Summary of Evaluating Vision Transformer Models For Visual Quality Control in Industrial Manufacturing, by Miriam Alber et al.
Evaluating Vision Transformer Models for Visual Quality Control in Industrial Manufacturing
by Miriam Alber, Christoph Hönes, Patrick Baier
First submitted to arxiv on: 22 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A machine learning-based quality control system can efficiently detect defective products in industrial manufacturing, reducing costs and human errors. Researchers have developed various methods to identify rare defects in unbalanced datasets. These approaches typically consist of a visual backbone and an anomaly detection algorithm. With transformer architectures emerging as popular visual backbones, numerous combinations of these components exist, balancing detection quality and inference time. Practitioners often spend significant time researching the best combination for their use case. Our contribution is to review and evaluate current vision transformer models with anomaly detection methods, focusing on small, fast, and efficient models suitable for industrial manufacturing. We evaluated our results on the MVTecAD and BTAD datasets, providing guidelines for choosing a suitable model architecture considering use-case and hardware constraints. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers developed a new way to detect defective products in factories using machine learning. They looked at how to identify rare problems in a type of data that has many normal examples and only a few abnormal ones. Their approach uses two parts: one that looks at the features of an image and another that decides if those features are unusual. With this method, they found that some combinations work better than others for detecting defects quickly or accurately. The researchers want to help people in industry choose the best combination by reviewing what’s currently available and testing different models on specific data sets. |
Keywords
» Artificial intelligence » Anomaly detection » Inference » Machine learning » Transformer » Vision transformer
