  Computer vision has long relied on ImageNet and other large datasets of
images sampled from the Internet for pretraining models. However, these
datasets have ethical and technical shortcomings, such as containing personal
information taken without consent, unclear license usage, biases, and, in some
cases, even problematic image content. On the other hand, state-of-the-art
pretraining is nowadays obtained with unsupervised methods, meaning that
labelled datasets such as ImageNet may not be necessary, or perhaps not even
optimal, for model pretraining. We thus propose an unlabelled dataset PASS:
Pictures without humAns for Self-Supervision. PASS only contains images with
CC-BY license and complete attribution metadata, addressing the copyright
issue. Most importantly, it contains no images of people at all, and also
avoids other types of images that are problematic for data protection or
ethics. We show that PASS can be used for pretraining with methods such as
MoCo-v2, SwAV and DINO. In the transfer learning setting, it yields similar
downstream performances to ImageNet pretraining even on tasks that involve
humans, such as human pose estimation. PASS does not make existing datasets
obsolete, as for instance it is insufficient for benchmarking. However, it
shows that model pretraining is often possible while using safer data, and it
also provides the basis for a more robust evaluation of pretraining methods.
