maryam089/SDViT
Official repository for "Self-Distilled Vision Transformer for Domain Generalization" (ACCV-2022 ORAL)
This project offers an improved way to train Vision Transformer models so they can accurately classify images even when the image style, background, or other features are significantly different from what they were initially trained on. It takes existing image datasets and Vision Transformer models, applies a self-distillation technique, and outputs a more robustly trained model ready for real-world deployment. Scientists, machine learning engineers, and researchers working with image classification in varied environments would use this.
No commits in the last 6 months.
Use this if you need your image classification models to perform reliably on new, unseen image data distributions, without requiring retraining on every new scenario.
Not ideal if your image data distribution is always consistent and doesn't vary much from your training data.
Stars
42
Forks
7
Language
Python
License
MIT
Category
Last pushed
Dec 02, 2022
Commits (30d)
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