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.

| Method | mAP (%) | FPS (Intel i7) |FPS (NVIDIA TX2) |FPS (iPhone 8) | # parameters |:-------|:-----:|:-------:|:-------:|:-------:|:-------:| | YOLOv2-288 | 69.0 | 1.0 | - | - | 58.0M | | DSOD300_smallest| 73.6 | 1.3 | - | - |5.9M | | Tiny-YOLOv2 | 57.1 | 2.4 | - | 23.8 | 15.9M | | SSD+MobileNet | 68.0 | 6.1 | 82 | 22.8 |5.8M | | Pelee | 70.9 | 6.7 | 125 | 23.6 | 5.4M |

| Method | 07+12 | 07+12+coco |:-------|:-----:|:-------:| | SSD300 | 77.2 | 81.2| | SSD+MobileNet | 68 | 72.7| | Pelee | 70.9 | 76.4|

Results on COCO

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

| Method | mAP@[0.5:0.95] | mAP@0.5 |mAP@0.75|FPS (NVIDIA TX2) | # parameters |:-------|:-----:|:-------:|:-------:|:-------:|:-------:| | SSD300 | 25.1 | 43.1 | 25.8 | - | 34.30 M | | YOLOv2-416| 21.6 | 44.0 | 19.2 | 32.2|67.43 M | | YOLOv3-320| - | 51.5| - | 21.5|67.43 M | | TinyYOLOv3-416| - | 33.1 | - | 105|12.3 M | | SSD+MobileNet-300 | 18.8 | - | - | 80 | 6.80 M | | SSDLite+MobileNet V2-320| 22 | - | - | 61 | 6.80 M | | Pelee-304 | 22.4 | 38.3 | 22.9 | 120 |5.98 M |

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)