π₯ this article by [Moneo-ai] original, you big guy, the first to report, a lot of care
β€οΈ YOLO combat, you only see this article, half foot has been introduced
π Life is too short. Welcome to learn AI with Murray
π recently will gradually put some dry goods blog, integration into the gold digging platform, thank you for checking
π 1024, dry goods coming
π Deep learning, adjusting and moving bricks, developing sharp tools
You deserve VSCode and pycharm
- β€ οΈ VSCode Linux server for remote connection development debug | to | | Python remote debugging c + + remote debugging VSCode artifact, you’re worth it
- β€ οΈ Windows conda pycharm Linux server configuration under the environment of the strongest guide | deep learning model of remote debugging | SFTP synchronous transmission
If you are running Windows, it is highly recommended that you start with a virtual machine or dual system
- π latest detailed graphic tutorials create Ubuntu20 VMware virtual machine | small white to white tutorial
- π ubuntu20 conda under a virtual machine environment build trip | opencv blurred image determination test
π Establishment of basic environment for deep learning model training
π [Moli AI] blog covers: many categories of deep learning environment construction, model training, paper code testing, model deployment, basic tutorial, continuous update, quality and quantity, welcome to refer to
π§ Cuda installation
π ubuntu18 cuda11.2 graphic tutorials to the current user install cuDNN8.1 | configuration
π under Linux server for the current user install their own CUDA 10.0 | bsde | “effective installation”
π Install Pytorch and Torchvision in Cuda10.0 on Linux – any version of cuda10.0 will be able to install the blog
π Linux can install multiple versions of the Cuda? | how to switch Cuda version
π see CUDA and cuDNN version number | Win10 CUDA10 and cuDNN installation
π cudnn tar installation package quickly get | cloud disk share | γ β€ οΈ cudnn installation package β€ οΈ γ
π¨ Learn about ACCELERATED installation of various libraries through Cuda and PIP
- π anaconda conda switch to a domestic source | Windows and Linux configuration method
- π Linux and Windows setup PIP image source | the most practical environment download speed Settings
π¦ Pytorch installation
π Install Pytorch and Torchvision in Cuda10.0 on Linux – any version of cuda10.0 will be able to install the blog
π pytorch under Linux 1.8 installed minimalist | fast according to the official website command pytorch (more detailed)
π§ Ncnn installation
- π NCNN compile | example – column post operation
- π NCNN – Installation – Official tutorial
π¨ TensorRT installation
- π NVIDIA TensorRT installation package download share | take no x | γ β€ οΈ TensorRT installation β€ οΈ γ
π¦ openCV installed
- π openCV installed | the most basic of openCV program run the example “Linux”
π§ ONNX installation
- π ONNX person | ONNX – Python installation γ γ
π effective entry target detection YOLO combat series selection
π¦ YOLO Theory explanation learning chapter
Can be found quality OK interpretation
A new Gitee warehouse has been built for everyone to download
- π interprets CVPR2016 target detection paper YOLO from five aspects
- π YOLOv2 / YOLO9000 For in-depth understanding
- π YOLO series yOLO V3
- π YOLOv4 θ― ζ – V4 It’s finally here!
- π read YOLO V5 and YOLO V4
π§ Yolov5 series
- | peak π YOLOv5 coco128 training sample | β€ οΈ detailed records β€ οΈ | YOLOv5 γ γ
- π YOLOv5 COCO training data sets | γ γ YOLOv5 training
π¨ YOLOX series
- π YOLOX | | testing peak COCO repetition training γ YOLOX combat γ
- π YOLOX (pytorch) model ONNX export | run reasoning YOLOX practical 2 γ γ
- π YOLOX (PyTorch) model to ONNX to NCNN operation reasoning
- π YOLOX (PyTorch) Model to tensorRT Operation Inference
π¦ Yolov3 series
- Yolov3 (darknet) training – testing – model conversion β€οΈdarknet to NCNN C++ operation reasoning β€οΈ
π¦ continue to add updates
β€οΈ everyone, don’t forget to collect, thank sanlian β€οΈ
