计算机视觉

image classification

• BP:backprop_old 论文笔记
• ZFNet：Visualizing and Understanding Convolutional Networks 中文版 论文笔记 pytorch tensorflow
• VGG：Very Deep Convolutional Networks for Large-Scale Image Recognition 论文笔记
• GoogleNet :Going Deeper with Convolutions 中文版 论文笔记 pytorch tensorflow
• NiN ： Network In Network 笔记
• ResNet：Deep Residual Learning for Image Recognition 中文版 论文笔记 pytorch tensorflow
• ResNeXt：Aggregated Residual Transformations for Deep Neural Networks
• InceptionV3：Rethinking the Inception Architecture for Computer Vision 中文版 论文笔记 pytorch tensorflow
• InceptionV4：Inception-ResNet and the Impact of Residual Connections on Learning 中文版 论文笔记 pytorch
• MnasNet: Platform-Aware Neural Architecture Search for Mobile 论文笔记
• SeNet:Squeeze-and-Excitation Networks
• DenseNet： Densely Connected Convolutional Networks 论文笔记
• EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks 论文笔记

Object Detection

• R-CNN:Rich feature hierarchies for accurate object detection and semantic segmentation 中文版 论文笔记
• Fast-R-CNN: Fast R-CNN - The Computer Vision Foundation 中文版 论文笔记 pytorch tensorflow
• Faster R-CNN:Towards Real-Time Object Detection with Region Proposal Networks 中文版 论文笔记 pytorch
• Cascade R-CNN:Cascade R-CNN: Delving into High Quality Object Detection 论文笔记
• YOLOv1：You Only Look Once: Unified, Real-Time Object Detection 论文笔记
• YOLOv2: YOLO9000: Better, Faster, Stronger 论文笔记
• YOLOv3：An Incremental Improvement 论文笔记
• YOLOv4:Optimal Speed and Accuracy of Object Detection 中文翻译 论文笔记
• YOLOv5: 笔记
• PPYOLOE：PP-YOLOE: An evolved version of YOLO 论文笔记
• RT-DETR:DETRs Beat YOLOs on Real-time Object Detection 论文笔记
• FPN：Feature Pyramid Networks for Object Detection 中文翻译 论文笔记 pytorch tensorflow
• RetinaNet：Focal Loss for Dense Object Detection 论文笔记
• FCOS: Fully Convolutional One-Stage Object Detection 论文笔记
• Mask rcnn:Mask R-CNN 论文笔记
• M2Det: A Single-Shot Object Detector based on Multi-Level Feature Pyramid Network 论文笔记
• EfficientDet: Scalable and Efficient Object Detection 论文笔记
• Cascade RCNN-RS：Simple Training Strategies and Model Scaling for Object Detection 论文笔记

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Semantic segmentation

• FCN：Fully Convolutional Networks for Semantic Segmentation 中文版 论文笔记 pytorch tensorflow

• DeconvNet：Learning Deconvolution Network for Semantic Segmentation 论文笔记 pytorch tensorflow

• U-Net：Convolutional Networks for Biomedical Image Segmentation 中文版 论文笔记 pytorch tensorflow

• SegNet：A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation 论文笔记 pytorch tensorflow

• ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation 论文笔记 pytorch tensorflow

• FusionNet：A deep fully residual convolutional neural network for image segmentation in connectomics 论文笔记 pytorch tensorflow

• DeepLabv1:Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs 论文笔记

• DeepLabv2： Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs 论文笔记

• DeepLabv3： Rethinking Atrous Convolution for Semantic Image Segmentation 论文笔记

• DeepLabv3 plus：Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation 论文笔记

• GCN: Large Kernel Matters ——Improve Semantic Segmentation by Global Convolutional Network 论文笔记

• ExFuse：Enhancing Feature Fusion for Semantic Segmentation

• DFN:Learning a Discriminative Feature Network for Semantic Segmentation

• BiSeNetv1：BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation 论文笔记

• BiSeNet V2：Bilateral Network with Guided Aggregation for Real-time Semantic Segmentation 论文笔记

• RDFNet：RGB-D Multi-level Residual Feature Fusion for Indoor Semantic Segmentation

• RedNet: Residual Encoder-Decoder Network for indoor RGB-D Semantic Segmentation 论文笔记

• DFANet: Deep Feature Aggregation for Real-Time Semantic Segmentation 论文笔记

Light_weight_Network

• MobileNetv1： MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications 论文笔记 pytorch

• MobileNetV2：Inverted Residuals and Linear Bottlenecks 论文笔记 代码复现

• MobileNetV3：Howard et al_2019_Searching for MobileNetV3 论文笔记 pytorch tensorflow

• ShuffleNetv1: An Extremely Efficient Convolutional Neural Network for Mobile 论文笔记 pytorch tensorflow

• ShuffleNet V2:Practical Guidelines for Efficient CNN Architecture Design 论文笔记

• Xception: Deep Learning with Depthwise Separable Convolutions 论文笔记 pytorch

Transformer

• Transformer: Attention Is All You Need
• ViT：AN IMAGE IS WORTH 16X16 WORDS: TRANSFORMERS FOR IMAGE RECOGNITION AT SCALE 中文版 论文笔记 pytorch
• T2T: Training Vision Transformers from Scratch on ImageNet 论文笔记 pytorch
• BotNet: Bottleneck Transformers for Visual Recognition 论文笔记 pytorch
• TnT : Transformer in Transformer 论文笔记 pytorch
• MAE : Masked Autoencoders Are Scalable Vision Learners 论文笔记 pytorch
• PVT: A Versatile Backbone for Dense Prediction without Convolutions 论文笔记 pytorch
• swin-Transfromer:Hierarchical Vision Transformer using Shifted Windows 论文笔记 pytorch
• Deit:Training data-efficient image transformers & distillation through attention 论文笔记pytorch

GAN

• GAN：Generative Adversarial Networks 论文笔记 pytorch
• pix2pix: Image-to-Image Translation with Conditional Adversarial Networks 论文笔记
• CycleGAN: Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks 论文笔记

Graph

• node2vec: Scalable Feature Learning for Networks 论文笔记 pytorch
• LINE: Large-scale Information Network Embedding 论文笔记 pytorch
• SDNE: Structural Deep Network Embedding 论文笔记 pytorch
• metapath2vec: Scalable Representation Learning for Heterogeneous Networks 论文笔记 pytorch
• Graph neural networks: A review of methods and applications 论文笔记
• A Survey on Graph Diffusion Models: Generative AI in Science for Molecule, Protein and Material 论文笔记

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