BigTransfer (also known as BiT) is a state-of-the-art transfer learning method for image classification. Transfer of pre-trained representations improves sample efficiency and simplifies hyperparameter tuning when training deep neural networks for vision. BiT revisit the paradigm of pre-training on large supervised datasets and fine-tuning the model on a target task. The importance of appropriately choosing normalization layers and scaling the architecture capacity as the amount of pre-training data increases.