BiT stands for Big Transfer and was introduced in Big Transfer (BiT): General Visual Representation Learning. BiT variants of ResNets use Group Normalization (Wu et al.) and Weight Standardization (Qiao et al.) in place of Batch Normalization (Ioffe et al.). In order to limit the time it takes to run this example, we will be using a BiT ResNet101x3 already trained on the Flowers102 dataset. You can refer to this notebook to learn more about the training process. This model reaches 98.18% accuracy on the test set of Flowers102.