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darknet_yolov3

Pytorch darknet to caffe

Modify from pytorch-caffe-darknet-convert,object_detetction_tools

Modified items :

  1. yolov3 output layer
  2. when pooling layer stide =1 , size =2 , assign size = 1
  3. upsample layer

Usage :

  1. Download weights from original darknet web
  2. Unmark custom_class in examples\ssd\ssd_detect.cpp
  3. Remake project
> python darknet2caffe.py yolov3.cfg yolov3.weights yolov3.prototxt yolov3.caffemodel
> cd $caffe_root
> sh demo_darknet_yolov3.sh

Retrain :

You can try retrain models to approach original darknet mAP , below was my test

Network mAP Resolution iters
yolov3-spp 58.7 608 100
yolov3-spp 59.0 608 200
yolov3-spp 59.8 608 1000