/Caffe-YOLOv3-Windows

A windows caffe implementation of YOLO detection network

Primary LanguageC++OtherNOASSERTION

caffe-yolov3-windows

A caffe implementation of MobileNet-YOLO detection network , first train on COCO trainval35k then fine-tune on 07+12 , test on VOC2007

Network mAP Resolution Download NetScope Inference time (GTX 1080) Inference time (i5-4440)
MobileNet-YOLOv3-Lite 0.747 320 caffemodel graph 6 ms 150 ms
MobileNet-YOLOv3-Lite 0.757 416 caffemodel graph 11 ms 280 ms
  • the benchmark of cpu performance on Tencent/ncnn framework
  • the deploy model was made by merge_bn.py , or you can try my custom version
  • bn_model download here

Linux Version

MobileNet-YOLO

Performance

Compare with YOLO , (IOU 0.5)

Network mAP Weight size Resolution NetScope
MobileNet-YOLOv3-Lite 34.0* 21.5 mb 320 graph
MobileNet-YOLOv3-Lite 37.3* 21.5 mb 416 graph
MobileNet-YOLOv3 40.3* 22.5 mb 416 graph
YOLOv3-Tiny 33.1 33.8 mb 416
  • (*) testdev-2015 server was closed , here use coco 2014 minival

Oringinal darknet-yolov3

Converter

test on coco_minival_lmdb (IOU 0.5)

Network mAP Resolution Download NetScope
yolov3 54.4 416 caffemodel graph
yolov3-spp 59.3 608 caffemodel graph

Other models

You can find non-depthwise convolution network here , Yolo-Model-Zoo

network mAP resolution macc param
PVA-YOLOv3 0.703 416 2.55G 4.72M
Pelee-YOLOv3 0.703 416 4.25G 3.85M

Configuring and Building Caffe

Requirements

  • Visual Studio 2013 or 2015
  • CMake 3.4 or higher (Visual Studio and Ninja generators are supported)
  • Anaconda

The build step was the same as MobileNet-SSD-windows

> cd $caffe_root
> script/build_win.cmd 

Mobilenet-YOLO Demo

> cd $caffe_root/
> examples\demo_yolo_lite.cmd

If load success , you can see the image window like this

alt tag

Trainning Prepare

Download lmdb

Unzip into $caffe_root/

Please check the path exist "$caffe_root\examples\VOC0712\VOC0712_trainval_lmdb"

Trainning Mobilenet-YOLOv3

> cd $caffe_root/
> examples\train_yolov3_lite.cmd

Reference

https://github.com/weiliu89/caffe/tree/ssd

https://pjreddie.com/darknet/yolo/

https://github.com/gklz1982/caffe-yolov2

https://github.com/duangenquan/YoloV2NCS

https://github.com/eric612/Vehicle-Detection

https://github.com/eric612/MobileNet-SSD-windows

License and Citation

Please cite MobileNet-YOLO in your publications if it helps your research:

@article{MobileNet-YOLO,
  Author = {eric612,Avisonic},
  Year = {2018}
}