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Connect with us on Social Media! 📸 Instagram: https://www.instagram.com/algorithm_a... 🧵 Threads: https://www.threads.net/@algorithm_av... 📘 Facebook: / algorithmavenue7 🎮 Discord: / discord link-https://colab.research.google.com/dri... From Black & White to Color — Powered by AI! In this project, we build an intelligent CNN-based Image Colorization system that transforms grayscale images into realistic colored images using deep learning. Instead of manually adding colors, our model learns to predict them automatically by understanding image patterns, textures, and objects. We use a U-Net encoder–decoder architecture along with the LAB color space, where the model takes the brightness (L channel) as input and predicts the color components (A & B channels). 🚀 What you’ll learn in this project: How image colorization works using deep learning Why LAB color space is better than RGB for this task Understanding encoder–decoder (U-Net) architecture Step-by-step implementation using PyTorch Training a CNN to generate colors from grayscale images Testing on custom images 🧠 Technologies Used: PyTorch • CNN • U-Net • OpenCV • NumPy • scikit-image 📊 This project is perfect for: Machine Learning & Deep Learning beginners Computer Vision enthusiasts Students building academic or portfolio projects Anyone curious about how AI can “see” and color images 👍 If you enjoyed the video, don’t forget to Like, Share and Subscribe for more deep learning and AI tutorials! #deeplearning #cnn #computervision #imagecolorization #machinelearning #artificialintelligence #pytorch #neuralnetworks #unet #encoderdecoder #labcolorspace #grayscale #imageprocessing #aiproject #mlproject #datascience #ai #python #opencv #skimage #visionai #dlproject #automation #imagegeneration #aidevelopment #coding #programming #tech #innovation #learning #computerscience #aiengineer #mlengineer #deeplearningproject #cvproject #imageai #modeltraining #neuralnetwork #aiinnovation