A Modular, Configuration-Driven Framework for Knowledge Distillation. Trained models, training logs and configurations are available for ensuring the reproducibility.
PyPI publishes no adoption figure, so there is no growth to derive. Nothing is estimated in its place.
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Adoption history
Adoption history
PyPI publishes no adoption count, so there is no history to chart. That is a fact about the registry, not a gap in ours: the package’s releases and maintenance are measured instead.
A coding-free framework built on PyTorch for reproducible deep learning studies. PyTorch Ecosystem. 🏆26 knowledge distillation methods presented at TPAMI, CVPR, ICLR, ECCV, NeurIPS, ICCV, AAAI, etc are implemented so far. 🎁 Trained models, training logs and configurations are available for ensuring the reproducibiliy and benchmark.
