dbolya/parc
A benchmark suite for Scalable Diverse Model Selection for Accessible Transfer Learning from our NeurIPS 2021 paper.
This tool helps machine learning practitioners evaluate how well different pre-trained models can be adapted to new, specific image recognition tasks. It takes a collection of diverse pre-trained image models and a set of "probe" datasets, then provides a score indicating each model's suitability for transfer learning. It's designed for researchers and engineers who need to select the most effective pre-trained models for their particular computer vision applications without extensive trial and error.
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Use this if you need a systematic way to compare and select pre-trained image models for transfer learning to ensure they are well-suited for your specific downstream tasks.
Not ideal if you are looking for a simple, out-of-the-box solution to fine-tune a single pre-trained model for a new task without needing to compare multiple options.
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Python
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Last pushed
Dec 14, 2022
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