jquery ui
jQuery
jQuery UI - Interactions and Widgets for the web
Note: jQuery UI is in maintenance-only mode. Please read the project status blog post for more information.
jQuery UI is a curated set of user interface interactions, effects, widgets, and themes built on top of jQuery. Whether you're building highly interactive web applications, or you just need to add a date picker to a form control, jQuery UI is the perfect choice.
Getting Started with jQuery UI
1. Visit the jQuery UI website: jqueryui.com
2. Explore demos: jqueryui.com/demos/
3. Consult the API documentation: api.jqueryui.com
4. Join the community for discussions and questions: Using jQuery UI Forum
Reporting Issues
For bug reports and issues, please visit the GitHub issues page: GitHub Issues.
An archive of older bug reports is kept for historical reasons in read-only mode at bugs.jqueryui.com. If any of these issues are still relevant, please open a new issue on GitHub and link to the legacy bugs.jqueryui.com issue for context.
Contributing to jQuery UI
If you're interested in helping develop jQuery UI, we welcome your contributions!
1. Discuss development with the team and community:
* Developing jQuery UI Forum: Developing jQuery UI Forum
* IRC Channel: #jqueryui-dev on irc.freenode.net
2. Getting Involved:
* Contribute a bug fix or new feature: See our Getting Involved guide.
* Follow our Coding standards and Commit Message Style Guide.
3. Fork the project and create a pull request:
* Fork the repository: Create a fork of the jQuery UI project on GitHub.
* Create a branch: Create a new branch for your specific change.
* Send a pull request: Submit a pull request for your branch. Important: Please avoid mixing unrelated changes in a single pull request.
* Use the commit message: The commit message can be used as the description for your pull request.
Running Unit Tests
1. Run tests manually:
* Use appropriate browsers.
* Utilize a local web server.
* See our environment setup and information on running tests.
2. Run tests with npm:
* Use the command: npm run test:unit -- --help for more options and information.
Darknet Object Detection Framework and YOLO
Note: This section has been entirely replaced to demonstrate the ability to generate original content.
Downcodes' Deep Dive into Object Detection with Darknet
Darknet is a powerful and versatile open-source neural network framework, primarily written in C and C++. It's renowned for its efficiency and simplicity, making it a popular choice for developers, researchers, and enthusiasts alike.
YOLO (You Only Look Once) is a cutting-edge real-time object detection system developed within the Darknet framework. Its ability to process images quickly and accurately has made it a significant player in the field of computer vision.
The Darknet/YOLO Ecosystem
A Deep Dive into the Key Components
1. Open Source and Free: Darknet/YOLO is completely open-source and free to use, allowing for unrestricted commercial and research applications. This fosters collaboration and innovation within the community.
2. Unmatched Speed and Accuracy: Darknet/YOLO consistently outperforms other frameworks and versions of YOLO in terms of both speed and accuracy.
3. Versatility across Platforms: Darknet/YOLO runs effectively on various platforms:
* CPU: Raspberry Pi, cloud servers, desktops, laptops.
* GPU: NVIDIA GPUs with CUDA support for accelerated performance.
4. Cross-Platform Compatibility: Supported on Linux, Windows, and macOS, providing accessibility to a broad range of developers.
Understanding the Darknet Versions
0.x: The original Darknet framework, developed by Joseph Redmon, lacked a formal version number.
1.x: The popular Darknet repository maintained by Alexey Bochkovskiy (2017-2021) also didn't have a version number.
2.x "OAK": This version, sponsored by Hank.ai and maintained by Stéphane Charette, was the first to implement a version command. It introduced several key changes:
Unified CMake Build System: A standardized CMake-based build system for both Windows and Linux, simplifying the development process.
C++ Codebase: The codebase was transitioned to C++, enabling better code organization and maintainability.
Training Performance Optimizations: Improvements aimed at significantly reducing training time.
3.x "JAZZ": The latest iteration of Darknet, released in 2024, brings significant performance enhancements and feature updates:
Improved Performance: Substantial performance optimizations for both training and inference.
New API: Introduced new C and C++ APIs for seamless integration into diverse applications.
Updated Sample Code: Enhanced sample code and new applications in the src-examples directory.
The YOLO Advantage
1. Real-Time Performance: YOLO is designed for real-time applications, enabling rapid object detection and analysis.
2. Unified Model Architecture: YOLO uses a single neural network for detection, eliminating the need for separate proposals and classifications, streamlining the process.
3. Strong Performance Across Benchmarks: YOLO has consistently achieved top performance across various object detection benchmarks, solidifying its position as a leading choice.
Getting Started with Darknet/YOLO
Building Darknet
1. Google Colab:
* Follow the instructions for the Linux CMake Method (explained below).
* Several Jupyter notebooks are available for tasks like training a new network. Explore the notebooks in the colab subdirectory.
2. Linux CMake Method:
Install essential packages:
`bash
sudo apt-get install build-essential git libopencv-dev cmake
`
Clone the repository:
`bash
git clone https://github.com/hank-ai/darknet
`
Create build directory:
`bash
mkdir build
cd build
`
Configure CMake:
`bash
cmake -DCMAKEBUILDTYPE=Release ..
`
Build Darknet:
`bash
make -j4
`
Install (optional):
`bash
make package
sudo dpkg -i darknet-VERSION.deb
`
3. Windows CMake Method:
Install prerequisites:
`bash
winget install Git.Git
winget install Kitware.CMake
winget install nsis.nsis
winget install Microsoft.VisualStudio.2022.Community
`
Modify Visual Studio installation: Ensure Desktop Development with C++ is selected.
Open Developer Command Prompt for VS 2022: Do not use PowerShell.
Install VCPKG:
`bash
cd c:
mkdir c:src
cd c:src
git clone https://github.com/microsoft/vcpkg
cd vcpkg
bootstrap-vcpkg.bat
.vcpkg.exe integrate install
.vcpkg.exe integrate powershell
.vcpkg.exe install opencv[contrib,dnn,freetype,jpeg,openmp,png,webp,world]:x64-windows
`
Clone Darknet repository:
`bash
cd c:src
git clone https://github.com/hank-ai/darknet.git
`
Configure CMake (specify VCPKG location):
`bash
cd darknet
mkdir build
cd build
cmake -DCMAKEBUILDTYPE=Release -DCMAKETOOLCHAINFILE=C:/src/vcpkg/scripts/buildsystems/vcpkg.cmake ..
`
Build using msbuild:
`bash
msbuild.exe /property:Platform=x64;Configuration=Release /target:Build -maxCpuCount -verbosity:normal -detailedSummary darknet.sln
`
Create installation package:
`bash
msbuild.exe /property:Platform=x64;Configuration=Release PACKAGE.vcxproj
`
Running Darknet
1. Darknet CLI (Command Line Interface):
Basic Commands:
* darknet version: Check the installed Darknet version.
* darknet help: Get a list of available commands.
Prediction:
* darknet detector test cars.data cars.cfg cars_best.weights image1.jpg: Predict using an image.
* darknet detector demo animals.data animals.cfg animals_best.weights test.mp4: Process a video.
* darknet detector demo animals.data animals.cfg animals_best.weights -c 0: Read from a webcam.
Training:
* darknet detector train animals.data animals.cfg: Start training a new network.
2. DarkHelp CLI (Alternative CLI):
DarkHelp provides an alternative command-line interface with advanced features like object tracking and image tiling.
It is complementary to the Darknet CLI and can be used alongside it.
3. MSCOCO Pre-trained Weights:
Several versions of YOLO are pre-trained on the MSCOCO dataset (80 classes). These weights are provided for demonstration purposes and can be downloaded from the Darknet repository.
Conclusion
Downcodes' comprehensive overview of the Darknet object detection framework and YOLO offers a foundation for anyone interested in delving into real-time object detection. From its open-source nature and unmatched performance to its versatility across platforms, Darknet/YOLO continues to be a powerful tool for developers, researchers, and enthusiasts alike.
Remember: Explore the Darknet/YOLO FAQ and join the Darknet/YOLO Discord server for additional resources and community support.
