# computer-vision-learning

**Repository Path**: madao33/computer-vision-learning

## Basic Information

- **Project Name**: computer-vision-learning
- **Description**: some classical computer vision papers learning and codes implementation
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2022-01-14
- **Last Updated**: 2022-01-14

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# computer-vison-learning(机器视觉经典论文学习及复现)

## 前言

该仓库存储的是个人阅读的机器视觉的经典论文，包括相关的笔记以及尝试复现的代码，有相关同好的可以相互交流，目前复现的代码及相关参考仓库如下

## LeNet-5

* paper: [Lecun Y ,  Bottou L . Gradient-based learning applied to document recognition[J]. Proceedings of the IEEE, 1998, 86(11):2278-2324.](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=726791)

* codes: [activatedgeek/LeNet-5](https://github.com/activatedgeek/LeNet-5)

* LeNet-5网络框架学习笔记及仿真: [LeNet-5网络仿真实现](https://www.madao33.com/post/8/)

## AlexNet

* paper: [Krizhevsky A , Sutskever I , Hinton G . ImageNet Classification with Deep Convolutional Neural Networks[J]. Advances in neural information processing systems, 2012, 25(2).](https://proceedings.neurips.cc/paper/2012/file/c399862d3b9d6b76c8436e924a68c45b-Paper.pdf)

* codes: [Sowndharya206/alexnet](https://github.com/Sowndharya206/alexnet)

* 个人笔记：
* [AlexNet网络学习及仿真](https://www.madao33.com/post/10/)
* [AlexNet基于MNIST数据集的代码实现](https://www.madao33.com/post/12/)

## GoogleNet

* paper: [Szegedy C ,  Liu W ,  Jia Y , et al. Going Deeper with Convolutions[J]. IEEE Computer Society, 2014.](https://arxiv.org/pdf/1409.4842.pdf)
* codes: [hawrot/image-classification-pytorch](https://github.com/hawrot/image-classification-pytorch)
* 个人笔记: [GoogLeNet学习笔记](https://www.madao33.com/post/13/)
> GoogLeNet 因为目前没有GPU，训练没有完成
  

## VGGNet

* paper: [Simonyan K ,  Zisserman A . Very Deep Convolutional Networks for Large-Scale Image Recognition[J]. Computer Science, 2014.](https://arxiv.org/pdf/1409.1556.pdf)
* codes: [jays0606/VGGNet](https://github.com/jays0606/VGGNet)
* 个人笔记: [VGGNet学习笔记及仿真](https://www.madao33.com/post/14/)

## ResNet

* paper: [He K ,  Zhang X ,  Ren S , et al. Deep Residual Learning for Image Recognition[J]. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.](https://arxiv.org/pdf/1512.03385.pdf)
* codes: [Bingmang/pytorch-cifar10-notebook](https://github.com/Bingmang/pytorch-cifar10-notebook)
* 个人笔记：[ResNet学习笔记及仿真](https://www.madao33.com/post/15/)

## SeNet
* paper: [Hu J, Shen L, Sun G. Squeeze-and-excitation networks[J]. arXiv preprint arXiv:1709.01507, 2017, 7.](https://arxiv.org/pdf/1709.01507.pdf)
* codes: [JYPark09/SENet-Pytorch](https://github.com/JYPark09/SENet-PyTorch)
* 个人学习笔记：[SeNet学习笔记及仿真](https://www.madao33.com/post/16/)

## DenseNet
* paper: [Huang G ,  Liu Z ,  Laurens V , et al. Densely Connected Convolutional Networks[J]. IEEE Computer Society, 2016.](https://arxiv.org/pdf/1608.06993.pdf)
* codes: [gpleiss/efficient_densenet_pytorch](https://github.com/gpleiss/efficient_densenet_pytorch)
* 个人学习笔记：[DenseNet学习笔记及仿真](https://www.madao33.com/post/20/)