Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/sally20921/NoisyStudent
"Self-training with Noisy Student improves ImageNet classification" pytorch implementation
https://github.com/sally20921/NoisyStudent
docker latex pytorch-ignite pytorch-implementation
Last synced: about 1 month ago
JSON representation
"Self-training with Noisy Student improves ImageNet classification" pytorch implementation
- Host: GitHub
- URL: https://github.com/sally20921/NoisyStudent
- Owner: sally20921
- License: mit
- Created: 2020-06-21T05:50:22.000Z (over 4 years ago)
- Default Branch: main
- Last Pushed: 2024-07-25T10:57:19.000Z (5 months ago)
- Last Synced: 2024-08-04T01:15:04.538Z (5 months ago)
- Topics: docker, latex, pytorch-ignite, pytorch-implementation
- Language: Python
- Homepage:
- Size: 1.16 MB
- Stars: 50
- Watchers: 2
- Forks: 6
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-image-classification - unofficial-pytorch: https://github.com/sally20921/NoisyStudent
README
# Self-training with Noisy Student improves ImageNet classification
Noisy Student Training is a semi-supervised training method which achieves 88.4% top-1 accuracy on ImageNet
and surprising gains on robustness and adversarial benchmarks.
Noisy Student Training is based on the self-training framework and trained with 4-simple steps:1. Train a classifier on labeled data (teacher).
2. Infer labels on a much larger unlabeled dataset.
3. Train a larger classifier on the combined set, adding noise (noisy student).
4. Go to step 2, with student as teacher.