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

Note :

• 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

Windows Version

Caffe-YOLOv3-Windows

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

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}
}