📩 Subscribe to our Newsletter (👀Coming 🔜): In this video, we leave the theory behind and build a real-world industrial computer vision system from scratch. Human eyes aren't built for high-speed conveyor belts—but Convolutional Neural Networks (CNNs) are. Join me as I deconstruct the entire engineering journey of building an anomaly detector for water bottle production. We go from a folder of messy, transparent PNGs to a high-performance model achieving 98% accuracy on hard-to-spot defects. We cover the full stack of Machine Learning engineering: The Data Nightmare: Handling transparency (Alpha channels) in images and why resizing down actually helped the model learn faster. Solving Class Imbalance: How to train a model when you don't have enough "bad" data. I walk through my custom scripts for Geometric Augmentation and Synthetic Defect Injection (mathematically generating scratches and impurities). The Architecture: A deep dive into the custom CNN built with PyTorch, explaining Convolutions, ReLU, Batch Normalization, and Adaptive Pooling. The "Black Box" Problem: How I used a custom feedback loop to discover why the model was failing (spoiler: it was confusing studio light glare with scratches) and how we fixed it. Key Technical Concepts Explained: Data Pipeline: Normalizing RGBA to RGB & Bicubic Resampling. Synthetic Data: Using OpenCV to inject realistic scratches, noise, and occlusion. CNN Layers: Feature extraction hierarchy (Edges - Shapes - Concepts). Optimization: using the Adam optimizer and BCEWithLogitsLoss for stability. Validation: Why "Accuracy" is a vanity metric and why we track False Negatives. Timestamps: 0:00 - The problem with human quality control 1:05 - Data Engineering: Handling transparent images 2:28 - The resolution trap (Why 500px failed) 3:41 - Solving Class Imbalance: Rotation & Synthetic Defects 5:40 - The Brain: Understanding the CNN Architecture 9:24 - Dropout & Adaptive Pooling strategies 11:28 - Training Loop: Backpropagation & Adam Optimizer 13:00 - The Validation Feedback Loop: Fixing the "Glare" bug 14:02 - Final Results: 98% Accuracy Code Stack: Python, PyTorch, OpenCV, Pillow, NumPy. #ComputerVision #MachineLearning #DeepLearning #NeuralNetworks #PyTorch #Python #DataScience #ArtificialIntelligence #Engineering