TaesikGong/NOTE
The official PyTorch Implementation of "NOTE: Robust Continual Test-time Adaptation Against Temporal Correlation (NeurIPS '22)"
This project helps machine learning researchers evaluate their vision models when the real-world data streams they encounter are continuously changing. You input a pre-trained image classification model and a corrupted image dataset, and it outputs updated model performance metrics, showing how well the model adapts to new, unseen corruptions over time. It is designed for researchers or practitioners developing and assessing robust computer vision systems.
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Use this if you need to rigorously test how well your image classification models adapt to evolving data conditions and corruptions in a continuous real-time scenario.
Not ideal if you are looking for a general-purpose image classification tool or a solution for static datasets without temporal shifts.
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50
Forks
7
Language
Python
License
MIT
Category
Last pushed
Dec 21, 2023
Commits (30d)
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