🧠 GenAI Lab – Computer Vision Tutorials

📘 A curated collection of tutorials covering the breadth of Computer Vision, from foundational concepts to cutting-edge Generative AI applications.

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📚 Live Book Preview

📖 Click here to view the full interactive Jupyter Book ➜

Short-form, high-impact tutorials include:

• 📷 Image Basics (pixels, RGB, histograms)
• 🌈 Color Spaces (HSV, LAB)
• 🧪 Filters and Transforms
• 📦 Segmentation & Object Detection
• 🤖 Vision Transformers & CLIP
• 🌀 Generative AI (Diffusion, Inpainting)

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📁 Plan of action

cv-tutorials/
├── basics/                # Image processing, filters, contours
├── classical_cv/          # Feature detection, tracking
├── deep_learning/         # CNNs, segmentation, detection
├── transformers/          # Vision Transformers, DETR
├── multimodal/            # CLIP, BLIP, OWL-ViT
├── generative_ai/         # Stable Diffusion, inpainting
├── datasets/              # Tutorials for CV datasets
├── utils/                 # Common tools/utilities
└── README.md              # This file

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🚀 Getting Started

# Clone the repository
git clone git@github.com:GenAI-lab-tutorials/cv-tutorials.git
cd cv-tutorials

# Setup environment (recommended)
conda create -n genai_lab python=3.10 -y
conda activate genai_lab
pip install -r requirements.txt

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🎯 Goals

• Build a unified knowledge base for learning CV end-to-end
• Offer reproducible notebooks and scripts
• Support beginner to advanced users
• Encourage experimentation and community contributions

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👥 Contributing

We welcome pull requests! If you’d like to contribute:

1. Open an issue or discussion.
2. Fork the repo.
3. Create a branch with your tutorial.
4. Submit a PR.

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📬 Contact

For ideas, suggestions, or feedback:

• Twitter: @shravankumar147
• GitHub: shravankumar147

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Stay tuned — more tutorials and structured experiments coming soon!