Pipeline training and inference Anomalib models UI in Anomaly Detection

Major Project - Industry 4.0 tinyML Anomaly Detection and prevention. Using embedded systems and machine learning algorithm. Effective for machines used in manufacturing plants.

Anomalous sound detection (ASD) is the task of identifying whether the sound emitted from a machine is normal or anomalous. This is important in industries such as manufacturing, where early detection of anomalies can prevent disruptions to the production line and reduce maintenance costs.

If you can find the pattern for expected or "normal" data, then you can also find those data points that don't fit the pattern. Companies in industries as diverse as financial services, healthcare, retail and manufacturing regularly employ a variety of data science methods to identify anomalies in their data for uses such as fraud detection, custom

Deep learning is a part of machine learning and is well applied in many areas with messy and unstructured data. The inspection in industries manufacturing is an essential requirement because outlier or anomaly can be detected on product manufacturing. Done in the efficient way, that mean by introducing the machine learning and deep learning in the process of anomaly detection, the productivity and the quality of the product can be improved. In this project we performed therefore the Convolutional autoencoder and the semantic segmentation methods like U-Net and FCN and we compared them with each other to see which one is the most performantsuch as pictures. On this project, we applied

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