mingkai-zheng/ReSSL
ReSSL: Relational Self-Supervised Learning with Weak Augmentation
This project provides pre-trained models and code for self-supervised learning on image datasets like ImageNet, CIFAR10, and STL10. It takes unlabeled image collections as input and produces image classification models that can perform well even with minimal labeled data. Machine learning researchers and practitioners focused on computer vision would use this to build robust image recognition systems.
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Use this if you are a machine learning researcher or engineer working with image data and want to leverage self-supervised learning to reduce your reliance on large, expensively labeled datasets for image classification.
Not ideal if you are looking for an off-the-shelf application to categorize images without any machine learning development expertise.
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58
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6
Language
Python
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Last pushed
Dec 01, 2021
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