iurada/px-ntk-pruning
Official repository of our work "Finding Lottery Tickets in Vision Models via Data-driven Spectral Foresight Pruning" accepted at CVPR 2024
This project helps machine learning engineers and researchers reduce the computational costs and memory demands of deep learning models, especially for computer vision tasks. It takes an existing deep learning model (either untrained or pre-trained) and outputs a much smaller, 'sparse' version that performs almost identically but runs more efficiently. The end-user is typically an ML practitioner focused on deploying efficient vision models.
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Use this if you need to significantly reduce the size and computational requirements of deep learning models for computer vision applications without sacrificing performance.
Not ideal if your primary goal is to improve model accuracy or if you are not working with deep neural networks for vision tasks.
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Python
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Last pushed
Feb 18, 2025
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