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Recently, self-supervised learning methods like MoCo, SimCLR, BYOL and SwAVhave reduced the gap with supervised methods. These results have been achievedin a control environment, that is the highly curated ImageNet dataset. However,the premise of self-supervised learning is that it can learn from any randomimage and from any unbounded dataset. In this work, we explore ifself-supervision lives to its expectation by training large models on random,uncurated images with no supervision. Our final SElf-supERvised (SEER) model, aRegNetY with 1.3B parameters trained on 1B random images with 512 GPUs achieves84.2% top-1 accuracy, surpassing the best self-supervised pretrained model by1% and confirming that self-supervised learning works in a real world setting.Interestingly, we also observe that self-supervised models are good few-shotlearners achieving 77.9% top-1 with access to only 10% of ImageNet. Code:https://github.com/facebookresearch/vissl

Self-supervised Pretraining of Visual Features in the Wild