Facebook AI learned object recognition from 1 billion Instagram pics

Facebook announced on Thursday that it had built an artificial intelligence program that can "see" what it is looking at.

Dubbed SEER (SElf-SupERvised), the model was fed one billion publicly available Instagram images, which had not previously been manually curated. But even without the labels and annotations that typically go into algorithm training, SEER was able to autonomously work its way through the dataset, learning as it was going, and eventually achieving top levels of accuracy on tasks such as object detection.

After learning from these images, Seer correctly identified and categorized the dominant object in photos with an accuracy rate of 84.2%. Seer outperformed the best existing self-supervised systems by one percentage point.

In developing SEER, Facebook took advantage of an algorithm called SwAV, which was borne out of the company's investigations into self-supervised learning. SwAV uses a technique called clustering to rapidly group images from similar visual concepts and leverage their similarities, improving over the previous state-of-the-art in self-supervised learning while requiring up to 6 times less training time.

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