aiaudit-org/raw2logit
In order to control processing-induced dataset drift we require two ingredients: raw sensor data and an image processing model. This code repository contains the materials for the second ingredient, the image processing models, as well as scripts to load data and run experiments. https://arxiv.org/abs/2211.02578
This project helps machine learning practitioners or researchers who work with optical images from cameras or scientific instruments to understand and control 'dataset drift.' It takes raw sensor data and image processing parameters as input to simulate different image processing pipelines. The output helps analyze how these pipelines affect model performance, enabling more robust and reliable machine learning systems.
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Use this if you need to understand how variations in image processing, such as camera settings or instrument calibration, impact the performance of your machine learning models and want to mitigate those risks.
Not ideal if your machine learning models do not use optical images or if you are not concerned with the effects of upstream image processing on your data.
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Python
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MIT
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Last pushed
May 07, 2023
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