Awesome
Recurrent Scale Approximation (RSA) for Object Detection
Codebase for Recurrent Scale Approximation for Object Detection in CNN published at ICCV 2017, [arXiv]. Here we offer the training and test code for two modules in the paper, scale-forecast network
and recurrent scale approximation (RSA)
. Models for face detection trained on some open datasets are also provided.
<span style="color:red">Note: This project is still underway. Please stay tuned for more features soon!</span>
Codebase at a Glance
train/
: Training code for modules scale-forecast network
and RSA
predict/
: Test code for the whole detection pipeline
afw_gtmiss.mat
: Revised face data annotation mentioned in Section 4.1 in the paper.
Grab and Go (Demo)
Caffe models for face detection trained on popular datasets.
- Base RPN model:
predict/output/ResNet_3b_s16/tot_wometa_1epoch
, trained onWiderface
(fg/bg),COCO
(bg only) andImageNet Det
(bg only) - RSA model:
predict/output/ResNet_3b_s16_fm2fm_pool2_deep/65w
, trained onWiderface
,COCO
, andImageNet Det
Steps to run the test code:
-
Compile CaffeMex_v2 with matlab interface
-
Add
CaffeMex_v2/matlab/
to matlab search path -
See tips in
predict/script_start.m
and run it! -
After processing for a few minutes, the detection and alignment results will be shown in an image window. Please click the image window to view all results. If you set line 8 in
script_start.m
tofalse
as default, you should observe some results as above.
Train Your Own Model
Still in progress, this part will be released later.
FAQ
We will list the common issues of this project as time goes. Stay tuned! :)
Citation
Please kindly cite our work if it helps your research:
@inproceedings{liu_2017_rsa,
Author = {Yu Liu and Hongyang Li and Junjie Yan and Fangyin Wei and Xiaogang Wang and Xiaoou Tang},
Title = {Recurrent Scale Approximation for Object Detection in CNN},
Journal = {IEEE International Conference on Computer Vision},
Year = {2017}
}
Acknowledgment
We appreciate the contribution of the following researchers:
Dong Chen @Microsoft Research, some basic ideas are inspired by him when Yu Liu worked as an intern at MSR.
Jiongchao Jin @Beihang University, some baseline results are provided by him.