BEiT (base sized model, fine tuned on ImageNet 22k) BEiT model pre trained in a self supervised fashion on ImageNet 22k also called ImageNet 21k (14 million images, 21,841 classes) at resolution 224x224, and fine tuned on the same dataset at resolution 224x224. It was introduced in the paper BEIT: BERT Pre Training of Image Transformers by Hangbo Bao, Li Dong and Furu Wei and first released in this repository. Disclaimer: The team releasing BEiT did not write a model card for this model so this model card has been written by the Hugging Face team. Model description The BEiT model is a Vision Transformer (ViT), which is a transformer encoder model (BERT like). In contrast to the original ViT model, BEiT is pretrained on a large collection of images in a self supervised fashion, namely ImageNet 21k, at a resolution of 224x224 pixels. The pre training objective for the model is to predict visual tokens from the encoder of OpenAI's DALL E's VQ VAE, based on masked patches. Next, the model was fine tuned in a supervised fashion on ImageNet (also referred to as ILSVRC2012), a dataset comprising 1 million images and 1,000 classes, also at resolution 224x224. Images are presented to the mo…
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