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RetinaFace-Cpp: RetinaFace detector with C++

原作者: [db:作者] 来自: 网络 收藏 邀请

开源软件名称:

RetinaFace-Cpp

开源软件地址:

https://gitee.com/huiwei13/RetinaFace-Cpp

开源软件介绍:

RetinaFace-Cpp

RetinaFace detector with C++

official RetinaFace

I convert mobilenet-0.25 mxnet model (trained by yangfly) to caffe model

I convert R50 mxnet model to caffe model BaiDuYun | Google Drive

  • I have checked the output of the two models be the same.

  • For same input images, the output of the two detector (python version and cpp version) is same.

(the code is too simple, only for reference 23333)

This is anchor parsing demo, NO INFERENCE, you should replace your own inference header and code (caffe/ncnn/feather .etc) in the source code

I create QQ group: 760688309 for communicating


Update 2019.5.28

R50:Because of the GPU memory limited, I set max(width, height) to 1000 and test it on WiderFace_val set for SINGLE SCALE, NO MULTI-SCALE, NO FLIP, results as follows:

wider valeasymediumhard
python version92.2588.8664.02
cpp version91.3687.1562.22

Update 2019.5.27

mnet:I test on WiderFace_val set for SINGLE SCALE, NO MULTI-SCALE, NO FLIP, results as follows:

wider valeasymediumhard
python version83.2877.0239.52
cpp version83.0476.8439.43

You can see the two detect results as results/cpp_detect and results/python_detect. The main cause of the difference in test results is the numerical precision difference between C++ and python.

Time Benchmark

VGA (640x480)

platformnetthreads 1/2/4(ms)inference
qcom625mnet418/262/202NCNN
qcom625mnet379/244/180NCNN(optmize)
qcom835mnet137.37/82.97/62.79 by hanson-youngNCNN
qcom835mnet125.10/74.52/75.09 by hanson-youngNCNN(optmize)

TODO

  • implement bbox_vote
  • multi-scale test
  • speed benchmark (CPU/ARM)
  • new models

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