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