megvii-research/mdistiller
The official implementation of [CVPR2022] Decoupled Knowledge Distillation https://arxiv.org/abs/2203.08679 and [ICCV2023] DOT: A Distillation-Oriented Trainer https://openaccess.thecvf.com/content/ICCV2023/papers/Zhao_DOT_A_Distillation-Oriented_Trainer_ICCV_2023_paper.pdf
This is a tool for machine learning practitioners to make their large, accurate computer vision models (teachers) teach smaller, faster models (students) to perform nearly as well. You provide your existing teacher model and a student model architecture, and it outputs a highly optimized student model. It's designed for machine learning engineers and researchers working on deploying efficient computer vision solutions.
894 stars. No commits in the last 6 months.
Use this if you need to deploy high-performing computer vision models on resource-constrained devices or in latency-sensitive applications.
Not ideal if you are looking for an off-the-shelf, no-code solution for general image classification or object detection, as it requires expertise in model training and architecture.
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Last pushed
Nov 05, 2023
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