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,