图像分类(CLAS)

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