学习过程中的重要知识链接汇总

本是古典何须时尚 2023-06-07 08:29 12阅读 0赞

### 文章目录 ###

*  2019.9-2020.5
 *  论文
 *  python的高级使用
 *  2020.6-2020.11
 *  2020.11- 2021.7
 *  2021.8 -

# 2019.9-2020.5 #

1.  [windows下使用labelImg标注图像][windows_labelImg]
2.  [启动进程报错 RuntimeError: An attempt has been made to start a new process before the current process 使用pytorch读取数据集时，多进程报错][RuntimeError_ An attempt has been made to start a new process before the current process _pytorch]
3.  [pytorch版yolov3训练自己数据集][pytorch_yolov3]  
    [pytorch版yolov3训练自己数据集-董][pytorch_yolov3_-]  
    [yolo3 配置文件yolo3\_voc.cfg参数学习-better][yolo3 _yolo3_voc.cfg_-better]  
    [yolo3 配置文件yolo3\_voc.cfg参数学习][yolo3 _yolo3_voc.cfg]  
    [深度学习：学习率learning rate 的设定规律][learning rate]  
    董博客：  
    [yolov1][]  
    [yolov2][]  
    [yolov3][]  
    [yolov3 github][]
4.  [ModuleNotFoundError: No module named 'tkinter’解决办法][ModuleNotFoundError_ No module named _tkinter]
5.  [【线性代数】通俗的理解奇异值以及与特征值的区别，还有奇异值分解及其应用][Link 1]  
    [增广拉格朗日函数法（ALM）][ALM]  
    [交替方向乘子法（ADMM）的原理和流程的白话总结][ADMM]  
    [线性规划求解——增广拉格朗日函数法][Link 2]
6.  [目标检测之素材标注][Link 3]
7.  [BrokenPipeError: Errno 32 - pytorch多线程错误解决办法，修改torch.utils.data.DataLoader()里面的num\_workers为0][BrokenPipeError_ Errno 32 - pytorch_torch.utils.data.DataLoader_num_workers_0]
8.  [box plot & volin plot & bean plot][box plot _ volin plot _ bean plot]
9.  [yolov1通俗理解-写的很好，我由此入门 ][yolov1_-_] ←[IoU][]  
    [yolov2通俗理解-写的很好，我由此深入][yolov2_-]  
    [Batch Normalization][]  
    [yolov3深入理解][yolov3 1]
10. [hadoop版本库][hadoop]  
    [hadoop镜像库][hadoop 1]  
    [hadoop伪集群简书教程2.7.1][hadoop_2.7.1]  
    [hadoop伪集群cdsn教程-3.0.2][hadoop_cdsn_-3.0.2]  
    [hadoop踩坑经验][hadoop 2]  
    [hadoop的windows专用二进制文件和工具类依赖库][hadoop_windows]
11. [tex live的下载][tex live]  
    [tex live 的安装][tex live 1]
12. [texstudio配置中文][texstudio]
13. [make\_blobs聚类数据生成器][make_blobs]
14. 手写聚类函数：  
    [k均值算法和python实现][k_python]  
    [学习向量量化（LVQ）和python实现][LVQ_python]  
    [高斯混合聚类和python实现][python]
15. [np.linalg.norm(求范数)][np.linalg.norm]
16. [python中copy()和deepcopy()详解][python_copy_deepcopy]
17. [模式识别核函数][Link 4]
18. [西农学校代理][Link 5]
19. [为 Jupyter 添加目录][Jupyter]
20. [w3school前端文档][w3school]
21. [bootstrap全局css样式][bootstrap_css]
22. [【steam】用ASF挂卡（懒人）][steam_ASF]
23. [在word中插入代码块][word]
24. [γ code及其实现][code]  
    [γ 编码的编码和解码过程][Link 6]
25. 牛脸识别相关：  
    [行人重识别(ReID) ——数据集描述 Market-1501][ReID_ _ Market-1501]  
    [【论文理解】ArcFace: Additive Angular Margin Loss for Deep Face Recognition（InsightFace）][ArcFace_ Additive Angular Margin Loss for Deep Face Recognition_InsightFace]  
    [layumi/Person-reID-triplet-loss][layumi_Person-reID-triplet-loss]  
    [deepinsight/insightface][deepinsight_insightface]  
    [TreB1eN/InsightFace\_Pytorch][TreB1eN_InsightFace_Pytorch]  
    [ronghuaiyang/arcface-pytorch][ronghuaiyang_arcface-pytorch]
26. [Win10上安装CUDA和CUDNN][Win10_CUDA_CUDNN]
27. [python文件封装成\*.exe文件（单文件和多文件）][python_.exe]  
    [Pyinstaller打包多个.py文件][Pyinstaller_.py]  
    [python制作ico图标][python_ico]  
    [Pyinstaller打包时出现：UnicodeDecodeError: ‘utf-8’ codec can’t decode byte 0xce…][Pyinstaller_UnicodeDecodeError_ _utf-8_ codec can_t decode byte 0xce]  
    [pyinstaller 打包出现failed to execute script pyi\_rth\_pkgres][pyinstaller _failed to execute script pyi_rth_pkgres]  
    [pyinstaller机器学习含sklearn打包遇到的各种问题][pyinstaller_sklearn]
28. [关于python的pandas获取csv\\Excel的第一行（列标签）][python_pandas_csv_Excel]
29. [深度学习-目标检测评估指标P-R曲线、AP、mAP][-_P-R_AP_mAP]
30. pytorch中使用tensorboard  
    [PyTorch中使用TensorboardX][PyTorch_TensorboardX]  
    [详解PyTorch项目使用TensorboardX进行训练可视化][PyTorch_TensorboardX 1]
31. git安装  
    [Git安装教程（详细）  
    ][Git_]
32. [解决Pycharm大量占用C盘问题][Pycharm_C]  
    [转移C盘中Pycharm缓存文件][C_Pycharm]
33. [Sublime Text如何实现html文件代码格式化][Sublime Text_html]
34. [COCO数据集使用][COCO]
35. [图像数据标注工具labelme使用教程][labelme]
36. [parse\_args（argsparse）：python和命令行之间的交互][parse_args_argsparse_python]
37. [python中eval和ast.literal\_eval的区别][python_eval_ast.literal_eval]
38. [python—OpenCV2中 cv2.VideoCapture()，read()，waitKey()的使用][python_OpenCV2_ cv2.VideoCapture_read_waitKey]
39. [windows安装pycocotools][windows_pycocotools]
40. hrnet  
    [Linux下基于Pytorch框架用HRNet提取骨架特征点+解决CUDA和Torch版本不匹配问题][Linux_Pytorch_HRNet_CUDA_Torch]  
    [配置训练HRNet的环境][HRNet]  
    [HRNet提取骨架特征点+ ST-GCN训练自己的数据集代码实践问题记录][HRNet_ ST-GCN]  
    [HRNet使用中遇到的问题记录][HRNet 1]

# 论文 #

--------------------

论文阅读

1.  [CNN中卷积核、feature map以及可训练的参数个数的关系][CNN_feature map]
2.  [LeNet论文阅读：CNN设计原理][LeNet_CNN] / [LeNet详解][LeNet]
3.  [密集卷积网络(Densenet)论文阅读笔记][Densenet]
4.  [resnet论文阅读][resnet]
5.  [AlexNet论文阅读总结及代码][AlexNet]
6.  [GoogleNet论文解读][GoogleNet] / [大话CNN经典模型：GoogLeNet（从Inception v1到v4的演进）][CNN_GoogLeNet_Inception v1_v4] / [经典神经网络论文阅读笔记（四）（GoogLeNet）][GoogLeNet] / [googlenet论文解读][googlenet]
7.  [自动搜索数据增强方法分享——fast-autoaugment][fast-autoaugment]
8.  [从ReLU到GELU，一文概览神经网络的激活函数][ReLU_GELU]
9.  [HOW TO TRAIN YOUR MAML][]
10. [大话CNN经典模型：VGGNet][CNN_VGGNet] \- 这一系列博客写的都非常好
11. [大话深度残差网络（DRN）ResNet网络原理][DRN_ResNet]
12. [深度学习网络篇——DenseNet][DenseNet] / [【文献阅读】Densely Connected Convolutional Networks-best paper-CVPR-2017][Densely Connected Convolutional Networks-best paper-CVPR-2017]
13. [27种深度学习主流神经网络][27]
14. [Sublime Text 3 配置python交互运行环境的快捷键][Sublime Text 3 _python]
15. [two-stage与one-stage目标检测算法概览][two-stage_one-stage]
16. [大话目标检测经典模型：Mark R-CNN][Mark R-CNN]
17. [R-CNN文章详细解读][R-CNN]
18. [Fast RCNN算法详解][Fast RCNN] / [目标检测之选择性搜索-Selective Search][-Selective Search] / [基于图的图像分割（Graph-Based Image Segmentation）][Graph-Based Image Segmentation] / [Fast R-CNN论文详解（Fast R-CNN）][Fast R-CNN_Fast R-CNN]
19. [一文读懂Faster RCNN][Faster RCNN] \- 讲的很好 / [Faster RCNN详解][Faster RCNN 1] / [【论文笔记】Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks][Faster R-CNN_ Towards Real-Time Object Detection with Region Proposal Networks]
20. [R-CNN --> FAST-RCNN --> FASTER-RCNN][R-CNN --_ FAST-RCNN --_ FASTER-RCNN]
21. [神经网络: SSD目标检测网络解析][SSD] / [SSD：Single Shot MultiBox Detector解读][SSD_Single Shot MultiBox Detector] / [SSD神经网络学习——训练自己的目标检测模型][SSD 1]
22. [FPN详解][FPN] / [【论文笔记】FPN —— 特征金字塔][FPN _] / [FPN全解-最全最详细][FPN_-]
23. [从LeNet到EfficientNet：CNN的演变和特点总结][LeNet_EfficientNet_CNN]
24. [（RegionProposal Network)RPN网络结构及详解][RegionProposal Network_RPN]
25. [EfficientNet-可能是迄今为止最好的CNN网络][EfficientNet-_CNN] / [速度与精度的结合 - EfficientNet 详解][- EfficientNet]
26. [感受野][Link 7]
27. [\[深度学习论文笔记\]\[CVPR 18\]Path Aggregation Network for Instance Segmentation][CVPR 18_Path Aggregation Network for Instance Segmentation]
28. [EfficientNet和EfficientDet总结][EfficientNet_EfficientDet]  
    [道路场景语义分割算法][Link 8]
29. [python opencv cv2.rectangle 参数含义][python opencv cv2.rectangle] \- 可以画矩形框和旋转的矩形框
30. [Mask R-CNN翻译][Mask R-CNN]

--------------------

论文书写英文引用论文

1.  [SIMPLE ONLINE AND REALTIME TRACKING （SORT）论文阅读笔记][SIMPLE ONLINE AND REALTIME TRACKING _SORT]
2.  [SST: Single-Stream Temporal Action Proposals][SST_ Single-Stream Temporal Action Proposals]
3.  yolov4 / [YOLOv4-MS COCO数据集上最准确的实时神经网络。][YOLOv4-MS COCO] / [深入浅出Yolo系列之Yolov3&Yolov4&Yolov5核心基础知识完整讲解][Yolo_Yolov3_Yolov4_Yolov5]
4.  [论文：GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond 阅读笔记][GCNet_ Non-local Networks Meet Squeeze-Excitation Networks and Beyond]
5.  [行人检索–Beyond triplet loss: a deep quadruplet network for person re-identification][Beyond triplet loss_ a deep quadruplet network for person re-identification] \- 行人重识别
6.  [CenterNet( Keypoint Triplets for Object Detection) 学习笔记][CenterNet_ Keypoint Triplets for Object Detection_]
7.  [DSSD : Deconvolutional Single Shot Detector][DSSD _ Deconvolutional Single Shot Detector]
8.  [RCNN学习笔记(2):Rich feature hierarchies for accurate object detection and semantic segmentation][RCNN_2_Rich feature hierarchies for accurate object detection and semantic segmentation]
9.  [Fast R-CNN论文详解（Fast R-CNN）][Fast R-CNN_Fast R-CNN]
10. [Mask R-CNN详解][Mask R-CNN 1]
11. [SPP-Net (Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition)][SPP-Net _Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition]
12. [Deep residual learning for image recognition][]
13. [High-Speed Tracking with Kernelized Correlation Filters][]
14. [MobileNets：Efficient Convolutional Neural Networks for Mobile Vision Applications][MobileNets_Efficient Convolutional Neural Networks for Mobile Vision Applications]
15. [Squeeze-and-Excitation Networks（SENet）][Squeeze-and-Excitation Networks_SENet]
16. [DenseBox: Unifying Landmark Localization with End to End Object Detection（CVPR2015）][DenseBox_ Unifying Landmark Localization with End to End Object Detection_CVPR2015]
17. [Spatial Transformer Networks（STN）][Spatial Transformer Networks_STN]
18. [FoveaBox: Beyond Anchor-based Object Detector（CVPR2019）][FoveaBox_ Beyond Anchor-based Object Detector_CVPR2019]
19. [AlexNet论文(ImageNet Classification with Deep Convolutional Neural Networks)(译)][AlexNet_ImageNet Classification with Deep Convolutional Neural Networks]
20. [ECCV2018 | 论文阅读CornerNet: Detecting Objects as Paired Keypoints][ECCV2018 _ _CornerNet_ Detecting Objects as Paired Keypoints]
21. [Selective Kernel Networks(SKNet CVPR2019)][Selective Kernel Networks_SKNet CVPR2019]
22. [Scale-Aware Trident Networks for Object Detection][] \- 中科院大学论文
23. [FSSD: Feature Fusion Single Shot Multibox Detector][FSSD_ Feature Fusion Single Shot Multibox Detector]
24. [语义分割–RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentation][RefineNet_ Multi-Path Refinement Networks for High-Resolution Semantic Segmentation]
25. [Feature Pyramid Networks for Object Detection][]
26. [Focal Loss for Dense Object Detection论文和代码理解][Focal Loss for Dense Object Detection]
27. [SSD：Single Shot MultiBox Detector][SSD_Single Shot MultiBox Detector 1]
28. [MultiBox Detector解读][SSD_Single Shot MultiBox Detector]
29. [论文阅读：Understanding the Effective Receptive Field][Link 7] \- 感受野
30. [【论文笔记】MOT16 A Benchmark for Multi-Object Tracking数据集介绍][MOT16 A Benchmark for Multi-Object Tracking]
31. [神经网络 | ThunderNet: Towards Real-time Generic Object Detection（代码类）][_ ThunderNet_ Towards Real-time Generic Object Detection] — 牛牛牛，给了一个网络顺序图
32. [论文笔记：You Only Look Once: Unified, Real-Time Object Detection][You Only Look Once_ Unified_ Real-Time Object Detection] / [yolo（翻译）][yolo]
33. [YOLO9000: Better,Faster,Stronger（YOLO9000：更好，更快，更强）][YOLO9000_ Better_Faster_Stronger_YOLO9000]
34. [YOLOv3：An Incremental Improvement 全文翻译][YOLOv3_An Incremental Improvement]
35. [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks][Faster R-CNN_ Towards Real-Time Object Detection with Region Proposal Networks]
36. [MobileNetV2 ：Inverted Residuals and Linear Bottlenecks][MobileNetV2 _Inverted Residuals and Linear Bottlenecks]
37. [A real-time computer vision assessment and control of thermal comfort for group-housed pigs][] \- 家猪的研究，发布COMPUTERS AND ELECTRONICS IN AGRICULTURE上
38. [Deep Affinity Network for Multiple Object Tracking][]
39. [论文阅读笔记五十七:FCOS: Fully Convolutional One-Stage Object Detection(CVPR2019)][FCOS_ Fully Convolutional One-Stage Object Detection_CVPR2019]
40. [t-SNE完整笔记][t-SNE] / [无监督学习之t-SNE][t-SNE 1] \- t-SNE可视化实现
41. [【CV中的Attention机制】Non-Local neural networks的理解与实现][CV_Attention_Non-Local neural networks] \- 董佩杰博客，本文讲了注意力机制
42. [多目标跟踪：Simple Online and Realtime Tracking with a Deep Association Metric 2017 (deep-sort)][Simple Online and Realtime Tracking with a Deep Association Metric 2017 _deep-sort]
43. [【论文复现】CBAM: Convolutional Block Attention Module][CBAM_ Convolutional Block Attention Module]
44. [【论文笔记】：RepPoints: Point Set Representation for Object Detection][RepPoints_ Point Set Representation for Object Detection]
45. [分类数据增强论文“CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features”][CutMix_ Regularization Strategy to Train Strong Classifiers with Localizable Features]
46. [Wide Residual Networks][]
47. [人脸检测：《S3FD: Single Shot Scale-invariant Face Detector》论文详解][S3FD_ Single Shot Scale-invariant Face Detector]
48. [论文阅读-FairMOT:《A Simple Baseline for Multi-Object Tracking》][-FairMOT_A Simple Baseline for Multi-Object Tracking]
49. [论文精读——CenterNet :Objects as Points][CenterNet _Objects as Points]
50. [ExtremeNet（Bottom-up Object Detection by Grouping Extreme and Center Points）][ExtremeNet_Bottom-up Object Detection by Grouping Extreme and Center Points]
51. [Object Detection in 20 Years: A Survey][Object Detection in 20 Years_ A Survey] \- 20年间的目标检测  
    语义分割：
52. [MobileNet系列][MobileNet] / [神经网络学习小记录23——MobileNet模型的复现详解][23_MobileNet]
53. [Unet 论文解读 代码解读][Unet _] / [憨批的语义分割3——unet模型详解以及训练自己的unet模型（划分斑马线）][3_unet_unet]
54. [SegNet论文笔记][SegNet] / [憨批的语义分割1——基于Mobile模型的segnet讲解][1_Mobile_segnet] / [憨批的语义分割2——训练自己的segnet模型（划分斑马线）][2_segnet] / [憨批的语义分割7——基于resnet模型的segnet讲解（划分斑马线）][7_resnet_segnet]
55. [PSP Net 论文笔记][PSP Net] / [憨批的语义分割4——pspnet模型详解以及训练自己的pspnet模型（划分斑马线）][4_pspnet_pspnet]
56. [DeepLab系列][DeepLab] / 憨批的语义分割5——DeeplabV3+模型解析以及训练自己的DeeplabV3+模型（划分斑马线）
57. [论文笔记之CSPNet][CSPNet]
58. [(CVPR2019)视频图像语义分割(21)联合传播数据增广+标签松弛提升边界精度=语义分割效果提升][CVPR2019_21]

--------------------

# python的高级使用 #

1.  [python常用魔法函数][python 1]
2.  [Python内置函数vars()使用方法][Python_vars]
3.  [Python \_\_dict\_\_属性详解][Python _dict]
4.  [Python 函数装饰器][Python]

--------------------

# 2020.6-2020.11 #

1.  [手动修改cpu\_nms中的setup\_linux.py为windows版本][cpu_nms_setup_linux.py_windows]  
    [python-cpu-nms实现][python-cpu-nms]
2.  selenium爬虫回顾  
    [python爬虫之xpath的基本使用][python_xpath]  
    [基于python的selenium常用操作方法][python_selenium]  
    [Selenium下载页面上的图片][Selenium]  
    [Python爬虫教程-12-爬虫使用cookie（上）][Python_-12-_cookie]  
    [XPath 语法][XPath]  
    [Python Selenium库的使用][Python Selenium]
3.  [cv保存视频][cv]  
    [Python 将图片转视频 avi 格式 cv2.VideoWriter cv2.VideoWriter\_fourcc][Python _ avi _ cv2.VideoWriter cv2.VideoWriter_fourcc]
4.  [从目标检测到小目标检测][Link 9]
5.  [PyTorch项目使用TensorboardX进行训练可视化][PyTorch_TensorboardX 1]
6.  [pytorch使用tensorboardX可视化训练情况的方式(以及远程可视化方式)][pytorch_tensorboardX]
7.  双目摄像头  
    [ubuntu 18.04 Linux 安装OpenCV 教程][ubuntu 18.04 Linux _OpenCV]  
    [UBUNTU16.04安装+ROS KINETIC安装+INDEMIND双目惯导摄像头ORB-SLAM2算法安装（小白详细教程操作+常见错误解决方法）][UBUNTU16.04_ROS KINETIC_INDEMIND_ORB-SLAM2]  
    [Ubuntu18.04.1安装ROS（‘E:无法定位软件包’）][Ubuntu18.04.1_ROS_E]  
    [sudo apt-get update出现 W: GPG 错误：http://packages.ros.org/ros/ubuntu xenial InRelease: 由于没有公钥，无法验证下列签名][sudo apt-get update_ W_ GPG _http_packages.ros.org_ros_ubuntu xenial InRelease_]  
    [ubuntu下ROS安装时sudo rosdep init和rosdep update的解决方法][ubuntu_ROS_sudo rosdep init_rosdep update] \-  
    新版的sdk：  
    [IMSEE-SDK][]  
    [SDK-api][]  
    [SDK demo测试视频][SDK demo]
8.  18.04.4 LTS 清华源

deb http://mirrors.tuna.tsinghua.edu.cn/ubuntu-ports/ bionic main multiverse restricted universe
    deb http://mirrors.tuna.tsinghua.edu.cn/ubuntu-ports/ bionic-security main multiverse restricted universe
    deb http://mirrors.tuna.tsinghua.edu.cn/ubuntu-ports/ bionic-updates main multiverse restricted universe
    deb http://mirrors.tuna.tsinghua.edu.cn/ubuntu-ports/ bionic-backports main multiverse restricted universe
    deb-src http://mirrors.tuna.tsinghua.edu.cn/ubuntu-ports/ bionic main multiverse restricted universe
    deb-src http://mirrors.tuna.tsinghua.edu.cn/ubuntu-ports/ bionic-security main multiverse restricted universe
    deb-src http://mirrors.tuna.tsinghua.edu.cn/ubuntu-ports/ bionic-updates main multiverse restricted universe
    deb-src http://mirrors.tuna.tsinghua.edu.cn/ubuntu-ports/ bionic-backports main multiverse restricted universe

1.  [小波变换][Link 10]
2.  [网络可视化工具netron详细安装流程][netron]
3.  [tensorflow和keras安装版本对照表][tensorflow_keras]
4.  [tensorflow 和 keras安装][tensorflow _ keras]
5.  labelme==3.16.7
6.  AI 还原画原来的外貌，并动起来唱歌  
    [Artbreeder][] \-基于：[stylegan2][] \---- 生成虚拟人像技术  
    [DAIN插帧算法][DAIN] \------ 视频插帧技术  
    [First Order Motion Model for Image Animation 论文解读][First Order Motion Model for Image Animation] / [和马斯克Zoom开个会，竟是AI换脸，GitHub 4000星项目登上热榜][Zoom_AI_GitHub 4000] / [详细教程][Link 11] \---- AI换脸技术
7.  [AttributeError: module ‘tensorflow’ has no attribute 'placeholder’等问题的解决][AttributeError_ module _tensorflow_ has no attribute _placeholder]
8.  [data agumentation 数据增强方法][data agumentation]
9.  [超全的电脑端Youtube视频下载教程（国内视频适用）][Youtube]
10. [网络可视化工具netron详细安装流程][netron]
11. [庖丁解牛yolo\_v4之CmBN][yolo_v4_CmBN]
12. [深度学习中的BN\_CBN\_CmBN][BN_CBN_CmBN]
13. [dlib的安装教程][dlib]
14. [OKS指标详解][OKS] \- 姿态检测的评估指标
15. [语义分割 图像增强（数据扩充）][Link 12]
16. sift航拍拼接  
    [Python-OpenCV：图像的全景拼接融合以及图片对齐处理][Python-OpenCV]  
    [SIFT算法之python实现][SIFT_python]  
    [图像特征检测方法—SIFT的Python实现][SIFT_Python]
17. pandas写入excel相关博客  
    [pandas DataFrame的创建方法][pandas DataFrame]  
    [python pandas写入excel文件][python pandas_excel]  
    [python – 向pandas添加行DataFrame更改dtype][python _ _pandas_DataFrame_dtype]
18. win10加python3.7安装tensorflow以及keras遇到的问题  
    [win10 python3.7 Anaconda3 安装tensorflow+Keras][win10 python3.7 Anaconda3 _tensorflow_Keras]  
    [keras和tensorflow对照表][tensorflow_keras]  
    [python3.7如何解决安装tensorflow的问题][python3.7_tensorflow]  
    [tensorboard 1.14.0 has requirement setuptools>=41.0.0, but you’ll have setuptools 39.0.1][tensorboard 1.14.0 has requirement setuptools_41.0.0_ but you_ll have setuptools 39.0.1]  
    [AttributeError: module ‘keras\_applications’ has no attribute ‘set\_keras\_submodules’][AttributeError_ module _keras_applications_ has no attribute _set_keras_submodules]  
    [Ubuntu系统在Anaconda中安装Python3.6的虚拟环境][Ubuntu_Anaconda_Python3.6]
19. [解决Tensorflow2.0出现：AttributeError: module ‘tensorflow’ has no attribute 'get\_default\_graph’的问题][Tensorflow2.0_AttributeError_ module _tensorflow_ has no attribute _get_default_graph] \- 调用keras的时候tensorflow 2.0出现的很多问题的解决方案。
20. [Linux vi/vim][Linux vi_vim]
21. [PyTorch对ResNet网络的实现解析][PyTorch_ResNet]

--------------------

1.  [gan的简单介绍][gan]
2.  [ProGAN：Step by step, better than better][ProGAN_Step by step_ better than better]
3.  [SAGAN：自注意力生成对抗网络][SAGAN]
4.  [深度解读DeepMind新作：史上最强GAN图像生成器—BigGAN ][DeepMind_GAN_BigGAN] /// [BigGAN CSDN][]
5.  [StyleGAN-基于样式的生成对抗网络（论文阅读总结）][StyleGAN-]

--------------------

# 2020.11- 2021.7 #

1.  [美区ios注册][ios]
2.  [六种常见的数据扩增方式（附代码）][Link 13]
3.  [segmentation语义分割数据增强方法及代码][segmentation]
4.  [怎样通俗易懂地解释反卷积？][Link 14]
5.  chrome网页变黑`chrome://flags/#enable-force-dark`
6.  蚁群算法  
    [蚁群算法原理及其实现(python)-csdn][python_-csdn]  
    [黑客蚁群优化，国外蚁群算法网站][Link 15]  
    [哥伦比亚大学讲解读博期间经历，包含了论文的选题、写作、slide的技巧、google doc做语法检查、overleaf做latex的在线编辑][slide_google doc_overleaf_latex]  
    [卷积码的维特比（Viterbi）译码][Viterbi]  
    [迁移学习，不同模型的讲解][Link 16]  
    [Keras复现CBAM注意力模块][Keras_CBAM]  
    [python保存列表][python 2]

--------------------

# 2021.8 - #

1.  [nn.init.trunc\_normal\_的实现方法][nn.init.trunc_normal]

import torch
    from scipy.stats import truncnorm
    import matplotlib.pyplot as plt
    
    def truncated_normal_(tensor, mean=0, std=1):
        size = tensor.shape
        tmp = tensor.new_empty(size + (4,)).normal_()
        valid = (tmp < 2) & (tmp > -2)
        ind = valid.max(-1, keepdim=True)[1]
        tensor.data.copy_(tmp.gather(-1, ind).squeeze(-1))
        tensor.data.mul_(std).add_(mean)
        return tensor
    
    
    
    fig, ax = plt.subplots(1, 1)
    
    
    def test_truncnorm():
        a, b = -2, 2
        size = 1000000
        r = truncnorm.rvs(a, b, size=size)
        ax.hist(r, density=True, histtype='stepfilled', alpha=0.2, bins=50)
    
        tensor = torch.zeros(size)
        utils.truncated_normal_(tensor)
        r = tensor.numpy()
    
        ax.hist(r, density=True, histtype='stepfilled', alpha=0.2, bins=50)
        ax.legend(loc='best', frameon=False)
        plt.show()
    
    
    if __name__ == '__main__':
        test_truncnorm()

1.  [pycharm 删除插件][pycharm]
2.  [Pycharm远程调试docker containers][Pycharm_docker containers]

[windows_labelImg]: https://blog.csdn.net/python_pycharm/article/details/85338801
[RuntimeError_ An attempt has been made to start a new process before the current process _pytorch]: https://blog.csdn.net/yaoyutian/article/details/85086129
[pytorch_yolov3]: https://blog.csdn.net/sinat_27634939/article/details/89884011
[pytorch_yolov3_-]: https://www.cnblogs.com/pprp/p/10863496.html
[yolo3 _yolo3_voc.cfg_-better]: https://blog.csdn.net/ll_master/article/details/81487844
[yolo3 _yolo3_voc.cfg]: https://pprp.github.io/2018/09/20/tricks.html
[learning rate]: https://blog.csdn.net/qq_33485434/article/details/80452941
[yolov1]: https://www.cnblogs.com/pprp/p/10124370.html
[yolov2]: https://www.cnblogs.com/pprp/p/10124591.html
[yolov3]: https://www.cnblogs.com/pprp/p/10124657.html
[yolov3 github]: https://github.com/ultralytics/yolov3
[ModuleNotFoundError_ No module named _tkinter]: http://www.pianshen.com/article/8476118338/
[Link 1]: https://blog.csdn.net/qq_32742009/article/details/82286434
[ALM]: https://blog.csdn.net/itnerd/article/details/86012869
[ADMM]: https://blog.csdn.net/qauchangqingwei/article/details/82112103
[Link 2]: https://blog.csdn.net/itnerd/article/details/86183469
[Link 3]: https://www.cnblogs.com/xiaozhi_5638/p/11288118.html
[BrokenPipeError_ Errno 32 - pytorch_torch.utils.data.DataLoader_num_workers_0]: https://blog.csdn.net/qq_33666011/article/details/81873217
[box plot _ volin plot _ bean plot]: http://www.timedoo.com/3527.html
[yolov1_-_]: https://zhuanlan.zhihu.com/p/46691043
[IoU]: https://blog.csdn.net/weixin_41278720/article/details/88770034
[yolov2_-]: https://zhuanlan.zhihu.com/p/47575929
[Batch Normalization]: https://zhuanlan.zhihu.com/p/34879333
[yolov3 1]: https://www.jianshu.com/p/d13ae1055302
[hadoop]: http://archive.apache.org/dist/hadoop/core/
[hadoop 1]: https://archive.apache.org/dist/hadoop/common/
[hadoop_2.7.1]: https://www.jianshu.com/p/1e7e9a70262d
[hadoop_cdsn_-3.0.2]: https://blog.csdn.net/weixin_43713105/article/details/93603900
[hadoop 2]: https://blog.csdn.net/Zhou_Dao/article/details/101602119
[hadoop_windows]: https://github.com/steveloughran/winutils
[tex live]: https://tug.org/texlive/
[tex live 1]: https://tug.org/texlive/quickinstall.html
[texstudio]: https://blog.csdn.net/Homewm/article/details/85061638
[make_blobs]: https://blog.csdn.net/sunshunli/article/details/82016851
[k_python]: https://blog.csdn.net/z962013489/article/details/82823419
[LVQ_python]: https://blog.csdn.net/z962013489/article/details/82823932
[python]: https://blog.csdn.net/z962013489/article/details/82824303
[np.linalg.norm]: https://blog.csdn.net/hqh131360239/article/details/79061535
[python_copy_deepcopy]: https://blog.csdn.net/u010712012/article/details/79754132
[Link 4]: https://www.cnblogs.com/lcchuguo/p/5173555.html
[Link 5]: https://nic.nwsuaf.edu.cn/wlfw/yhzn/32445.htm
[Jupyter]: https://www.cnblogs.com/shanger/p/12006482.html
[w3school]: https://www.w3school.com.cn/h.asp
[bootstrap_css]: https://v3.bootcss.com/css/#grid
[steam_ASF]: https://www.jianshu.com/p/c65980d6872e
[word]: http://www.planetb.ca/syntax-highlight-word
[code]: https://blog.csdn.net/phyeas/article/details/83726615
[Link 6]: https://blog.csdn.net/ahaha413525642/article/details/73497600
[ReID_ _ Market-1501]: https://blog.csdn.net/ctwy291314/article/details/83544088
[ArcFace_ Additive Angular Margin Loss for Deep Face Recognition_InsightFace]: https://blog.csdn.net/fuwenyan/article/details/79657738
[layumi_Person-reID-triplet-loss]: https://github.com/layumi/Person-reID-triplet-loss
[deepinsight_insightface]: https://github.com/deepinsight/insightface
[TreB1eN_InsightFace_Pytorch]: https://github.com/TreB1eN/InsightFace_Pytorch
[ronghuaiyang_arcface-pytorch]: https://github.com/ronghuaiyang/arcface-pytorch
[Win10_CUDA_CUDNN]: https://blog.csdn.net/james506/article/details/105901589
[python_.exe]: https://www.cnblogs.com/jackzz/p/9431923.html
[Pyinstaller_.py]: https://blog.csdn.net/CholenMine/article/details/80964272
[python_ico]: https://www.cnblogs.com/risunlee/p/ico.html
[Pyinstaller_UnicodeDecodeError_ _utf-8_ codec can_t decode byte 0xce]: https://blog.csdn.net/lch551218/article/details/104579153/
[pyinstaller _failed to execute script pyi_rth_pkgres]: https://blog.csdn.net/zizle_lin/article/details/106639076
[pyinstaller_sklearn]: https://blog.csdn.net/weixin_42434055/article/details/90296143
[python_pandas_csv_Excel]: https://blog.csdn.net/xufankang/article/details/83688379
[-_P-R_AP_mAP]: https://blog.csdn.net/qq_41994006/article/details/81051150
[PyTorch_TensorboardX]: http://www.luyixian.cn/news_show_332467.aspx
[PyTorch_TensorboardX 1]: https://blog.csdn.net/bigbennyguo/article/details/87956434
[Git_]: https://www.jianshu.com/p/bebba0d8038e
[Pycharm_C]: https://blog.csdn.net/xhaimail/article/details/105023852
[C_Pycharm]: https://blog.csdn.net/qq_34374211/article/details/89394807?utm_medium=distribute.pc_relevant.none-task-blog-BlogCommendFromMachineLearnPai2-2.nonecase&depth_1-utm_source=distribute.pc_relevant.none-task-blog-BlogCommendFromMachineLearnPai2-2.nonecase
[Sublime Text_html]: https://www.jianshu.com/p/f1e0902e4f03
[COCO]: https://www.cnblogs.com/Meumax/p/12021913.html
[labelme]: https://blog.csdn.net/Mr_kuilei/article/details/86640746
[parse_args_argsparse_python]: https://www.cnblogs.com/still-smile/p/11636958.html
[python_eval_ast.literal_eval]: https://blog.csdn.net/sun2333/article/details/82418326
[python_OpenCV2_ cv2.VideoCapture_read_waitKey]: https://blog.csdn.net/Zhou_yongzhe/article/details/80310537
[windows_pycocotools]: https://www.cnblogs.com/zhengbiqing/p/12552775.html
[Linux_Pytorch_HRNet_CUDA_Torch]: https://blog.csdn.net/Cheungleilei/article/details/106680570
[HRNet]: https://blog.csdn.net/weixin_44061195/article/details/107015655
[HRNet_ ST-GCN]: https://blog.csdn.net/Cheungleilei/article/details/106732746
[HRNet 1]: https://blog.csdn.net/weixin_44061195/article/details/106906728
[CNN_feature map]: https://blog.csdn.net/mch2869253130/article/details/88806551
[LeNet_CNN]: https://blog.csdn.net/silent56_th/article/details/53428057
[LeNet]: https://blog.csdn.net/qq_42570457/article/details/81460807
[Densenet]: https://www.jianshu.com/p/8efccef8d6f1
[resnet]: https://blog.csdn.net/JiangZuning/article/details/88168681
[AlexNet]: https://blog.csdn.net/Katrina_ALi/article/details/102854403
[GoogleNet]: https://blog.csdn.net/abc_138/article/details/80412413
[CNN_GoogLeNet_Inception v1_v4]: https://my.oschina.net/u/876354/blog/1637819
[GoogLeNet]: https://zhuanlan.zhihu.com/p/157883475
[googlenet]: https://www.cnblogs.com/sdu20112013/p/11593113.html
[fast-autoaugment]: https://blog.csdn.net/u011583927/article/details/104724419/
[ReLU_GELU]: https://baijiahao.baidu.com/s?id=1653421414340022957&wfr=spider&for=pc
[HOW TO TRAIN YOUR MAML]: https://www.dazhuanlan.com/2020/03/23/5e78113a199ca/
[CNN_VGGNet]: https://my.oschina.net/u/876354/blog/1634322
[DRN_ResNet]: https://my.oschina.net/u/876354/blog/1622896
[DenseNet]: https://blog.csdn.net/weixin_43624538/article/details/85227041
[Densely Connected Convolutional Networks-best paper-CVPR-2017]: https://blog.csdn.net/u011995719/article/details/76687476
[27]: https://my.oschina.net/u/876354/blog/1924779
[Sublime Text 3 _python]: https://my.oschina.net/u/876354/blog/1825729
[two-stage_one-stage]: https://blog.csdn.net/eternity1118_/article/details/89476028?utm_medium=distribute.pc_relevant.none-task-blog-BlogCommendFromMachineLearnPai2-2.channel_param&depth_1-utm_source=distribute.pc_relevant.none-task-blog-BlogCommendFromMachineLearnPai2-2.channel_param
[Mark R-CNN]: https://my.oschina.net/u/876354/blog/1802743
[R-CNN]: https://www.jianshu.com/p/5056e6143ed5
[Fast RCNN]: https://blog.csdn.net/u014380165/article/details/72851319
[-Selective Search]: https://www.cnblogs.com/zyly/p/9259392.html
[Graph-Based Image Segmentation]: https://blog.csdn.net/guoyunfei20/article/details/78727972
[Fast R-CNN_Fast R-CNN]: https://www.jianshu.com/p/fbbb21e1e390
[Faster RCNN]: https://zhuanlan.zhihu.com/p/31426458
[Faster RCNN 1]: https://blog.csdn.net/weixin_43198141/article/details/90178512
[Faster R-CNN_ Towards Real-Time Object Detection with Region Proposal Networks]: https://blog.csdn.net/tmylzq187/article/details/51441553
[R-CNN --_ FAST-RCNN --_ FASTER-RCNN]: https://www.cnblogs.com/dudumiaomiao/p/6560841.html
[SSD]: https://blog.csdn.net/JACK_YOUNG007/article/details/88429293
[SSD_Single Shot MultiBox Detector]: https://blog.csdn.net/hancoder/article/details/89387236
[SSD 1]: https://www.cnblogs.com/victorywr/p/12748390.html
[FPN]: https://blog.csdn.net/WZZ18191171661/article/details/79494534
[FPN _]: https://zhuanlan.zhihu.com/p/92005927
[FPN_-]: https://blog.csdn.net/baidu_30594023/article/details/82623623
[LeNet_EfficientNet_CNN]: https://zhuanlan.zhihu.com/p/148590608
[RegionProposal Network_RPN]: https://blog.csdn.net/qq_36269513/article/details/80421990
[EfficientNet-_CNN]: https://zhuanlan.zhihu.com/p/67834114
[- EfficientNet]: https://www.jianshu.com/p/2ac06d97a830
[Link 7]: https://zhuanlan.zhihu.com/p/86599013
[CVPR 18_Path Aggregation Network for Instance Segmentation]: https://blog.csdn.net/u010158659/article/details/79530125
[EfficientNet_EfficientDet]: https://blog.csdn.net/sunlanchang/article/details/103475619?utm_medium=distribute.pc_relevant.none-task-blog-BlogCommendFromMachineLearnPai2-1.channel_param&depth_1-utm_source=distribute.pc_relevant.none-task-blog-BlogCommendFromMachineLearnPai2-1.channel_param
[Link 8]: https://www.cnblogs.com/wujianming-110117/p/12481972.html
[python opencv cv2.rectangle]: https://blog.csdn.net/sinat_41104353/article/details/85171185
[Mask R-CNN]: https://www.cnblogs.com/majiale/p/9251541.html
[SIMPLE ONLINE AND REALTIME TRACKING _SORT]: https://blog.csdn.net/c20081052/article/details/86500352
[SST_ Single-Stream Temporal Action Proposals]: https://www.cnblogs.com/wangxiaocvpr/p/6985508.html
[YOLOv4-MS COCO]: https://medium.com/@alexeyab84/yolov4-the-most-accurate-real-time-neural-network-on-ms-coco-dataset-73adfd3602fe?sk=6039748846bbcf1d960c3061542591d7
[Yolo_Yolov3_Yolov4_Yolov5]: https://zhuanlan.zhihu.com/p/143747206
[GCNet_ Non-local Networks Meet Squeeze-Excitation Networks and Beyond]: https://blog.csdn.net/m0_37263345/article/details/91344786
[Beyond triplet loss_ a deep quadruplet network for person re-identification]: https://blog.csdn.net/zhangjunhit/article/details/70239948
[CenterNet_ Keypoint Triplets for Object Detection_]: https://blog.csdn.net/zhulf0804/article/details/102373351/
[DSSD _ Deconvolutional Single Shot Detector]: https://blog.csdn.net/m0_37717568/article/details/70765691
[RCNN_2_Rich feature hierarchies for accurate object detection and semantic segmentation]: https://blog.csdn.net/u011534057/article/details/51218250
[Mask R-CNN 1]: https://blog.csdn.net/WZZ18191171661/article/details/79453780
[SPP-Net _Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition]: https://blog.csdn.net/forever__1234/article/details/79910175
[Deep residual learning for image recognition]: https://blog.csdn.net/weixin_37993251/article/details/87905239
[High-Speed Tracking with Kernelized Correlation Filters]: https://blog.csdn.net/xiaozuo_zhazha/article/details/90378962
[MobileNets_Efficient Convolutional Neural Networks for Mobile Vision Applications]: https://blog.csdn.net/qq_31914683/article/details/79330343
[Squeeze-and-Excitation Networks_SENet]: https://www.jianshu.com/p/59fdc448a33f
[DenseBox_ Unifying Landmark Localization with End to End Object Detection_CVPR2015]: https://www.cnblogs.com/fourmi/p/10771387.html
[Spatial Transformer Networks_STN]: https://blog.csdn.net/WZZ18191171661/article/details/100621925
[FoveaBox_ Beyond Anchor-based Object Detector_CVPR2019]: https://www.cnblogs.com/fourmi/p/10771454.html
[AlexNet_ImageNet Classification with Deep Convolutional Neural Networks]: https://blog.csdn.net/hongbin_xu/article/details/80271291
[ECCV2018 _ _CornerNet_ Detecting Objects as Paired Keypoints]: https://blog.csdn.net/weixin_40414267/article/details/82379793
[Selective Kernel Networks_SKNet CVPR2019]: https://www.cnblogs.com/fourmi/p/10771533.html
[Scale-Aware Trident Networks for Object Detection]: https://zhuanlan.zhihu.com/p/56468485
[FSSD_ Feature Fusion Single Shot Multibox Detector]: https://zhuanlan.zhihu.com/p/67298607
[RefineNet_ Multi-Path Refinement Networks for High-Resolution Semantic Segmentation]: https://blog.csdn.net/zhangjunhit/article/details/72844862
[Feature Pyramid Networks for Object Detection]: https://blog.csdn.net/bi_diu1368/article/details/93107330
[Focal Loss for Dense Object Detection]: https://blog.csdn.net/weixin_38145317/article/details/98621095
[SSD_Single Shot MultiBox Detector 1]: https://blog.csdn.net/wweiainn/article/details/82831701
[MOT16 A Benchmark for Multi-Object Tracking]: https://blog.csdn.net/sinat_17196995/article/details/57946708
[_ ThunderNet_ Towards Real-time Generic Object Detection]: https://blog.csdn.net/u011344545/article/details/88927579
[You Only Look Once_ Unified_ Real-Time Object Detection]: https://blog.csdn.net/alip39/article/details/82389738
[yolo]: https://www.cnblogs.com/CZiFan/p/9516504.html
[YOLO9000_ Better_Faster_Stronger_YOLO9000]: https://blog.csdn.net/hysteric314/article/details/53909408
[YOLOv3_An Incremental Improvement]: https://blog.csdn.net/amusi1994/article/details/79698890
[MobileNetV2 _Inverted Residuals and Linear Bottlenecks]: https://blog.csdn.net/Julialove102123/article/details/80042929
[A real-time computer vision assessment and control of thermal comfort for group-housed pigs]: http://www.researchgate.net/profile/Hongwei_Xin/publication/267364609_Real-time_behavior-based_assessment_and_control_of_swine_thermal_comfort/links/5730ac4d08aed286ca0db966.pdf
[Deep Affinity Network for Multiple Object Tracking]: https://zhuanlan.zhihu.com/p/48115073
[FCOS_ Fully Convolutional One-Stage Object Detection_CVPR2019]: https://www.cnblogs.com/fourmi/p/10771436.html
[t-SNE]: http://www.datakit.cn/blog/2017/02/05/t_sne_full.html
[t-SNE 1]: https://zhuanlan.zhihu.com/p/28967965
[CV_Attention_Non-Local neural networks]: https://www.cnblogs.com/pprp/p/12153255.html
[Simple Online and Realtime Tracking with a Deep Association Metric 2017 _deep-sort]: https://blog.csdn.net/yuhq3/article/details/78857614
[CBAM_ Convolutional Block Attention Module]: https://blog.csdn.net/u013738531/article/details/82731257
[RepPoints_ Point Set Representation for Object Detection]: https://blog.csdn.net/qq_41375609/article/details/102729845
[CutMix_ Regularization Strategy to Train Strong Classifiers with Localizable Features]: https://blog.csdn.net/Najlepszy/article/details/104269485
[Wide Residual Networks]: https://blog.csdn.net/LYKymy/article/details/101081618
[S3FD_ Single Shot Scale-invariant Face Detector]: https://blog.csdn.net/shentanyue/article/details/81536635
[-FairMOT_A Simple Baseline for Multi-Object Tracking]: https://blog.csdn.net/DJames23/article/details/105482419
[CenterNet _Objects as Points]: https://blog.csdn.net/c20081052/article/details/89358658
[ExtremeNet_Bottom-up Object Detection by Grouping Extreme and Center Points]: https://blog.csdn.net/sinat_37532065/article/details/86693930
[Object Detection in 20 Years_ A Survey]: https://blog.csdn.net/clover_my/article/details/92794719
[MobileNet]: https://www.cnblogs.com/dengshunge/p/11334640.html
[23_MobileNet]: https://blog.csdn.net/weixin_44791964/article/details/102819915?ops_request_misc=%257B%2522request%255Fid%2522%253A%2522159738944619725211907776%2522%252C%2522scm%2522%253A%252220140713.130102334.pc%255Fblog.%2522%257D&request_id=159738944619725211907776&biz_id=0&utm_medium=distribute.pc_search_result.none-task-blog-2~blog~first_rank_v2~rank_blog_default-2-102819915.pc_v2_rank_blog_default&utm_term=mobilenet&spm=1018.2118.3001.4187
[Unet _]: https://www.jianshu.com/p/f9b0c2c74488
[3_unet_unet]: https://blog.csdn.net/weixin_44791964/article/details/102983853?ops_request_misc=%257B%2522request%255Fid%2522%253A%2522159765265119724843343839%2522%252C%2522scm%2522%253A%252220140713.130102334.pc%255Fblog.%2522%257D&request_id=159765265119724843343839&biz_id=0&utm_medium=distribute.pc_search_result.none-task-blog-2~blog~first_rank_v2~rank_blog_default-1-102983853.pc_v2_rank_blog_default&utm_term=unet&spm=1018.2118.3001.4187
[SegNet]: https://zhuanlan.zhihu.com/p/36525939
[1_Mobile_segnet]: https://blog.csdn.net/weixin_44791964/article/details/102979289?ops_request_misc=%257B%2522request%255Fid%2522%253A%2522159765301719195264556365%2522%252C%2522scm%2522%253A%252220140713.130102334.pc%255Fblog.%2522%257D&request_id=159765301719195264556365&biz_id=0&utm_medium=distribute.pc_search_result.none-task-blog-2~blog~first_rank_v2~rank_blog_default-1-102979289.pc_v2_rank_blog_default&utm_term=segnet&spm=1018.2118.3001.4187
[2_segnet]: https://blog.csdn.net/weixin_44791964/article/details/102980249?ops_request_misc=%257B%2522request%255Fid%2522%253A%2522159765301719195264556365%2522%252C%2522scm%2522%253A%252220140713.130102334.pc%255Fblog.%2522%257D&request_id=159765301719195264556365&biz_id=0&utm_medium=distribute.pc_search_result.none-task-blog-2~blog~first_rank_v2~rank_blog_default-2-102980249.pc_v2_rank_blog_default&utm_term=segnet&spm=1018.2118.3001.4187
[7_resnet_segnet]: https://blog.csdn.net/weixin_44791964/article/details/103244411?ops_request_misc=%257B%2522request%255Fid%2522%253A%2522159765301719195264556365%2522%252C%2522scm%2522%253A%252220140713.130102334.pc%255Fblog.%2522%257D&request_id=159765301719195264556365&biz_id=0&utm_medium=distribute.pc_search_result.none-task-blog-2~blog~first_rank_v2~rank_blog_default-3-103244411.pc_v2_rank_blog_default&utm_term=segnet&spm=1018.2118.3001.4187
[PSP Net]: https://zhuanlan.zhihu.com/p/42719930
[4_pspnet_pspnet]: https://blog.csdn.net/weixin_44791964/article/details/102985167?ops_request_misc=%257B%2522request%255Fid%2522%253A%2522159765440319195264523252%2522%252C%2522scm%2522%253A%252220140713.130102334.pc%255Fblog.%2522%257D&request_id=159765440319195264523252&biz_id=0&utm_medium=distribute.pc_search_result.none-task-blog-2~blog~first_rank_v2~rank_blog_default-6-102985167.pc_v2_rank_blog_default&utm_term=%E5%88%86%E5%89%B2&spm=1018.2118.3001.4187
[DeepLab]: https://www.jianshu.com/p/755b001bfe38
[CSPNet]: https://blog.csdn.net/haha0825/article/details/106102762
[CVPR2019_21]: https://zhuanlan.zhihu.com/p/81606961
[python 1]: https://www.cnblogs.com/small-office/p/9337297.html
[Python_vars]: https://blog.csdn.net/mighty13/article/details/98676613
[Python _dict]: https://www.cnblogs.com/alvin2010/p/9102344.html
[Python]: https://www.runoob.com/w3cnote/python-func-decorators.html
[cpu_nms_setup_linux.py_windows]: https://www.cnblogs.com/whlook/p/6974174.html
[python-cpu-nms]: https://blog.csdn.net/xunan003/article/details/93894860
[python_xpath]: https://www.cnblogs.com/lei0213/p/7506130.html
[python_selenium]: https://www.cnblogs.com/xiaogongjin/p/11490075.html
[Selenium]: https://www.jianshu.com/p/5bd91e245b01
[Python_-12-_cookie]: https://www.cnblogs.com/xpwi/p/9600739.html
[XPath]: https://www.w3school.com.cn/xpath/xpath_syntax.asp
[Python Selenium]: https://blog.csdn.net/weixin_36279318/article/details/79475388
[cv]: https://blog.csdn.net/weixin_36670529/article/details/100977375
[Python _ avi _ cv2.VideoWriter cv2.VideoWriter_fourcc]: https://blog.csdn.net/weixin_42419002/article/details/89403341
[Link 9]: https://www.cnblogs.com/hellojamest/p/11426565.html
[pytorch_tensorboardX]: https://blog.csdn.net/qq_33590958/article/details/103444664
[ubuntu 18.04 Linux _OpenCV]: https://blog.csdn.net/qq_38236190/article/details/88642984
[UBUNTU16.04_ROS KINETIC_INDEMIND_ORB-SLAM2]: https://www.freesion.com/article/1039296266/
[Ubuntu18.04.1_ROS_E]: https://blog.csdn.net/sinat_34130812/article/details/81666728
[sudo apt-get update_ W_ GPG _http_packages.ros.org_ros_ubuntu xenial InRelease_]: https://blog.csdn.net/dragongiri/article/details/96423662
[ubuntu_ROS_sudo rosdep init_rosdep update]: https://www.cnblogs.com/wangyarui/p/6059688.html
[IMSEE-SDK]: https://github.com/INDEMIND/IMSEE-SDK
[SDK-api]: https://imsee-sdk-docs.readthedocs.io/zh/latest/
[SDK demo]: https://www.bilibili.com/video/BV1K741157xY?from=search&seid=3698717038881818680
[Link 10]: https://zhuanlan.zhihu.com/p/22450818
[netron]: https://blog.csdn.net/nan355655600/article/details/106245563
[tensorflow_keras]: https://docs.floydhub.com/guides/environments/
[tensorflow _ keras]: https://blog.csdn.net/yeyang911/article/details/84968473
[Artbreeder]: https://artbreeder.com/browse
[stylegan2]: https://github.com/NVlabs/stylegan2
[DAIN]: https://www.jianshu.com/p/e4c3af965376
[First Order Motion Model for Image Animation]: https://zhuanlan.zhihu.com/p/136606648
[Zoom_AI_GitHub 4000]: https://zhuanlan.zhihu.com/p/135321992
[Link 11]: https://www.ithome.com/0/489/393.htm
[AttributeError_ module _tensorflow_ has no attribute _placeholder]: https://blog.csdn.net/flowingfog/article/details/93597697
[data agumentation]: https://blog.csdn.net/ouyangfushu/article/details/88186513
[Youtube]: https://www.jianshu.com/p/b44c11b4e8da
[yolo_v4_CmBN]: https://blog.csdn.net/Bruce_0712/article/details/106695591
[BN_CBN_CmBN]: https://blog.csdn.net/qq_35447659/article/details/107797737
[dlib]: https://blog.csdn.net/Hubz131/article/details/80031058
[OKS]: https://blog.csdn.net/xiaolouhan/article/details/90169297
[Link 12]: https://blog.csdn.net/qianbin3200896/article/details/88656524
[Python-OpenCV]: https://blog.csdn.net/weixin_43431189/article/details/93382538?utm_medium=distribute.pc_feed_404.none-task-blog-BlogCommendFromBaidu-2.nonecase&depth_1-utm_source=distribute.pc_feed_404.none-task-blog-BlogCommendFromBaidu-2.nonecas
[SIFT_python]: https://blog.csdn.net/qq_39022478/article/details/105281244
[SIFT_Python]: https://blog.csdn.net/zhuxiaoyang2000/article/details/53930610
[pandas DataFrame]: https://www.cnblogs.com/datasnail/p/9675410.html
[python pandas_excel]: https://www.cnblogs.com/keeptg/p/10609222.html
[python _ _pandas_DataFrame_dtype]: http://www.voidcn.com/article/p-bzlnrrhs-btm.html
[win10 python3.7 Anaconda3 _tensorflow_Keras]: https://www.cnblogs.com/tingtin/p/12361460.html
[python3.7_tensorflow]: https://blog.csdn.net/renzheadai/article/details/106167836
[tensorboard 1.14.0 has requirement setuptools_41.0.0_ but you_ll have setuptools 39.0.1]: https://blog.csdn.net/Charonmomo/article/details/97799547
[AttributeError_ module _keras_applications_ has no attribute _set_keras_submodules]: https://blog.csdn.net/zmlovelx/article/details/103300847
[Ubuntu_Anaconda_Python3.6]: https://www.cnblogs.com/dyc99/p/12546638.html
[Tensorflow2.0_AttributeError_ module _tensorflow_ has no attribute _get_default_graph]: https://blog.csdn.net/public669/article/details/99686151
[Linux vi_vim]: https://www.runoob.com/linux/linux-vim.html
[PyTorch_ResNet]: https://www.cnblogs.com/jiyou/p/11272650.html
[gan]: https://www.sohu.com/a/325882199_114877
[ProGAN_Step by step_ better than better]: https://zhuanlan.zhihu.com/p/93748098
[SAGAN]: https://blog.csdn.net/HaoTheAnswer/article/details/82733234
[DeepMind_GAN_BigGAN]: https://www.sohu.com/a/259015827_500659
[BigGAN CSDN]: https://blog.csdn.net/qq_14845119/article/details/85619705
[StyleGAN-]: https://zhuanlan.zhihu.com/p/63230738
[ios]: https://view.inews.qq.com/a/20210107A0DW3A00
[Link 13]: https://www.cnblogs.com/lky-learning/p/11653861.html
[segmentation]: https://blog.csdn.net/qq_21997625/article/details/86556667
[Link 14]: https://www.zhihu.com/question/48279880
[python_-csdn]: https://blog.csdn.net/fanxin_i/article/details/80380733
[Link 15]: https://www.theprojectspot.com/tutorial-post/ant-colony-optimization-for-hackers/10
[slide_google doc_overleaf_latex]: https://zhuanlan.zhihu.com/p/338193330
[Viterbi]: https://www.cnblogs.com/ncdxlxk/p/9240938.html
[Link 16]: https://cloud.tencent.com/developer/news/199205
[Keras_CBAM]: https://blog.csdn.net/a2824256/article/details/108752660
[python 2]: https://blog.csdn.net/weixin_39986952/article/details/82018193
[nn.init.trunc_normal]: https://discuss.pytorch.org/t/implementing-truncated-normal-initializer/4778/15
[pycharm]: https://www.jetbrains.com/help/idea/managing-plugins.html#repos
[Pycharm_docker containers]: https://blog.csdn.net/github_33934628/article/details/80919646?utm_medium=distribute.pc_relevant.none-task-blog-2~default~BlogCommendFromBaidu~default-8.control&depth_1-utm_source=distribute.pc_relevant.none-task-blog-2~default~BlogCommendFromBaidu~default-8.control