yoshitomo-matsubara/torchdistill
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.
Health Breakdown
Should you contribute to yoshitomo-matsubara/torchdistill?
yoshitomo-matsubara/torchdistill has a FoundDev health score of 89/100, which puts it in the active-and-maintained tier. The maintainer team is shipping recently, issues are being closed, and a PR you open this week has a realistic chance of being reviewed.
Last push was 0 days ago — that signals an actively maintained project. New issues are likely to get a maintainer response within days. The project is written primarily in Python, so prior Python experience will shorten ramp-up.
Licensed under MIT, a standard OSI-approved license — safe to contribute to under normal employer IP policies.
Community
