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 (ICLR 2018 workshop track)

The code is based on the SSD framework.

Citation

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

@article{wang2018pelee,
  title={Pelee: A Real-Time Object Detection System on Mobile Devices},
  author={Wang, Robert J and Li, Xiang and Ao, Shuang and Ling, Charles X},
  journal={arXiv preprint arXiv:1804.06882},
  year={2018}
}

Results on VOC 2007

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

Method	mAP (%)	FPS (Intel i7)	FPS (iPhone 6s)	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	9.3	23.8	15.9M
SSD+MobileNet	68.0	6.1	16.1	22.8	5.8M
Pelee	70.9	6.7	17.1	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]	Computational Cost (MACs)	# parameters
SSD300	25.1	43.1	25.8	34,360 M	34.30 M
YOLOv2-416	21.6	44.0	19.2	17,500 M	67.43 M
SSD+MobileNet	18.8	-	-	1,200 M	6.80 M
Pelee	22.4	38.3	22.9	1,290 M	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)

• MS COCO: Download (21M)