filipbasara0/relic
A simple PyTorch implementation of the Representation Learning via Invariant Causal Mechanisms self-supervised contrastive learning paper
This tool helps machine learning engineers pre-train image classification models efficiently without needing large labeled datasets. It takes an unlabeled collection of images and outputs a trained model encoder that can extract meaningful features from new images. This is ideal for ML practitioners and researchers looking to quickly build robust computer vision models.
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Use this if you need to train a high-performing image feature extractor with limited or no labeled image data.
Not ideal if you already have a fully labeled dataset and want to train a model from scratch using traditional supervised learning.
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12
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5
Language
Jupyter Notebook
License
MIT
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Last pushed
Apr 07, 2024
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