卷积神经网络AlexNet VGG ResNet DenseNet ShuffleNet MobileNet GhostNet EfficientNet RepVGG

卷积神经网络AlexNet VGG ResNet DenseNet ShuffleNet MobileNet GhostNet EfficientNet RepVGG

• • 1.ResNet
  • 2.DenseNet
  • 3.ShuffleNet
  • 4.MobileNet
  • 5.GhostNet
  • 6.EfficientNet
  • 7.RepVGG
  • 8.BN,SE,

【图像去噪 paper 系列 (1) (2)】
【文档图像二值化数据集 databases】
【文档图像二值化 paper 系列 -1- | 系列 -2-】

找paper搭配 Sci-Hub 食用更佳 (๑•̀ㅂ•́)و✧
Sci-Hub 实时更新 : https://tool.yovisun.com/scihub/
公益科研通文献求助：https://www.ablesci.com/
硕士期间我的公开数据集paperswithcode.com/dataset/cntd、tlhdibd2021、ceahb2021-5，包含街景文本检测、文档图像识别、文档图像二值化

卷积神经网络list paper with code

1.ResNet

2.DenseNet

3.ShuffleNet

4.MobileNet

5.GhostNet

6.EfficientNet

7.RepVGG

Advantages:
The model has a VGG-like plain (a.k.a. feed-forward) topology 1 without any branches. I.e., every layer takes the output of its only preceding layer as input and feeds the output into its only following layer.

The model’s body uses only 3 × 3 conv and ReLU.

The concrete architecture (including the specific depth and layer widths) is instantiated with no automatic search, manual refinement, compound scaling, nor other heavy designs.

Reference ：图解RepVGG

8.BN,SE,

标签：RepVGG,layer,MobileNet,VGG,GhostNet,only,paper,文档
来源： https://blog.csdn.net/qq_35200351/article/details/122196789