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/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