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SUMMARY:DEEP Inspection for Materials Science - Classification
DESCRIPTION:Register latest by: 11 September 2026\nAbout the event\nInspection and characterization of materials are fundamental for understanding material properties\, ensuring quality control\, and accelerating materials development and advanced manufacturing. However\, conventional approaches often require extensive expert analysis of complex images\, making them time-consuming and difficult to scale. With the rapid growth of high-resolution imaging techniques\, deep learning-based methods provide powerful solutions for automated materials image analysis by learning meaningful visual features for tasks such as defect classification\, microstructure recognition\, process monitoring\, and intelligent quality assessment. \nThis workshop offers a focused\, hands-on introduction to deep learning for visual inspection in materials science. Using the Severstal Steel Defect Detection dataset as a concrete\, running example\, participants will follow a complete analysis pipeline\, from raw image data through model training to interpretable results. The techniques covered are deliberately chosen for their breadth of applicability: while the examples are grounded in industrial quality control\, the same approaches translate directly to defect detection in electron microscopy\, anomaly identification in medical imaging\, and structural characterization across a wide range of domains. \nOver three half-day sessions\, participants will build solid intuition for how modern neural networks process and learn from image data\, implement deep neural network classifiers from scratch\, apply transfer learning to leverage pre-trained models for materials-specific tasks\, and explore state-of-the-art architectures (including convolutional networks and transformer-based approaches) used across both research and industry. \nWe encourage participants to follow up in our upcoming workshop session on Sept. 30th to dive deeper into object detection and instance segmentation. \nWho is this course for?\n\n\nEngineers\, analysts\, academic researchers\, and students in materials science or manufacturing who want to apply AI-based image analysis to quality control and defect inspection \n\n\nAnyone with a general interest in computer vision and deep learning\, regardless of domain \n\n\nKey takeaways for participants\n\n\nUnderstand the core concepts behind deep neural networks (including CNNs) and transfer learning and know when and why to use them for image-based inspection tasks \n\n\nGain hands-on experience building and training defect classifiers — from a simple binary classifier to multi-class CNNs \n\n\nLearn how to extract and visualize feature embeddings to understand what your model has learned \n\n\nKnow how to leverage pretrained models (VGG\, ResNet\, ViT) through transfer learning\, even with limited amount of data \n\n\nPrerequisites\n\n\nBasic Python programming (loops\, functions\, libraries) \n\n\nFamiliarity with NumPy or similar data manipulation tools is helpful but not required \n\n\nNo prior deep learning experience necessary – core concepts will be introduced from scratch \n\n\nSchedule\n\n\n\nDay\nTime\nContents\n\n\n\n\n22 September 2026\n09:00 – 12:00\nAnomaly Detection & Feature Extraction\nIntroduction to industrial inspection and defect classification. Build a binary defect classifier\, explore learned embeddings\, and visualize feature clusters\n\n\n23 September 2026\n09:00 – 12:00\nCNN Fundamentals & Multi-class Classification\nDive into convolutional neural networks — convolutions\, pooling\, and the LeNet architecture. Apply training best practices\n\n\n24 September 2026\n09:00 – 12:00\nTransfer Learning & Advanced Vision Architectures Explore pretrained deep networks (VGG\, ResNet) and Vision Transformers (ViT). Fine-tune models on defect data and compare performance across architectures.\n\n\n\n\nInstructors and Teaching Assistants\n\n\nBenedikt Neyses (Mimer AIF / RISE) \n\n\nAndreas Thore (Mimer AIF / RISE) \n\n\nMarzieh Saeedimasine (MIMER AIF/NAISS) \n\n\nRuiwen Xie (MIMER AIF/NAISS) \n\n\nSmita Chakraborty (Mimer AIF / RISE) \n\n\nYuvarajendra Anjaneya Reddy (Mimer AIF / RISE) \n\n\nYonglei Wang (MIMER AIF/NAISS)
URL:https://mimer-ai.eu/event/deep-inspection-for-materials-science-classification/
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