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

【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/cntdtlhdibd2021ceahb2021-5,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
卷积神经网络AlexNet VGG ResNet DenseNet ShuffleNet MobileNet GhostNet EfficientNet RepVGG

8.BN,SE,

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