MKYucel/hybrid_augment
[ICCV 2023] HybridAugment++: Unified Frequency Spectra Perturbations for Model Robustness
This project offers methods for training image recognition models to be more robust. It takes your existing image datasets and applies specialized frequency-based modifications during training. The outcome is a more reliable image classification model that performs well even when encountering slightly altered or corrupted images. This is for machine learning engineers and researchers who train computer vision models.
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Use this if you need to train image recognition models that maintain high accuracy on standard images while also being resilient to common real-world corruptions, noise, or minor adversarial attacks.
Not ideal if you are looking for a pre-trained, plug-and-play image recognition solution without needing to train custom models or if you are not working with image-based deep learning.
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Language
Python
License
MIT
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Last pushed
Sep 28, 2023
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