RFBNet_master
（使用RFBNet模型训练自己的voc数据，教程请阅读pdf文件及代码请下载压缩包！原作者模型地址） Use RFBNet model to train your own VOC data. Read the PDF file for the tutorial. Download the compressed package for the code. Original author model address:
https://github.com/ruinmessi/RFBNet
1、Installation，Environmental Construction（环境搭建）
Install PyTorch-0.4.0 by selecting your environment on the website and running the appropriate command. Clone this repository. This repository is mainly based on ssd.pytorch and Chainer-ssd, a huge thank to them. Note: We currently only support PyTorch-0.4.0 and Python 3+.
Compile the nms and coco tools:
./make.sh
Note: Check you GPU architecture support in utils/build.py, line 131. Default is:
2、Datasets（数据准备） To make things easy, we provide simple VOC and COCO dataset loader that inherits torch.utils.data.Dataset making it fully compatible with the torchvision.datasets API.
3、Data Production（数据制作）
voc的windows版本标注工具下载：https://download.csdn.net/download/yunxinan/11033039
4、Training（训练模型）
By default, we assume you have downloaded the file in the RFBNet/weights dir:
（训练于评估请参考我写的pdf和原作者的相关解释。为了国内方便下载权重其他地址：https://download.csdn.net/download/yunxinan/11033019）
First download the fc-reduced VGG-16 PyTorch base network weights at: https://s3.amazonaws.com/amdegroot-models/vgg16_reducedfc.pth or from our BaiduYun Driver
MobileNet pre-trained basenet is ported from MobileNet-Caffe, which achieves slightly better accuracy rates than the original one reported in the paper, weight file is available at:
https://drive.google.com/open?id=13aZSApybBDjzfGIdqN1INBlPsddxCK14 or BaiduYun Driver.
5、Initialization of Transfer Learning mkdir weights cd weights
wget https://s3.amazonaws.com/amdegroot-models/vgg16_reducedfc.pth To train RFBNet using the train script simply specify the parameters listed in train_RFB.py as a flag or manually change them. train： python train_RFB.py -d VOC -v RFB_vgg -s 300
6、Evaluation To evaluate a trained network: python test_RFB.py -d VOC -v RFB_vgg -s 300 --trained_model /path/to/model/weights
By default, it will directly output the mAP results on VOC2007 test or COCO minival2014. For VOC2012 test and COCO test-dev results, you can manually change the datasets in the test_RFB.py file, then save the detection results and submitted to the server.
7、Result
