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deep-learning-for-image-process's Introduction

深度学习在图像处理中的应用

前言

  • 本项目是对本人研究生期间的研究内容进行整理总结,总结的同时也希望能够帮助更多的小伙伴。后期如果有学习到新的知识也会与大家一起分享。
  • 本项目将通过以下两种深度学习网络框架搭建网络。
    1)使用Pytorch进行网络的搭建与训练
    2)使用Tensorflow(内部的keras模块)进行网络的搭建与训练

教程目录(后期会根据学习内容增加)

  • 图像分类
    • LeNet(已完成)
    • AlexNet(已完成)
    • VggNet(已完成)
    • GoogLeNet(已完成)
    • ResNet(已完成)
    • MobileNet(已完成)
    • ShuffleNet (已完成)
  • 目标识别检测
    • Faster RCNN/FPN(已完成)
    • SSD/RetinaNet (已完成)
    • YOLOv3 SPP (已完成)
  • 目标分割

所需环境

  • Anaconda3(建议使用)
  • python3.6.8
  • pycharm2020.2.3 (IDE)
  • pytorch 1.7.1 (pip package)
  • torchvision 0.8.2 (pip package)
  • tensorflow 2.2.0 (pip package)

如果有什么问题,也可以到我的CSDN中一起讨论。
https://blog.csdn.net/hallobike/article/details/115300124

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