Awesome
PyramidBox: A Context-assisted Single Shot Face Detector
Introduction
A MXNet implementation of PyramiBbox:A Context-assisted Single Shot Face Detector. If you want to learn more details,please refer to the original paper.
@inproceedings{Tang2018PyramidBoxAC,
title={PyramidBox: A Context-Assisted Single Shot Face Detector},
author={Xu Tang and Daniel K. Du and Zeqiang He and Jingtuo Liu},
booktitle={ECCV},
year={2018}
}
I train PyramidBox with WIDER FACE dataset,results are as follows:
| Easy mAP | Medium mAP | Hard mAP | |
|---|---|---|---|
| paper | 96.1 | 95.0 | 88.9 |
| this repo | 92.5 | 90.8 | 83.3 |
I think mainly reasons that this repo can not get the same precision as paper as follows:
- I use batch size 4 because of memory limitations,which is 16 in the paper
- some parameters are not metioned in the paper
Here are several examples of succesful detection outputs:
Details
I implement following structures metioned in the paper:
- Low-Level FPN
- max-in-out layer
- PyramidAnchors
- Context-sensitive Prediction Module
- Data-anchor sampling
- Learning rate warmup and cosine decay
Dependencies
Implement Details
- Ubuntu 16.04 LTS
- CUDA 9.0
- cuDNN 7.0.5
Preparation
git clone git@github.com:JJXiangJiaoJun/gluon_PyramidBox.git
cd gluon_PyramidBox
Download and prepare data
- download WIDER FACE dataset into
widerface/downloads
$$ tree widerface/downloads
widerface/downloads
├── eval_tools.zip
├── Submission_example.zip
├── wider_face_split.zip
├── WIDER_test.zip
├── WIDER_train.zip
└── WIDER_val.zip
- Prepare data: unzip data, annotations and eval_tools to
./widerface
python tool/prepare.py
$$ tree widerface -L 1
widerface
├── downloads
├── eval_tools
├── wider_face_split
├── WIDER_train
└── WIDER_val
- Prepare custom val dataset for quick validation (crop and resize to 640)
$$ python tool/build_custom_val.py
$$ tree widerface -L 1
widerface
├── downloads
├── eval_tools
├── WIDER_custom
├── wider_face_split
├── WIDER_train
└── WIDER_val
Train on WIDER FACE Datasets
train vgg16 based pyramidbox with 1 gpus.I only implement VGG16 as backbone currently:
python train_end2end.py --use-bn
or you can see more details:
python train_end2end.py --help
Evalution
eval your own model on WIDER FACE Datasets:
python eval.py --use-bn --model models/pyramidbox/pyramidbox_best.params