Visual Anomaly Detection

Production-Ready AI Template for Real-World Deployment

Reduce development time and cost with a ready-to-integrate AI solution for image-based anomaly detection. Optimized for repetitive structures like textile patterns, industrial components, or quality control footage.

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What I offer

I provide a ready-to-deploy AI template designed specifically for image-based anomaly detection in industrial and manufacturing environments. Instead of spending months building everything from scratch, you can start with a tested and documented foundation that follows industry-proven best practices.

My solution is modular, transparent, and easy to extend. Whether you’re validating a prototype or preparing for production deployment. I’ve built and documented every component with real-world integration in mind: from preprocessing and modeling to inference APIs, Deployment to cloud services, and integration in CI/CD Pipelines.

This template is ideal for small and medium-sized businesses that want to accelerate their AI initiatives without becoming locked into complex platforms or investing in large AI teams. You retain full control over the codebase and deployment strategy and if needed, I can support you through workshops, technical coaching, or hands-on integration

This solution is ideal for…

  • Textile and material inspection
  • Surface defect detection
  • Manufacturing quality control
  • Any use case with repetitive visual structures

What’s the challenge?

Modern manufacturing and quality inspection generate massive amounts of visual data and yet most SMEs lack the time and resources to build and deploy custom AI solutions.

❗High Implementation effort

Building a robust AI solution from scratch requires significant time, internal coordination, and cross-functional expertise — often beyond what SMEs can easily allocate.

🧪 Long experimentation phases

Custom AI projects typically involve months of trial-and-error across data cleaning, modeling, evaluation, and deployment — delaying business impact.

🤖 Lack of ML/AI expertise

Most SMEs don’t have in-house data scientists or machine learning engineers, making it hard to get beyond prototypes or integrate AI reliably.

🏗️ Vendor lock-in or over-engineered platforms

Many solutions are either too rigid or too complex — locking you into specific ecosystems or overwhelming your team with features you don’t need.

Modeling

Includes three robust modeling approaches (VAE, PatchCore, DRAEM) tailored for visual anomaly detection in repetitive structures like textiles or surfaces.

Evaluation

Generates both visual and statistical anomaly scores, including pixel-wise error maps and classification-based defect likelihoods.

Deployment

Comes with REST API (FastAPI), and a ready-to-use GitHub Actions Code for seamless integration into production environments.

Cloud Support

Includes a Render-based deployment template, easily transferable to Azure ML or AWS SageMaker. Cloud onboarding can be offered as a service if needed.

Documentation

Step-by-step Jupyter walkthroughs, setup guides, and integration notes for every module — designed for engineering teams, not researchers.

Source Code

All core logic is modular and decoupled from the notebooks. You can directly use and extend the source code in your own pipelines — no notebook dependency.

Visual Insight into the Solution

The following screenshots offer a direct glimpse into the anomaly detection notebook in action — from preprocessing and model output to evaluation and visualization. If you want to get a feel for the structure, clarity, and practical relevance of the solution, this is the best place to start.

For a deeper look, you can explore the demo repository on GitHub, which contains selected notebooks from the full premium series:

🔗 View Demo on GitHub

Inside the Anomaly Detection Notebook Series

This curated notebook series takes you through my hands-on journey in building and evaluating an anomaly detection system for a practical industrial use-case. From early reconstructions with autoencoders to production-grade segmentation with DRAEM.

Each track focuses on a key step in the pipeline, backed by real experiments and visual results. You’ll find all source code, training logic, and evaluation tools for

Track 1 in the demo repository on GitHub.

Track 1: Variational Autoencoders (VAE)

In this track, I explore the use of autoencoders and variational autoencoders (VAEs) for detecting anomalies through image reconstruction. These models learn to compress and rebuild normal images — and fail in areas where anomalies occur. I walk through the theory, training process, and how to interpret reconstruction errors to localize defects.

✅ What You’ll Learn:

  • When VAEs struggle and how to improve them
  • How autoencoders and VAEs work
  • How to train on defect-free textile images
  • How to visualize anomaly maps from pixel-wise reconstruction errors
  • In-depth analyzation of the models performance

Track 2: PatchCore – Feature-Based Anomaly Detection

PatchCore is a powerful anomaly detection method that doesn’t rely on reconstruction. Instead, it extracts deep features from normal images, stores them in a memory bank, and detects anomalies as feature deviations using nearest neighbors.

✅ What You’ll Learn:

  • Why this approach struggles on fine-grained texture anomalies — and how to analyze false negatives
  • How PatchCore works without training: feature extraction, memory banks, and similarity scoring
  • How to use CNNs as feature extractors
  • How to build and query a memory bank efficiently
  • In-depth analyzation of the models performance

Track 3: DRAEM – Pixel-Accurate Anomaly Segmentation

DRAEM is a state-of-the-art anomaly detection framework that goes beyond classification or reconstruction. It’s trained to generate synthetic anomalies and then learn to segment them at the pixel level using a dual-network approach: one for reconstruction and one for anomaly localization.

In this track, I implemented and refined a DRAEM pipeline tailored for high-resolution fabric textures. Unlike VAEs or PatchCore, DRAEM was able to capture even subtle, line-like or point-wise defects with strong performance across ROC and segmentation metrics.

✅ What You’ll Learn:

  • How DRAEM combines synthetic anomaly generation with dual-network training
  • How to build realistic anomaly patterns for training
  • How to train and evaluate segmentation masks from real test images
  • Why DRAEM succeeded where other models failed – especially on complex textures

Track 4: Operationalization – From Notebook to Inference Service

In this track, I transform the trained DRAEM model into a production-ready inference system. It includes a FastAPI-based model server, automated deployments with GitHub Actions and Render, and full experiment tracking with MLflow and Weights & Biases.

The focus is on robust, repeatable ML engineering: from inference scripts to REST APIs, CI/CD pipelines, and structured project packaging — making the solution easy to test, monitor, and scale.

✅ What You’ll Learn:

  • How to serve a PyTorch model with FastAPI for real-time inference
  • How to automate deployments with GitHub Actions + Render
  • How to track experiments and model metrics using MLflow and W&B
  • How to structure ML projects for reproducibility and CI/CD readiness

Technologies Used

Throughout this series, I’ve used tools and frameworks that are widely adopted in modern machine learning workflows: from model training and evaluation to experiment tracking, API deployment, and infrastructure automation. My goal: build solutions that are robust, reproducible, and easy to extend.

Modeling & Data

  • PyTorch, NumPy
  • Matplotlib, OpenCV
  • Scikit-Learn
  • Torchvision

Experiment & Tracking

  • MLflow
  • Weights & Biases (W&B)
  • GitHub (Versioning)

Deployment & Automation

  • FastAPI
  • Render
  • GitHub Actions

✅ Ready to Dive In or Looking for a Faster Path?

This anomaly detection template isn’t just a learning resource. It’s a solid foundation for building your own AI-powered quality control system.

Optional Services

  • Remote Workshop (2h) – deep dive, code walkthrough, best practices (on request)
  • Custom Integration – setup & adaption to your stack (on request)
  • Cloud Deployment – Azure, Render or on-premise support
    (on request)
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