-
BP:backprop_old 论文笔记
-
ZFNet:Visualizing and Understanding Convolutional Networks 中文版 论文笔记 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
-
DenseNet: Densely Connected Convolutional Networks 论文笔记
-
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks 论文笔记
- 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
-
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
-
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
-
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: 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: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
- 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