SLAMPAI/large-scale-pretraining-transfer
Code for reproducing the experiments on large-scale pre-training and transfer learning for the paper "Effect of large-scale pre-training on full and few-shot transfer learning for natural and medical images" (https://arxiv.org/abs/2106.00116)
This project helps medical and natural image researchers apply advanced machine learning to new datasets more efficiently. It takes publicly available large-scale image datasets, pre-trains powerful models on them, and then lets you quickly fine-tune these models on your specific image classification tasks. Researchers can use this to build highly accurate image classifiers for medical conditions or other subjects with less effort and data.
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Use this if you need to build high-performing image classification models for natural or medical images, especially when you have limited data for your specific task.
Not ideal if you are not comfortable with command-line operations and dataset preparation, or if your image analysis task is not classification.
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Language
Jupyter Notebook
License
MIT
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Last pushed
May 29, 2022
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