google-research/compressive-visual-representations
Tensorflow 2 implementations of the C-SimCLR and C-BYOL self-supervised visual representation methods from "Compressive Visual Representations" (NeurIPS 2021)
This project offers an improved way to train computer vision models using unlabelled images. It takes raw image datasets and outputs highly accurate, robust visual representation models that perform similarly to those trained with extensive manual labeling. This is ideal for machine learning researchers and practitioners who need to develop high-performing computer vision systems without the costly and time-consuming process of data annotation.
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Use this if you need to train image classification models that are accurate and robust, but you have limited or no labeled data for training.
Not ideal if you are looking for a plug-and-play solution for general image tasks without any machine learning development expertise.
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37
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5
Language
Python
License
Apache-2.0
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Last pushed
Jan 18, 2022
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