/MobileNet-YOLO

A caffe implementation of MobileNet-YOLO detection network

Primary LanguageC++OtherNOASSERTION

MobileNet-YOLO Caffe

MobileNet-YOLO

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

Knowledge Transfer

I use the following training path to improve accuracy , and decrease lite version trainning time

  • First , train MobileNet-YOLOv3 on coco dataset
  • Second , train MobileNet-YOLOv3-Lite on coco dataset , pretrain weights use the first step output
  • Finally , train MobileNet-YOLOv3-Lite on voc dataset , pretrain weights use the second step output

Windows Version

Caffe-YOLOv3-Windows

Oringinal darknet-yolov3

Converter

test on coco_minival_lmdb (IOU 0.5)

Network mAP Resolution Download NetScope
yolov3 54.2 416 caffemodel graph
yolov3-spp 59.8 608 caffemodel graph
  • I haven't implement correct_yolo_boxes and relative function , so here only support square input resolution

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

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

Build , Run and Training

See wiki

License and Citation

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

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