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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.

License: MIT License

Shell 0.17% C++ 3.48% Python 94.51% Java 0.23% Cuda 0.25% Makefile 0.02% CMake 0.05% Jupyter Notebook 1.23% Dockerfile 0.07%

deeplearning's Introduction

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

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

metric_learning

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

self-supervised

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

deeplearning's People

Contributors

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