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Anomaly-Detection2

This is a research repository for anomaly-detection in agriculture. The different python scripts are named after the timeframe of the underlying field test.

Setup

Install Dependencies

Recommended: Use UV as a Package Manager (https://docs.astral.sh/uv/configuration/installer/)

Then Run following command in project root.

Linux and Mac

Run:

uv sync

If you want to use the elastic-ai.Explorer-Pico-Generator run:

uv sync --extra pico-generator

Windows

Run:

uv sync

Converting Models on Windows

If you want to use the elastic-ai.Explorer.Pico-Generator.TfliteModelTranslator use the docker_run_torch_to_tflite.py. Specify the model you want to convert and the output directory. Then just start the script.

Anomaly-Detection2 Package

The Anomaly-Detection2 package gives useful classes and functions to work with timeseries data and anomaly detection.
It contains data loading, processing, deep neural networks architectures, training, tests and more.