hualuluu/pelee

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

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]	[email protected]	[email protected]	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)