In the Download page, we provide the research community with:
A download link to get access to noisy labels in CIFAR-N;
A starter code (in Pytorch) to train CIFAR-N with CE loss.
In the Observations page, we share our major observations on CIFAR-N, the real-world human annotated noisy labels.
We welcome researchers to share their method performances on CIFAR-N, and contribute to an abundant leaderboard.
Researchers who contribute to CIFAR-N or the leaderboard.
CIFAR-10N and CIFAR-100N provide CIFAR-10 and CIFAR-100 train images with human annotated noisy labels obtained from Amazon Mechanical Turk.
3 independent workers for each train image of CIFAR-10, 1 worker for each train image of CIFAR-100.
We provide 5 noisy label sets for CIFAR-10 train images, named as:
Each image contains a coarse label and a fine label given by a human annotator, named as:
| CIFAR-10N Random 1
||CIFAR-10N Random 2||CIFAR-10N Random 3||CIFAR-10N Worst||CIFAR-100N Coarse||CIFAR-100N Fine|