ismail31416/LumiNet
The official (TMLR) implementation of LumiNet: Perception-Driven Knowledge Distillation via Statistical Logit Calibration
This project helps machine learning engineers create smaller, more efficient deep learning models (student models) that retain much of the accuracy of larger, more complex models (teacher models). It takes a large, pre-trained image classification model and a smaller, target model architecture as input. The output is a highly optimized, smaller model capable of performing image classification with improved accuracy compared to standard methods. It is primarily for deep learning practitioners working on computer vision tasks.
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Use this if you need to deploy high-performing image classification models to resource-constrained environments by making them smaller and faster without significant accuracy loss.
Not ideal if you are not working with image classification tasks or if you do not have existing large 'teacher' models to distill knowledge from.
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Aug 17, 2025
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