👁️ Computer Vision with Python & OpenCV — The Ultimate A‑to‑Z Learning Repository

🚀 Explore the vast landscape of computer vision through our comprehensive repository, It include resource about deep learning for vision, image processing tutorials, OpenCV projects, YOLO object detection, CNN tutorials, vision transformers, serving as your A-Z guide to this captivating field. Whether you're delving into image processing, object detection, or deep learning, you'll find a treasure trove of resources here to deepen your understanding and hone your skills.

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computer vision course, computer vision with Python, AI in image analysis, edge detection, computer vision GitHub repository, free computer vision resources

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---

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🔗 Basic of Computer Visionl

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📕Course Title - 👁️ Course Title: Basics of Computer Vision

👁️ Chapter1: - Foundations of Computer Vision

Topic Name/Tutorial	Video	Code	Note	Difficulty
1- What is computer Vision	1		blog	Beginer
✅2-Computer Vision Tasks and Applications	1-2		Link	Beginer
✅Best Free Resources to Computer Vision	---	---	Link	Beginer

🔹Chapter2: - Image As Function

Topic Name/Tutorial	Video	Notbook	Extra Resources
✅1-Images as Functions Part 1?	1-2
✅2-Images as Functions Part 2?	1-2
✅3-Define an Image as a Function (Quiz)	1-2-3
✅4-Color Planes and Color Image as a Function(Quiz)	1-2-3-4-4
✅5- Digital Images	1-2-3		1
✅6-Compute Image Size Quiz-s	1-2
✅7-Read image in Matlab and Python-S	---
✅8-Image Size and Data Type Quiz/Solution-S	1
✅9-Crop an Image-s	1
✅10-Add 2 Images-s	1-2-3
✅11-Multiply image by a scaler and Blend 2 Images⭐️	1-2-3-3
✅12-Common Types of Noise⭐️	1-2
✅13-Image Difference⭐️	1-2-3-4
✅14-Generate Gaussian Noise⭐️	1-2
✅15-Effect of Sigma on Gaussian Noise⭐️	1-2-3
✅16-Apply Gaussian Noise⭐️	1-2
✅17-Displaying Images in Matlab and Python⭐️	1
✅Minin Project-🚦 Smart Surveillance Frame Analyzer

🔹Chapter3: - Filtering

Topic Name/Tutorial	Video	NoteBook	Extra Resoruces
✅1- What is Filtering?	1-2		---
✅2- What is Gaussian Noise?	1-2-3
✅3-Averaging Assumptions	1-2-3
✅4-Weighted Moving Average	1-2-2
✅5-Moving Average In 2D	1-2
*✅4- Correlation Filtering?	1
✅5- Averaging Filter?	1
✅6- Gaussian Filter?	1-2
✅7- Gaussian Filter with Matlab and Python?	1
✅8- Remove Noise?(r)	1-2
✅Minin Project-Motion Detection in Surveillance Footage using Frame Differencing and Gaussian Smoothing

🔹Chapter4: - Linearity and Convolution

Topic Name/Tutorial	Video	NoteBook
🌐1- Introduction of linear intuition of filtering	1
🌐2- Impulse Function and Response	1
🌐4- Filtering an Impulse Signal	1
🌐5- Correlation vs Convolution	1-2
🌐5-Properties of Convolution	1
🌐6-Computational Complexity and Separability	1
🌐7-Boundary Issues	1
🌐8-Methods	1
🌐9-Explore Edge Options	1
🌐10-Practicing with Linear Filters	1-2
🌐11-Different Kinds of Noise	1-2-3

🔹Chapter5: - Filters as Templates

Topic Name/Tutorial	Video	NoteBook
🌐1- Introduction of Filters as templates, 1D correlation and 2D Correlations	1-2 -3
🌐2- Find Tempalte ID	1-2
🌐3- Template Matching⭐️	1-2-3-4-5

🔹Chapter6: - Edge detection: Gradients

Topic Name/Tutorial	Video	NoteBook
🌐1- Pattern Finding and Feature Detection	1
🌐2- Understanding Edges in Images: Why They Matter in Visual Perception	1-2
🌐3- Edge Detection⭐️	1
🌐4-Derivatives and Edges⭐️	1
🌐5-What is Gradients⭐️	1
🌐6-Finite Differences⭐️	1
🌐7-Partial Derivatives of an Image⭐️	1
🌐8-The Discrete Gradient⭐️	1-2
🌐9-Sobel Operator⭐️	1-2-3
🌐10-Well Known Gradients⭐️	1
🌐11-Gradients direction⭐️	1
🌐12-But in the Real World⭐️	1
✅13-Feature Description	1

🔹Chapter7: - Edge detection: 2D operators

Topic Name/Tutorial	Video	NoteBook
🌐1- Introduction	1
🌐2-Derivative of Gaussian Filter 2D	1
🌐3- Effect of Sigma on Derivatives	1
**🌐4-Canny Edge Operator P1 **	1
🌐5-Canny Edge Operator P2	1
🌐6- For Your Eyes Only Demo	1-2
🌐7-Canny Results	1
🌐8-Single 2D Edge Detection Filter	1

🔹Chapter8: - L1 Hough transform: Lines

Topic Name/Tutorial	Video	NoteBook
🌐1- Introduction	1
🌐2-Parametric Model	1
🌐3-Line Fitting	1
🌐4-Voting	1-2
🌐5-Hough Space	1-2
🌐6-Polar Representation for Lines	1
🌐7-Basic Hough Transform Algorithm	1
🌐8-Complexity of the Hough Transform	1
🌐9-Hough Example	1
🌐10-Hough Demo	1
🌐11-Hough on a Real Image	1
🌐12-Impact of Noise on Hough	1
🌐13-Extensions	1
🌐🧪 Mini Real-Life Project: Detecting Road Lane Markings in Real Images Using the Hough Transform	--

🔹Chapter9: - L2 Hough transform: Circles

Topic Name/Tutorial	Video	NoteBook	Note	Difficulty levels
🌐1-Understanding Hough Transform for Circle	1		---	🟧 Intermediate
🌐2-Detecting Circles with Hough	1		Link	🟧 Intermediate
🌐3-Hough Transform for Circles	1		Link	🟧 Intermediate
🌐4-Algorithm for Circles	1		Link	🟧 Intermediate
🌐5-Voting Practical Tips	1		Link	🟧 Intermediate
🌐6-Pros and Cons	1		Link	🟧 Intermediate
🌐Minin Projects-🎯 Detecting Road Traffic Signs (Circular Signs)	---		Link	🟧 Intermediate

🔹Chapter10: - L3 Generalized Hough transform

Topic Name/Tutorial	Video	NoteBook	Note	Difficulty levels	Extra Resources
🌐1-Introduction of Generalized Hough transform	1		Notes	🟧 Intermediate
🌐2-Generalized Hough Transform	1		Notes	🟧 Intermediate	1
🌐3-Generalized Hough Transform Example	1		Note	🟧 Intermediate	1
🌐4-Generalized Hough Transform Algorithm	1		Note	🟧 Intermediate	1
🌐5-Application in Recognition	1		Note	🟧 Intermediate	1
🌐6-Training	1		Note	🟧 Intermediate	--
🌐7-Application in Recognition	1		Note	🟧 Intermediate	--

🔹Chapter11: - L1 Fourier transform

Topic Name/Tutorial	Video	NoteBook	Note	Difficulty levels	Extra Resources
🌐1-Introduction of Frequency Analysis in Computer Vision	1-2		Notes	---
🌐2-Dali	1-2		Notes	---
🌐3-Basis Sets	1		Notes	---
🌐4-Fourier	1		Notes	---
🌐5-A Sum of Sines	1-2-3		Notes	---
🌐6-Time and Frequency	1-2-3		Notes	---
🌐7-Fourier Transform	1-2		Notes	---
🌐8-Computing Fourier Transform	1-2		Notes	---
🌐9-Fourier Transform More Formally	1-2		Notes	---
🌐10-Frequency Spectra	1-2		Notes	---
🌐11-Limitations	1-2		Notes	---
🌐12-Fourier Transform to Fourier Series	1-2		Notes	---
🌐13-2D	1-2		Notes	---
🌐14-Example	1-2		Notes	---

🔹Chapter12: - L2 Convolution in frequency domain

Topic Name/Tutorial	Video	NoteBook	Note	Difficulty levels	Extra Resources
🌐1-Introduction	1-2		Notes	---
🌐2-Fourier Transform and Convolution	1-2		Notes	---
🌐3-FFT	1-2		Notes	---
🌐4-Smoothing and Blurring	1-2		Notes	---
🌐5-2D Example	1-2		Notes	---
🌐6- Low and High Pass Filtering	1-2		Notes	---
🌐7-Properties of Fourier Transform	1-2		Notes	---
🌐8-Fourier Pairs	1-2		Notes	---

🔹Chapter13: - Aliasing

Topic Name/Tutorial	Video	NoteBook	Note	Difficulty levels	Extra Resources
🌐1-Introduction	1-2		Notes	---
🌐2-Fourier Transform Sampling Pairs	1-2		Notes	---
🌐3-Sampling and Reconstruction	1-2		Notes	---
🌐4-Sampling in Digital Audio	1-2		Notes	---
🌐5-Undersampling	1-2		Notes	---
🌐6-Aliasing	1-2		Notes	---
🌐7-Antialiasing	1-2		Notes	---
🌐8-Impulse Train and Bed of Nails	1-2		Notes	---
🌐9-Sampling Low Frequency	1-2		Notes	---
🌐10-Sampling High Frequency Signal	1-2		Notes	---
🌐11-Aliasing in Images	1-2		Notes	---
🌐12-Campbell-Robson Contrast Sensitivity	1-2		Notes	---
🌐13-Image Compression	1-2		Notes	---

🔹Chapter: - Feature Detection (Comming Soon)

Topic Name/Tutorial	Video	NoteBook	Note	Difficulty levels	Extra Resources
🌐1-SIFT feature detection	1		Notes	---
🌐1-SURF feature detection	1		Notes	---
🌐1-ORB feature detection	1		Notes	---

🔹Chapter: - Deep Learning for Vision (Comming Soon)

Topic Name/Tutorial	Video	NoteBook	Note	Difficulty levels	Extra Resources
🌐1-convolutional neural networks	1		Notes	---
🌐1-image classification	1		Notes	---
🌐1-transfer learning	1		Notes	---

🔹Chapter: - Object Detection(Comming Soon)

Topic Name/Tutorial	Video	NoteBook	Note	Difficulty levels	Extra Resources
🌐1-YOLO object detection	1		Notes	---
🌐1-Faster R-CNN	1		Notes	---
🌐1-SSD object detection	1		Notes	---

🔹Chapter: - Image Segmentation(Comming Soon)

Topic Name/Tutorial	Video	NoteBook	Note	Difficulty levels	Extra Resources
🌐1-semantic segmentation	1		Notes	---
🌐1-instance segmentation	1		Notes	---
🌐1-U-Net neural network	1		Notes	---
🌐1-Mask R-CNN	1		Notes	---

🔹Chapter: - Others topics

Topic Name/Tutorial	Video	NoteBook	Note	Difficulty levels	Extra Resources
🌐1-How LLMs Actually Understand Images	1		Notes	---

📕Course Title - 👁️ Course Title: Advance Computer Vision

YouTube Channels

• CS 198-126: Modern Computer Vision Fall 2022 (UC Berkeley) - FREE

Course

• Computer Vision by huggingface - FREE

👁️ Chapter1: - Vision Transformers

explore transformer architecture in the context of computer vision and learn how they compare to CNNs. Understand common vision transformers such as Swin, DETR, and CVT, along with techniques for transfer learning and fine-tuning.

Topic Name/Tutorial	Video	Code	Note	Extra Resoruces
1- Vision Transormers for image classification	1-2		---	---
2-Swin Transformer	1		---	---
3-CvT: Convolutional Vision Transformer Architecture and Implementation	1		---	---
4-Dilated Neighborhood Attention Transformer (DINAT)		1		---
5-MobileViT v2	1		---	---

👁️ Chapter2: - Multimodal Models

understand the fusion of text and vision by exploring multimodal tasks like image-to-text and text-to-image. Study models such as CLIP and its relatives (GroupViT, BLIPM, Owl-VIT), and master transfer learning techniques for multimodal tasks.

Topic Name/Tutorial	Video	Code	Note	Difficulty
1- What is computer Vision	1	[]

👁️ Chapter2: - Generative Models

explore generative models, including GANs, VAEs, and diffusion models. Learn about their differences and applications in tasks such as text-to-image, image-to-image, and inpainting.

Topic Name/Tutorial	Video	Code	Note	Difficulty
1- What is computer Vision	1	[]

👁️ Chapter2: - Video and Video Processing :

examine the characteristics of videos, the role of video processing, and the challenges compared to image processing. Explore temporal continuity, motion estimation, and practical applications in video processing.

Topic Name/Tutorial	Video	Code	Note	Difficulty
1- What is computer Vision	1	[]

👁️ Chapter3: - 3D Vision, Scene Rendering, and Reconstruction

delve into the complexities of three-dimensional vision, exploring concepts like Nerf and GQN for scene rendering and reconstruction. Understand the challenges and applications of 3D vision in computer vision, and how it provides an even more comprehensive view of spatial information..

Topic Name/Tutorial	Video	Code	Note	Difficulty
1- What is computer Vision	1	[]

👁️ Chapter4: - Model Optimization

Explore the critical aspects of model optimization. Cover techniques such as model compression, deployment considerations, and the usage of tools and frameworks. Include topics like distillation, pruning, and TinyML for efficient model deployment

Topic Name/Tutorial	Video	Code	Note	Difficulty
1- What is computer Vision	1	[]

👁️ Chapter6: - Synthetic Data Creation

discover the importance of synthetic data creation using deep generative models. Explore methods like point clouds and diffusion models and investigate major synthetic datasets and their applications in computer vision

Topic Name/Tutorial	Video	Code	Note	Difficulty
1- What is computer Vision	1	[]

👁️ Chapter7: - Zero Shot Computer Vision

delve into the realm of zero-shot learning in computer vision, covering aspects of generalization, transfer learning, and its applications in tasks such as zero-shot recognition and image segmentation. Explore the relationship between zero-shot learning and transfer learning across various computer vision domains.

Topic Name/Tutorial	Video	Code	Note	Difficulty
1- What is computer Vision	1	[]

👁️ Chapter8: - ** Ethics and Biases in Computer Vision**

understand the ethical considerations specific to computer vision. Explore why ethics matter, how biases can infiltrate AI models, and the types of biases prevalent in these domains. Learn how to do bias evaluation and mitigation strategies, emphasizing responsible development and deployment of AI technologies..

Topic Name/Tutorial	Video	Code	Note	Difficulty
1- What is computer Vision	1	[]

👁️ Chapter9: - ** Outlook and Emerging Trends**

explore current trends and emerging architectures . Delve into innovative approaches like Retentive Network, Hiera, Hyena, I-JEPA, and Retention Vision Models.

Topic Name/Tutorial	Video	Code	Note	Difficulty
1- What is computer Vision	1	[]

📕 Computer Vision Resources

🔹Chapter1: - Free Courses

Title/link	Description	Reading Status
✅1- Deep Learning for Computer Vision	by Michigan Online,Youtube	Pending
✅2- Introduction to Computer Vision	by Michigan Online,Youtube	Pending
✅2- Introduction of Computer Science	It is free course and it contain notes and video	Inprogress
✅3-Community Computer Vision Course	It is free course huggingface and it contain notes and video	Pending
✅4-Computer Vision Lane Detection Playlist	Highly recommend for anyone working on self-driving projects, OpenCV practice, or just learning how CV pipelines are structured in real-world scenarios.
✅5-Stanford CS231N Deep Learning for computer Vsion	Highly recommend for anyone working on self-driving projects, OpenCV practice, or just learning how CV pipelines are structured in real-world scenarios.
✅6-The Ancient Secrets of Computer Vision by Joseph Redmon	Highly recommend for anyone working on self-driving projects, OpenCV practice, or just learning how CV pipelines are structured in real-world scenarios.
✅7-The Ancient Secrets of Computer Vision by Joseph Redmon	Highly recommend for anyone working on self-driving projects, OpenCV practice, or just learning how CV pipelines are structured in real-world scenarios.
✅8-Computer Vision with Embedded Machine Learning	Highly recommend for anyone working on self-driving projects, OpenCV practice, or just learning how CV pipelines are structured in real-world scenarios.
✅9-CS 198-126: Modern Computer Vision Fall 2022 (UC Berkeley)	Highly recommend for anyone working on self-driving projects, OpenCV practice, or just learning how CV pipelines are structured in real-world scenarios.
✅10-Computer Science courses with video lectures)	Highly recommend for anyone working on self-driving projects, OpenCV practice, or just learning how CV pipelines are structured in real-world scenarios.
✅11-CS231n: Deep Learning for Computer Vision	Highly recommend for anyone working on self-driving projects, OpenCV practice, or just learning how CV pipelines are structured in real-world scenarios.
✅12-Introduction to Computer Vision	Highly recommend for anyone working on self-driving projects, OpenCV practice, or just learning how CV pipelines are structured in real-world scenarios.
✅13-Computer Vision Course by hugging face	we’ll cover everything from the basics to the latest advancements in computer vision.	Github

🔹Chapter2: - Important Website

Title/link	Description	Code
✅1- Road Map	Road Map on Coggle	---
✅2-visionbrick)	Road Map on Coggle	---
✅3-computer Vision Study Plan)	Road Map on Coggle	---
✅4-computer Vision with keras	implement all Computer In keras	---

🔹Chapter2: - Road Map

Title/link	Description	Code
✅1- ai-learning-roadmaps	Road Map on Coggle	---

🔹Chapter3: - Important Social medica Groups

Title/link	Description	Code
✅1- Jeff Heaton	It is Videos and github	---
✅2- First Principles of Computer Vision	It is Videos and github	---
✅3-Yannic Kilcher	It is Videos and github	---
✅4-AI-ML-Roadmap-from-scratch	It is Videos and github	---

🔹Chapter4: - Free Books

Title/link	Description	Code
✅1- Foundations of Computer Vision	Antonio Torralba, Phillip Isola, and William Freeman	---
✅2- Computer Vision: Algorithms and Applications, 2nd ed	© 2022 Richard Szeliski, The University of Washington	---
✅3- Foundations of Computer Vision	Antonio Torralba, Phillip Isola, and William Freeman	---
✅4- Comprehensive Study Resource	A curated collection of books and references for Computer Vision, Machine Learning, Deep Learning, NLP, Python, and more.
✅5- AI-ML-Roadmap-from-scratch	A curated collection of books and references for Computer Vision, Machine Learning, Deep Learning, NLP, Python, and more.
✅6- Dive into Deep Learning	Antonio Torralba, Phillip Isola, and William Freeman	---

|---|

🔹Chapter4: - List of Computer Vision Model

Category	Models	Notes
Classification	AlexNet, VGG, ResNet, DenseNet, EfficientNet, ViT 🔴🔵, Swin Transformer 🔴🔵, ConvNeXt 🔵	Image classification (CNNs and Transformers)
Object Detection	R-CNN, Fast R-CNN, Faster R-CNN, YOLO, SSD, RetinaNet, DETR 🔴🔵, Mask R-CNN	Detects objects with bounding boxes or masks
Segmentation	FCN, U-Net, DeepLab, PSPNet, SegFormer 🔴🔵, SAM 🔴🔵	Pixel-level understanding of images
Generative Models	Autoencoders, VAE, GAN, DCGAN, CycleGAN, StyleGAN, BigGAN, Diffusion Models (DDPM 🔵), DALL·E 🔴🔵, Stable Diffusion 🔵	Image synthesis & generation
3D & Video Models	PointNet, NeRF 🔵, 3D CNNs	3D object recognition, volumetric data & video understanding

---

Legend:

• 🔴 Transformer-based
• 🔵 Introduced after 2020

🔹Chapter4: - Colab Notebooks

Title/link	Description	Code
✅1- Top Computer Vision Google Colab Notebooks	Here is a list of the top google colab notebooks that use computer vision to solve a complex problem such as object detection, classification etc:	---
✅2-roboflow/Notebooks	This repository offers a growing collection of computer vision tutorials. Learn to use SOTA models like YOLOv11, SAM 2, Florence-2, PaliGemma 2, and Qwen2.5-VL for tasks ranging from object detection, segmentation	---
✅3-Machine Learning Notbook by google colab	This repository offers a growing collection of computer vision tutorials. Learn to use SOTA models like YOLOv11, SAM 2, Florence-2, PaliGemma 2, and Qwen2.5-VL for tasks ranging from object detection, segmentation	---

🔹Chapter5: - Github Repository

Title/link	Description	Status
✅1- Computer Science courses with video lectures	It is Videos and github	Pending
✅2-courses & resources	It is course of all AI domain	Pending
✅3-AIBauchi-Computer-Vision-Bootcamp	It is course of all AI domain	Inprogress
✅4-Awesome Computer Vision	It is course of all AI domain	Inprogress
✅5-Community-led Computer Vision Community Course	This is the repository for a community-led course on Computer Vision. Over 60 contributors from the Hugging Face	Inprogress
Computer Vision	---	---
Computer Vision Tutorial Series M1C1	---	---
Learning-based 3D Vision	---	---

👁️ Chapter 1: - 🔍 Tools, Frameworks & Platforms

Understanding all the tools, frameworks, architectures, and ecosystems around Computer Vision can sometimes feel harder than understanding the models themselves.
Below are the ones I’ve explored and used enough to feel confident recommending.
Of course, these won’t solve every use case, and I’m not listing every supporting technology you might need to build real-world AI systems, but it’s a solid starting point.

Tool / Framework	Description	Resources
✅1- RBOT (ROI-Based Object Tracking)	An alternative to YOLO for custom object tracking. Unlike traditional deep learning models that require thousands of images per object, RBOT aims to learn from 50–100 samples and track objects without bounding box detection.	---
✅2- skimage (scikit-image)	Open-source Python library for image processing and computer vision, built on NumPy/SciPy.	Docs
✅3- OpenCV	The most widely used library for image/video processing, feature extraction, filtering, and classical CV tasks.	Docs
✅4- Ultralytics YOLO	State-of-the-art object detection and segmentation framework, supporting YOLOv5–YOLOv8.	Docs
✅5- Detectron2	Facebook AI’s modular framework for object detection, segmentation, and keypoint detection.	Docs
✅6- TensorFlow	Google’s end-to-end machine learning and deep learning framework with strong support for production and deployment.	Docs
✅7- PyTorch	Widely used deep learning framework from Meta, popular in research and CV applications due to its flexibility and ease of use.	Docs
✅8- Keras	High-level API for building and training neural networks quickly, running on top of TensorFlow.	Docs
✅9- FastAI	PyTorch-based library for rapid prototyping of CV and NLP models, with high-level abstractions.	Docs
✅10- MMDetection	OpenMMLab’s powerful toolbox for object detection and instance segmentation, supporting hundreds of models.	Docs
✅11- MONAI	PyTorch-based framework for medical imaging, specialized for segmentation, classification, and 3D imaging.	Docs
✅12- Albumentations	Fast and flexible library for image augmentations, widely used in CV pipelines.	Docs
✅13- DVC (Data Version Control)	A tool for versioning datasets and ML experiments, ensuring reproducibility in CV research.	Docs

👁️ Chapter1: - Importatant tutorial

Title/link	Description	Status
✅1- Multimodal Data Analysis with Deep Learning	It is Videos and github	pending

💻 Workflow:

• Fork the repository

• Clone your forked repository using terminal or gitbash.

• Make changes to the cloned repository

• Add, Commit and Push

• Then in Github, in your cloned repository find the option to make a pull request

print("Start contributing for Computer Vision")

⚙️ Things to Note

• Anybody interested in learning and contributing to computer Vision repository
• There are no hard prerequisites other than a dedication to learning
• Some experience with the following will be beneficial:,C++ Programming, Basic of Computer
• You can only work on issues that have been assigned to you.
• If you want to contribute the algorithm, it's preferrable that you create a new issue before making a PR and link your PR to that issue.
• If you have modified/added code work, make sure the code compiles before submitting.
• Strictly use snake_case (underscore_separated) in your file_name and push it in correct folder.
• Do not update the README.md.

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• Computer Vision Basic

License

This project is licensed under the MIT License - see the LICENSE file for details.

All source code and educational material in this repository are released under the MIT License.

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