YannDubs/Invariant-Self-Supervised-Learning
Pytorch code for "Improving Self-Supervised Learning by Characterizing Idealized Representations"
This project provides pre-trained models and code for improving computer vision tasks, particularly image classification. It takes unlabeled image datasets as input and produces optimized image representations that can then be used for tasks like identifying objects in new images with higher accuracy. This is designed for machine learning engineers and researchers who are building and fine-tuning image recognition systems.
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Use this if you need to develop highly accurate image classification models and want to leverage cutting-edge self-supervised learning techniques to improve performance on your specific image data.
Not ideal if you are a non-technical user looking for an out-of-the-box image classification application without coding.
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42
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7
Language
Python
License
MIT
Category
Last pushed
Nov 27, 2022
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