=0.3.1 torchvision -c pytorch

Install opencv.

    conda install opencv

Compile both COCOAPI and NMS:

    cd $LFIP_ROOT/
    ./make.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:

${$LFIP_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:

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

Trained Models

Please access to Google Driver or BaiduYun Driver to obtain our trained models for PASCAL VOC and COCO and put the models into corresponding directory(e.g. '~/weights/COCO/LFIP_COCO_300/'). Note that the access code for the BaiduYun Driver is jay3 and for the time being we only release models with 300*300 input size.

Evaluation

To check the performance reported in the paper:

python test_LFIP.py -d VOC -s 300 --trained_model /path/to/model/weights

where '-d' denotes datasets, VOC or COCO and '-s' represents image size, 300 or 512.

Citation

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

@article{Pang2019LFIP,
    title = {Efficient Featurized Image Pyramid Network for Single Shot Detection},
    author = {Yanwei Pang, Tiancai Wang, Rao Muhammad Anwer, Fahad Shahbaz Khan, Ling Shao},
    booktitle = {CVPR},
    year = {2019}
}

vaesl / LFIP