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Pelee: A Real-Time Object Detection System on Mobile Devices

This repository contains the code for the following paper.

Pelee: A Real-Time Object Detection System on Mobile Devices (NeurIPS 2018)

The code is based on the SSD framework.

Citation

If you find this work useful in your research, please consider citing:

@incollection{NIPS2018_7466,
title = {Pelee: A Real-Time Object Detection System on Mobile Devices},
author = {Wang, Robert J and Li, Xiang and Ling, Charles X},
booktitle = {Advances in Neural Information Processing Systems 31},
editor = {S. Bengio and H. Wallach and H. Larochelle and K. Grauman and N. Cesa-Bianchi and R. Garnett},
pages = {1967--1976},
year = {2018},
publisher = {Curran Associates, Inc.},
url = {http://papers.nips.cc/paper/7466-pelee-a-real-time-object-detection-system-on-mobile-devices.pdf}
}

Results on VOC 2007

The table below shows the results on PASCAL VOC 2007 test.

Results on COCO

The table below shows the results on COCO test-dev2015.

Preparation

0. Install SSD (https://github.com/weiliu89/caffe/tree/ssd) following the instructions there, including: (1) Install SSD caffe; (2) Download PASCAL VOC 2007 and 2012 datasets; and (3) Create LMDB file. Make sure you can run it without any errors.

1. Download the pretrained PeleeNet model. By default, we assume the model is stored in $CAFFE_ROOT/models/

2. Clone this repository and create a soft link to $CAFFE_ROOT/examples

git clone https://github.com/Robert-JunWang/Pelee.git
ln -sf `pwd`/Pelee $CAFFE_ROOT/examples/pelee

Training & Testing

• Train a Pelee model on VOC 07+12:

  cd $CAFFE_ROOT
  python examples/pelee/train_voc.py

• Evaluate the model:

  cd $CAFFE_ROOT
  python examples/pelee/eval_voc.py
  
  

Models

• PASCAL VOC 07+12: Download (20.3M)

• PASCAL VOC 07+12+coco: Download (20.3M) Download (Model Merged BN with Conv）

• MS COCO: Download (21M)