Below we share, in reverse chronological order, the updates and new releases in VISSL. All VISSL releases are available here. VISSL is a computer VIsion library for state-of-the-art Self-Supervised Learning research with PyTorch. VISSL aims to accelerate research cycle in self-supervised learning: from designing a new self-supervised task to evaluating the learned representations. Benchmark suite: Variety of benchmarks tasks including linear image classification (places205, imagenet1k, voc07, food, CLEVR, dsprites, UCF101, stanford cars and many more), full finetuning, semi-supervised benchmark, nearest neighbor benchmark, object detection (Pascal VOC and COCO).

At a time when many versions of AI rely on pre-established data sets for image recognition, Facebook has developed SEER (Self-supERvised) – a deep learning solution able to register images on the Internet independent of curated and labeled data sets. With major advances already underway in natural language processing (NLP) including machine translation, natural language interference and question answering, SEER uses an innovative billion-parameter, self-supervised computer vision model able to learn from any online image. Thus far, the Facebook AI team has tested SEER on one billion uncurated and unlabeled public Instagram images. The new program performed better than the most advanced self-supervised systems as well as self-supervised models on downstream tasks such as low-shot, object detection, image detection and segmentation. In fact, exposure to only 10 percent of the ImageNet data set still resulted in a 77.9 percent recognition rate by SEER.