• SEER: a self-supervised neural network with a billion parameters from FAIR

    SEER is FAIR’s self-supervised billion-parameter neural network for computer vision applications. The model pre-trained on the Instagram pictures can be further trained on your tasks. The developers have published the VISSL library for training the SEER model.

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    More about model architecture

    SEER combines RegNet architecture and online self-supervised learning format. SwAV was used as an algorithm for online learning. RegNet, in turn, is a scalable convolutional neural network that circumvents training and memory constraints. This combination allows SEER to scale to billions of parameter and training images.

    Testing SEER performance

    After pre-training on a billion random, untagged Instagram images, SEER has bypassed most state-of-the-art self-supervised models. According to the results of the experiments, the maximum prediction accuracy of the neural network was 84.2% on the ImageNet dataset.

    SEER has also bypassed state-of-the-art supervised learning approaches on tasks such as low-shot, object detection, image segmentation and classification.s

    Using 10% of data from ImageNet to train, the maximum SEER accuracy is 77.9% for the entire ImageNet. If you train the neural network on 1% of labeled images from ImageNet, the accuracy will be 60.5%.

    The SEER results show that the self-supervised learning format is also suitable for computer vision tasks.

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