@Machine_learn
MNIST reborn, restored and expanded.
Now with an extra 50,000 training samples.
If you used the original #MNIST test set more than a few times, chances are your models #overfit the test set. Time to test them on those extra samples.
Now you will use #QMNIST instead of #MNIST
Detailed explanation at #paper: 👇
https://arxiv.org/pdf/1905.10498.pdf
and it's #implementation and some results by using #pytorch: 👇
https://github.com/facebookresearch/qmnist
MNIST reborn, restored and expanded.
Now with an extra 50,000 training samples.
If you used the original #MNIST test set more than a few times, chances are your models #overfit the test set. Time to test them on those extra samples.
Now you will use #QMNIST instead of #MNIST
Detailed explanation at #paper: 👇
https://arxiv.org/pdf/1905.10498.pdf
and it's #implementation and some results by using #pytorch: 👇
https://github.com/facebookresearch/qmnist
GitHub
GitHub - facebookresearch/qmnist: The QMNIST dataset
The QMNIST dataset. Contribute to facebookresearch/qmnist development by creating an account on GitHub.