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)
Oringinal darknet-yolov3
Converter
test on coco_minival_lmdb (IOU 0.5)
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
The build step was the same as MobileNet-SSD-windows
> cd $caffe_root
> script/build_win.cmd
Mobilenet-YOLO Demo
> cd $caffe_root/
> examplesdemo_yolo_lite.cmd
If load success , you can see the image window like this
Trainning Prepare
Download lmdb
Unzip into $caffe_root/
Please check the path exist "$caffe_rootexamplesVOC0712VOC0712_trainval_lmdb"
Trainning Mobilenet-YOLOv3
> cd $caffe_root/
> examplestrain_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}
}