Vision Transformer (huge sized model) pre trained with MAE Vision Transformer (ViT) model pre trained using the MAE method. It was introduced in the paper Masked Autoencoders Are Scalable Vision Learners by Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, Ross Girshick and first released in this repository. Disclaimer: The team releasing MAE did not write a model card for this model so this model card has been written by the Hugging Face team. Model description The Vision Transformer (ViT) is a transformer encoder model (BERT like). Images are presented to the model as a sequence of fixed size patches. During pre training, one randomly masks out a high portion (75%) of the image patches. First, the encoder is used to encode the visual patches. Next, a learnable (shared) mask token is added at the positions of the masked patches. The decoder takes the encoded visual patches and mask tokens as input and reconstructs raw pixel values for the masked positions. By pre training the model, it learns an inner representation of images that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled images for instance, you can train a st…
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