From Lab to Live: Implementing Open-Source AI Models for Real-Time Unsupervised Anomaly Detection in Images

• ReductStore

  44 137 9.2 Rust

  A time series database for storing and managing large amounts of blob data

  

ReductStore is an innovative time-series database designed specifically for managing Blob data, making it ideal for our needs in real-time unsupervised anomaly detection. The true strength of ReductStore lies in its ability to store not just raw data but also AI labels within the metadata and models at the edge.

• anomalib

  13 3,126 9.2 Python

  An anomaly detection library comprising state-of-the-art algorithms and features such as experiment management, hyper-parameter optimization, and edge inference.

  

Anomalib is an open-source library for unsupervised anomaly detection in images. It offers a collection of state-of-the-art models that can be trained on your specific images.

• WorkOS

  workos.com sponsored

  The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.

  

• Pytorch

  338 77,783 10.0 Python

  Tensors and Dynamic neural networks in Python with strong GPU acceleration

  

Sample config files are available in the repo, and it lets you set the paths of the folders containing your pictures for training and testing. Then, once your model is trained and validated, you can use the inference script to test it under simulated conditions on a single image or a folder of images. For example, with PyTorch, you can run the inference script as follows:

• onnx

  38 16,858 9.5 Python

  Open standard for machine learning interoperability

  

Once your model has been trained and validated using Anomalib, the next step is to prepare it for real-time implementation. This is where ONNX (Open Neural Network Exchange) or OpenVINO (Open Visual Inference and Neural network Optimization) comes into play.

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