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ssd-pytorch-1's Introduction

SSD:Single-Shot MultiBox Detector目标检测模型在Pytorch当中的实现


目录

  1. 所需环境 Environment
  2. 文件下载 Download
  3. 训练步骤 How2train
  4. 参考资料 Reference

所需环境

torch == 1.2.0

文件下载

训练所需的ssd_weights.pth可以在百度云下载。
链接: https://pan.baidu.com/s/1ltXCkuSxKRJUsLi0IoBg2A
提取码: uqnw

训练步骤

1、本文使用VOC格式进行训练。
2、训练前将标签文件放在VOCdevkit文件夹下的VOC2007文件夹下的Annotation中。
3、训练前将图片文件放在VOCdevkit文件夹下的VOC2007文件夹下的JPEGImages中。
4、在训练前利用voc2ssd.py文件生成对应的txt。
5、再运行根目录下的voc_annotation.py,运行前需要将classes改成你自己的classes。

classes = ["aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat", "chair", "cow", "diningtable", "dog", "horse", "motorbike", "person", "pottedplant", "sheep", "sofa", "train", "tvmonitor"]

6、就会生成对应的2007_train.txt,每一行对应其图片位置及其真实框的位置。
7、在训练前需要修改model_data里面的voc_classes.txt文件,需要将classes改成你自己的classes。
8、修改utils/config.py里面的NUM_CLASSES与需要训练的种类的个数相同。运行train.py即可开始训练。

mAP目标检测精度计算更新

更新了get_gt_txt.py、get_dr_txt.py和get_map.py文件。
get_map文件克隆自https://github.com/Cartucho/mAP
具体mAP计算过程可参考:https://www.bilibili.com/video/BV1zE411u7Vw

Reference

https://github.com/pierluigiferrari/ssd_keras
https://github.com/kuhung/SSD_keras

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