

MSN (masked siamese networks) is a method for self-supervised pre-training of Vision Transformers (ViTs). The pre-training
objective is to match the prototypes assigned to the unmasked views of the images to that of the masked views of the same images.
The authors have only released pre-trained weights of the backbone (ImageNet-1k pre-training). So, to use that on your own image classification dataset,
use the [ViTMSNForImageClassification] class which is initialized from [ViTMSNModel]. Follow
this notebook for a detailed tutorial on fine-tuning.
MSN is particularly useful in the low-shot and extreme low-shot regimes. Notably, it achieves 75.7% top-1 accuracy with only 1% of ImageNet-1K
labels when fine-tuned.