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feature-embedding

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FSE+Attention model for particle identification in ALICE (Pb-Pb Run 3) at CERN. State-of-the-art detector masking with 92.8% accuracy using JAX/Flax. Handles missing detector data through Feature Set Embedding + Multi-head Attention. Production-ready with Focal Loss, class weighting, and two-tier model persistence.

  • Updated Nov 20, 2025
  • Jupyter Notebook

This project builds a deep learning model to detect fake & real Indian currency notes using image data and note-specific features. It includes two training pipelines a base CNN with MobileNetV2 and a two-branch network combining image and feature embeddings. Achieved 99% validation accuracy, proving reliable for real-world currency authentication.

  • Updated Oct 18, 2025
  • Jupyter Notebook

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