Awesome

Windows Caffe

**This is an experimental, fixed some bugs from https://github.com/runhang/caffe-ssd-windows and I add following items into project

Support MobileNetV2 (source from MobileNetv2-SSDLite )
Support yolov2 loss layer (source from my git caffe-yolov2-windows)
Rplace group convolution layer from depthwise layer , speed 4x up faster with group convolution

Linux Version

MobileNet-SSD-linux

Windows Setup

Requirements

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

Optional Dependencies

Python for the pycaffe interface. Anaconda Python 2.7 or 3.5 x64 (or Miniconda)
Matlab for the matcaffe interface.
CUDA 7.5 or 8.0 (use CUDA 8 if using Visual Studio 2015)
cuDNN v5

We assume that cmake.exe and python.exe are on your PATH.

Configuring and Building Caffe (CPU Only)

Create a python2.7 env from Anaconda and activate

> cd $caffe_root/script
> build_win.cmd

For Visual 2013

Edit build_win.cmd and set varible MSVC_VERSION=12

For GPU

config build_win.cmd and set CPU_Only flag to 0

Running Caffe

Download SSD_300x300 deploy model and save at

$caffe_root\models\VGGNet\VOC0712\SSD_300x300\

Download deploy weights from original web and save at

$caffe_root\models\MobileNet\

> cd $caffe_root/
> dectect.cmd

Python Usage

> cd $caffe_root
> python examples\ssd\test_ssd.py data\VOC0712\000166.jpg models\MobileNet\MobileNetSSD_deploy.prototxt models\MobileNet\MobileNetSSD_deploy.caffemodel

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

alt tag

Optional detector

Set detect.cmd varible "detector" (0,1) to switch VGG or MobileNet

Trainning Prepare

Download lmdb

Unzip into $caffe_root/

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

Trainning VGG_SSD Caffe

Download SSD_300x300 pretrain weights and save at

$caffe_root\models\VGGNet\

> cd $caffe_root/
> train.cmd

Trainning Mobilenet_V1_SSD

Download pre-train weights from original web and save at

$caffe_root\models\MobileNet\

> cd $caffe_root/
> train_mobilenet.cmd

Trainning Mobilenet_V2_SSD

> cd $caffe_root/
> train_mobilenet_v2.cmd

Trainning MobilenetYOLO_V2

> cd $caffe_root/
> train_yolo.cmd

Trainning own dataset and deploy MobilentSSD_V1

follow this project step

MobilenetYOLO_V2 Demo

> cd $caffe_root/
> demo_yolo.cmd

alt tag

Video Demo

> cd $caffe_root/
> demo.cmd or demov2.cmd (MobilenetSSD_V2)

MobilnetSSD

MobilnetSSD_V2

Webcam Demo

> cd $caffe_root/
> demo_webcam.cmd

Vehicle deploy model

CLASS NAME

char* CLASSES2[6] = { "__background__","bicycle", "car", "motorbike", "person","cones" };

Model and Weights MobilnetSSD_V1

weights

model

Vehicle detection using MobilnetSSD_V2

> cd $caffe_root/
> demo.cmd or demov2_custom.cmd

Demo Video MobilnetSSD_V1

Demo Video MobilnetSSD_V2

Demo Video MobilenetYOLO_V2

> cd $caffe_root/
> demo_yolo_custom.cmd