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https://github.com/Robert-JunWang/Pelee
Pelee: A Real-Time Object Detection System on Mobile Devices
https://github.com/Robert-JunWang/Pelee
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Pelee: A Real-Time Object Detection System on Mobile Devices
- Host: GitHub
- URL: https://github.com/Robert-JunWang/Pelee
- Owner: Robert-JunWang
- License: apache-2.0
- Created: 2018-01-17T16:31:34.000Z (about 7 years ago)
- Default Branch: master
- Last Pushed: 2019-01-04T05:52:11.000Z (about 6 years ago)
- Last Synced: 2024-08-03T21:02:36.836Z (6 months ago)
- Language: Jupyter Notebook
- Homepage:
- Size: 637 KB
- Stars: 885
- Watchers: 51
- Forks: 255
- Open Issues: 54
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-AutoML-and-Lightweight-Models - Robert-JunWang/Pelee
README
# 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](https://arxiv.org/pdf/1804.06882.pdf) (NeurIPS 2018)
The code is based on the [SSD](https://github.com/weiliu89/caffe/tree/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](https://drive.google.com/open?id=1KJHKYQ2nChZXlxroZRpg-tRsksTXUhe9) | [76.4](https://drive.google.com/open?id=1ZKAP9d7Hzxi9Jc09ApL2BH1SgXXZPJk4)|
## 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](https://drive.google.com/file/d/1OBzEnD5VEB_q_B8YkLx-i3PMHVO-wagk/view?usp=sharing) 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
```shell
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:
```shell
cd $CAFFE_ROOT
python examples/pelee/train_voc.py
```
- Evaluate the model:
```shell
cd $CAFFE_ROOT
python examples/pelee/eval_voc.py
## Models
- PASCAL VOC 07+12: [Download (20.3M)](https://drive.google.com/open?id=1KJHKYQ2nChZXlxroZRpg-tRsksTXUhe9)
- PASCAL VOC 07+12+coco: [Download (20.3M)](https://drive.google.com/open?id=1ZKAP9d7Hzxi9Jc09ApL2BH1SgXXZPJk4)
[Download (Model Merged BN with Conv）](https://drive.google.com/open?id=1AH46csPPufZl3NYwk6xcHDmnYVwmN05e)
- MS COCO: [Download (21M)](https://drive.google.com/open?id=1NXfmytr_55Njg8h6MXVflo3-tvhxYdm8)