Deep-Learing-classification-Pytorch

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Deep-Learing-classification-Pytorch

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# 文件说明

### 复现的网络大多包含以下几个文件
* model.py：模型文件
* train.py： 调用模型训练的文件
* predict.py： 调用模型进行预测的文件
* class_indices.json： 训练数据集对应的标签文件

# 有关图像分类的文章链接

### 图像分类(Classification)-Papers
* LeNet [[paper](http://lushuangning.oss-cn-beijing.aliyuncs.com/CNN%E5%AD%A6%E4%B9%A0%E7%B3%BB%E5%88%97/Gradient-Based_Learning_Applied_to_Document_Recognition.pdf)]
* AlexNet [[paper](http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf)]
* ZFNet [[paper](https://arxiv.org/abs/1311.2901)]
* VGG [[paper](https://arxiv.org/abs/1409.1556)]
* GoogLeNet, Inceptionv1 [[paper](https://arxiv.org/abs/1409.4842)]
* Batch Normalization [[paper](https://arxiv.org/abs/1502.03167)]
* Inceptionv3 [[paper](https://arxiv.org/abs/1512.00567)]
* Inceptionv4, Inception-ResNet [[paper](https://arxiv.org/abs/1602.07261)]
* Xception [[paper](https://arxiv.org/abs/1610.02357)]
* ResNet [[paper](https://arxiv.org/abs/1512.03385)]
* ResNeXt [[paper](https://arxiv.org/abs/1611.05431)]
* DenseNet [[paper](https://arxiv.org/abs/1608.06993)]
* NASNet-A [[paper](https://arxiv.org/abs/1707.07012)]
* SENet [[paper](https://arxiv.org/abs/1709.01507)]
* MobileNet(v1) [[paper](https://arxiv.org/abs/1704.04861)]
* MobileNet(v2) [[paper](https://arxiv.org/abs/1801.04381)]
* MobileNet(v3) [[paper](https://arxiv.org/abs/1905.02244)]
* ShuffleNet(v1) [[paper](https://arxiv.org/abs/1707.01083)]
* ShuffleNet(v2) [[paper](https://arxiv.org/abs/1807.11164)]
* EfficientNet(v1) [[paper](https://arxiv.org/abs/1905.11946)]
* EfficientNet(v2) [[paper](https://arxiv.org/abs/2104.00298)]
* CSPNet [[paper](https://arxiv.org/abs/1911.11929)]
* RegNet [[paper](https://arxiv.org/abs/2003.13678)]
* NFNets [[paper](https://arxiv.org/abs/2102.06171)]
* Vision Transformer [[paper](https://arxiv.org/abs/2010.11929)]
* DeiT [[paper](https://arxiv.org/abs/2012.12877)]
* Swin Transformer [[paper](https://arxiv.org/abs/2103.14030)]
* Swin Transformer V2: Scaling Up Capacity and Resolution [[paper](https://arxiv.org/abs/2111.09883)]
* BEiT: BERT Pre-Training of Image Transformers [[paper](https://arxiv.org/abs/2106.08254)]
* MAE [[paper](https://arxiv.org/abs/2111.06377)]
* ConvNeXt [[paper](https://arxiv.org/abs/2201.03545)]

Deep-Learing-classification-Pytorch

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