[Info Need]: Image Anomaly Detection in TensorFlow 2 or Keras?

I’ve been working on anomaly detection problems on industrial products. Most of the samples are images (a few are audio data and others). As we’re focusing on an engineering solution, we need a reliable toolbox or library initially.

I’ve found this, Anomalib, an amazing library and best suited for this task. It’s in PyTorch and provides state of an art approach to address this task. I was wondering if there is any well-known library in TensorFlow 2 or Keras?

Some of the models are

  • Patch-Core
  • FastFlow
  • PaDim

Note, that there might be one or two open-source implementations in tf/keras. But I am looking for something reliable and supported, so it can give me an opportunity to continue my work with them.

[Update: 28 April, 2022]: Added Resources: GitHub - yzhao062/anomaly-detection-resources: Anomaly detection related books, papers, videos, and toolboxes

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I think that it could be interesting to extract/analyze if there are some missing components to compose these networks in Keras-cv and then add some models.

It seems that the main reference public dataset in the repository is MVTec.

Here we could find a partial list of papers, metrics and the reference implementations on this dataset:

Also, it could be also nice to contribute an adapter to this dataset, or any popular dataset in this specific domain, at:


MVTech is the first choice to benchmark. Here is another one, BeanTech : BTAD Dataset | Papers With Code

Yes a partial list of anomaly detection benchmarks with the related papers and reference implementations is available at:

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Would be great also to contribute these models to TensorFlow Hub if possible
With that, it would make very easy for other users to transfer learning and fine-tune them

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It could be nice if the TFHub team could coordinate more explicetly with the new repositories where we are collecting reusable components API to compose models.

I’ve mentioned directly TFHub at:

So I hope you could have an internal follow-up between teams.

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Will look into that! thanks for bringing it up!

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