swq-1993 / DeepLearning

A deep learning code base, mainly for paper replication, in the areas of image recognition, object detection, image segmentation, self-supervision, etc. Each project can be run independently, and there are corresponding articles to explain.

DeepLearning

介绍

个人学习项目，用于复现论文代码，深入理解算法原理。

注意：每个项目都可独立运行。若要运行某个项目，你需要将该项目作为根目录，以便找到对应模块。

每个项目都有对应论文解读，解读详情搜 [知乎] - 琪小钧

• paper read: 知乎 论文解读（有些博主对于某些论文已经作了很深刻的理解，因此有些算法直接引用了他们的知乎文章。如有处理不当的地方，请联系我。）
• code:对应项目代码

classification

• mnist paper read / code
• vggNet paper read / code
• resnet paper read / code
• resNeXt paper read / code
• resNeSt paper read / code
• coatNet paper read / code
• convNext paper read / code
• seNet paper read / code
• skNet paper read / code
• TransFG paper read / code
• RepVGG paper read / code
• efficientNet paper read / code
• shuffleNet paper read / code
• vision_transformer paper read / code
• swin-transformer paper read / code
• googleNet paper read / code
• inception
• denseNet
• CBAM
• mobileNet
• Xception
• SqueezeNet

detection

• FPN(实现resnet50 + fpn) paper read / code
• Faster-rcnn paper read / code
• yolov7 paper read / code
• RetinaNet (包含focal_loss) code
• YOLOV5 V5.0 (实现注释，更新pt->onnx代码) code
• yolox (修改了voc数据读取方式) code
• FCOS code
• yoloF
• yoloR
• detr
• ssd
• Mask-rcnn
• Cascade-rcnn
• SPPNet
• CenterNet
• RepPoints
• OTA
• ATSS

segmentation

• U-Net paper read / code
• HR-Net-Seg paper read / code
• DeepLabv3 paper read / code
• DeepLabv3Plus paper read / code
• few_shot segmentation code
• FCN code
• Mask-rcnn
• Cascade-rcnn
• UNet++
• PSPNet
• Segmenter

metric_learning

• BDB (用于图像检索) code
• Happy-Whale (鲸鱼竞赛检索baseline) code

self-supervised

• MAE (实现VIT+MAE) paper read / code
• SupCon (实现自对比学习+t-SNE可视化+swa) paper read / code
• MoCo
• SwAV

deep_stereo

• Real_time_self_adaptive_deep_stereo (实时双目里立体匹配，细节待完善) code

other

• label_convert (三种不同标注文件之间的转换以及box可视化) paper read / code
• normalization (BN、LN、IN、GN、SN图解) paper read / code
• DDP (模型分布式计算) paper read / code
• tensorboard test (可视化网络，图片，训练过程以及卷积核) / code
• load weights test (权重部分加载) / code
• visual weights map test (特征图、卷积核可视化分析) / code
• class_Activation_Map_Visual (可视化CNN的类激活图) / code
• deploy (pytorch模型转onnx，支持自定义算子 示例) / code