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https://github.com/eric612/Caffe-YOLOv3-Windows
A windows caffe implementation of YOLO detection network
https://github.com/eric612/Caffe-YOLOv3-Windows
caffe caffe-yolov2 caffemodel mobilenet-yolo visual-studio windows yolo yolov2 yolov3
Last synced: 12 days ago
JSON representation
A windows caffe implementation of YOLO detection network
- Host: GitHub
- URL: https://github.com/eric612/Caffe-YOLOv3-Windows
- Owner: eric612
- License: other
- Created: 2018-01-04T02:40:25.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2018-11-17T04:09:45.000Z (over 5 years ago)
- Last Synced: 2024-02-29T07:36:19.837Z (4 months ago)
- Topics: caffe, caffe-yolov2, caffemodel, mobilenet-yolo, visual-studio, windows, yolo, yolov2, yolov3
- Language: C++
- Homepage:
- Size: 286 MB
- Stars: 212
- Watchers: 18
- Forks: 128
- Open Issues: 21
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
Lists
- awesome-yolo-object-detection - eric612/Caffe-YOLOv3-Windows - YOLOv3-Windows?style=social"/> : A windows caffe implementation of YOLO detection network. (Other Versions of YOLO)
README
# 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](https://github.com/eric612/MobileNet-YOLO/tree/master/models/yolov3)|[graph](http://ethereon.github.io/netscope/#/gist/816d4d061c77d42246c5c9d49c4cbcf4)|6 ms|150 ms
MobileNet-YOLOv3-Lite|0.757|416|[caffemodel](https://github.com/eric612/MobileNet-YOLO/tree/master/models/yolov3)|[graph](http://ethereon.github.io/netscope/#/gist/816d4d061c77d42246c5c9d49c4cbcf4)|11 ms|280 ms
* the [benchmark](https://github.com/eric612/MobileNet-YOLO/tree/master/benchmark) of cpu performance on Tencent/ncnn framework
* the deploy model was made by [merge_bn.py](https://github.com/chuanqi305/MobileNet-SSD/blob/master/merge_bn.py) , or you can try my custom [version](https://github.com/eric612/MobileNet-YOLO/tree/master/examples/merge_bn)
* bn_model download [here](https://drive.google.com/file/d/1jB-JvuoMlLHvAhefGCwLGh_oBldcsfW3/view?usp=sharing)
## Linux Version
[MobileNet-YOLO](https://github.com/eric612/MobileNet-YOLO)
## Performance
Compare with [YOLO](https://pjreddie.com/darknet/yolo/) , (IOU 0.5)
Network|mAP|Weight size|Resolution|NetScope
:---:|:---:|:---:|:---:|:---:
[MobileNet-YOLOv3-Lite](https://github.com/eric612/MobileNet-YOLO/tree/master/models/yolov3_coco)|34.0*|[21.5 mb](https://drive.google.com/file/d/1bXZtB_wZBu1kOeagYtZgsjLq2CX0BGFD/view?usp=sharing)|320|[graph](http://ethereon.github.io/netscope/#/gist/b65f6b955e99c7d4c29a4b8008669f90)|
[MobileNet-YOLOv3-Lite](https://github.com/eric612/MobileNet-YOLO/tree/master/models/yolov3_coco)|37.3*|[21.5 mb](https://drive.google.com/file/d/1bXZtB_wZBu1kOeagYtZgsjLq2CX0BGFD/view?usp=sharing)|416|[graph](http://ethereon.github.io/netscope/#/gist/b65f6b955e99c7d4c29a4b8008669f90)|
[MobileNet-YOLOv3](https://github.com/eric612/MobileNet-YOLO/tree/master/models/yolov3_coco)|40.3*|[22.5 mb](https://drive.google.com/file/d/1G0FeQ7_ETc3zPn5HayhKi8Dz1-I5hU--/view?usp=sharing)|416|[graph](http://ethereon.github.io/netscope/#/gist/0ec45a4ca896553a20f9f9c16e80149f)|
YOLOv3-Tiny|33.1|33.8 mb|416
* (*) testdev-2015 server was closed , here use coco 2014 minival
### Oringinal darknet-yolov3
[Converter](https://github.com/eric612/MobileNet-YOLO/tree/master/models/darknet_yolov3)
test on coco_minival_lmdb (IOU 0.5)
Network|mAP|Resolution|Download|NetScope|
:---:|:---:|:---:|:---:|:---:
yolov3|54.4|416|[caffemodel](https://drive.google.com/file/d/12nLE6GtmwZxDiulwdEmB3Ovj5xx18Nnh/view?usp=sharing)|[graph](http://ethereon.github.io/netscope/#/gist/59c75a50e5b91d6dd80a879df3cfaf55)
yolov3-spp|59.3|608|[caffemodel](https://drive.google.com/file/d/17b5wsR9tzbdrRnyL_iFEvofJ8jCmQ1ff/view?usp=sharing)|[graph](http://ethereon.github.io/netscope/#/gist/71edbfacf4d39c56f2d82cbcb739ae38)
## Other models
You can find non-depthwise convolution network here , [Yolo-Model-Zoo](https://github.com/eric612/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](https://cmake.org/) 3.4 or higher (Visual Studio and [Ninja](https://ninja-build.org/) generators are supported)
- Anaconda
The build step was the same as [MobileNet-SSD-windows](https://github.com/eric612/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
### Trainning Prepare
Download [lmdb](https://drive.google.com/open?id=19pBP1NwomDvm43xxgDaRuj_X4KubwuCZ)
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}
}