The VGGFace2 dataset is made of around 3.31 million images divided into 9131 classes, each representing a different person identity. The dataset is divided into two splits, one for the training and one for test. The latter contains around 170000 images divided into 500 identities while all the other images belong to the remaining 8631 classes available for training. While constructing the datasets, the authors focused their efforts on reaching a very low label noise and a high pose and age diversity thus, making the VGGFace2 dataset a suitable choice to train state-of-the-art deep learning models on face-related tasks. The images of the training set have an average resolution of 137x180 pixels, with less than 1% at a resolution below 32 pixels (considering the shortest side).

CAUTION: Authors note that the distribution of identities in the VGG-Face dataset may not be representative of the global human population. Please be careful of unintended societal, gender, racial and other biases when training or deploying models trained on this data.

Source: Cross-Resolution Learning for Face Recognition


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