🛠️ Corrected Test Sets for ImageNet, MNIST, CIFAR, Caltech-256, QuickDraw, IMDB, Amazon Reviews, 20News, and AudioSet
Home Page: https://labelerrors.com/
License: GNU General Public License v3.0
Hello and thanks for a very interesting repo! The top-level README provides helpful step-by-step instructions for obtaining the data corrected in this repo.
For MNIST, the instructions include two variables with assignments that share the same right-hand side:
test_data = datasets.MNIST(data_dir, train=False, download=True).test_labels.numpy()
test_labels = datasets.MNIST(data_dir, train=False, download=True).test_labels.numpy()
They're the same in Python:
In [6]: np.all(np.equal(test_data, test_labels))
Out[6]: True
It looks like the test_labels on the right-hand side should be test_data for the first assignment.
(There are warnings from torchvision 0.13.0 about the names changing, but whichever torchvision version is supported by the step-by-step tutorial in the README, it would help to be consistent.)
The two assignments for training data appear to have a similar problem:
bash$ sed 's!.*=!!' | while read rhs; do echo $rhs | openssl sha256; done
train_data = datasets.MNIST(data_dir, train=True, download=True).test_data.numpy()
train_labels = datasets.MNIST(data_dir, train=True, download=True).test_data.numpy()
870562877997826fd9627b9eb3890323171ea41841499caec4c8ea1ccddfeea4
870562877997826fd9627b9eb3890323171ea41841499caec4c8ea1ccddfeea4
bash$
Where can i find the cleaned and corrected test sets.
Hi,
Are the cleaned training sets (from the Confident Learning paper in JAIR) and models that were trained on these datasets (cleaned training sets, cleaned validation sets) also available? Thanks.
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