# 图像分类(CLAS)

| Num | Title                                                                            | Field | Desc                  | Author | Time | read |
| --- | -------------------------------------------------------------------------------- | ----- | --------------------- | ------ | ---- | ---- |
| 1   | Gradient-based Learning Applied to Document Recognition                          |       | LeNet                 |        |      |      |
| 2   | ImageNet Classification with Deep Convolutional                                  |       | AlexNet               |        |      |      |
| 3   | Visualizing and Understanding Convolutional Networks                             |       | ZFNet                 |        |      |      |
| 4   | VERY DEEP CONVOLUTIONAL                                                          |       | VGG                   |        |      |      |
| 5   | Going deeper with convolutions                                                   |       | GoogleNet,Inceptionv1 |        |      |      |
| 6   | Batch Normalization-Accelerating Deep Network Training b                         |       |                       |        |      |      |
| 7   | Rethinking the Inception Architecture for Computer Vision                        |       | Inceptionv3           |        |      |      |
| 8   | Inception-v4：Inception-ResNet and the Impact of Residual Connections on Learning |       | Inception-v4          |        |      |      |
| 9   | Xception：Deep Learning with Depthwise Separable Convolutions                     |       | Xception              |        |      |      |
| 10  | Deep Residual Learning for Image Recognition                                     |       | ResNet                |        |      |      |
| 11  | Aggregated Residual Transformations for Deep Neural Networks                     |       | ResNeXt               |        |      |      |
| 12  | Densely Connected Convolutional Networks                                         |       | DenseNet              |        |      |      |
| 13  | Learning Transferable Architectures for Scalable Image Recognition               |       | NASNet-A              |        |      |      |
| 14  | MobileNets-Efficient Convolutional Neural Networks for Mobile Vision             |       | SENet                 |        |      |      |
| 15  | MobileNets- Efficient Convolutional Neural Networks for Mobile Vision            |       | MobileNets-v1         |        |      |      |
| 16  | MobileNetV2：Inverted Residuals and Linear Bottlenecks                            |       | MobileNets-v2         |        |      |      |
| 17  | Searching for MobileNetV3                                                        |       | MobileNets-v3         |        |      |      |
| 18  | ShuffleNet：An Extremely Efficient Convolutional Neural Network for Mobile        |       | ShuffleNet            |        |      |      |
| 19  | ShuffleNet V2：Practical Guidelines for Efficient                                 |       | ShuffleNet-v2         |        |      |      |
| 20  | Bag of Tricks for Image Classification with Convolutional Neural Networks        |       |                       |        |      |      |
| 21  | EfficientNet：Rethinking Model Scaling for Convolutional Neural Networks          |       | EfficientNet          |        |      |      |
| 22  | EfficientNetV2：Smaller Models and Faster Training                                |       | EfficientNet-v2       |        |      |      |
| 23  | CSPNET-A NEW BACKBONE THAT CAN ENHANCE LEARNING                                  |       | CSPNET-A              |        |      |      |
| 24  | High-Performance Large-Scale Image Recognition Without Normalization             |       | NFNets                |        |      |      |
| 25  | AN IMAGE IS WORTH 16X16 WORDS-T RANSFORMERS FOR I MAGE R ECOGNITION AT S CALE    |       | Vision Transformer    |        |      |      |
| 26  | Training data-efficient image transformers                                       |       | DeiT                  |        |      |      |
| 27  | Swin Transformer-Hierarchical Vision Transformer using Shifted Windows           |       | Swin Transformer      |        |      |      |
|     |                                                                                  |       |                       |        |      |      |
