You can use the raw model for image classification. Alternatively, one can mean-pool the final hidden states of the patch embeddings, and place a linear layer on top of that. One typically places a linear layer on top of the token, as the last hidden state of this token can be seen as a representation of an entire image. Contrary to the original ViT models, BEiT models do use relative position embeddings (similar to T5) instead of absolute position embeddings, and perform classification of images by mean-pooling the final hidden states of the patches, instead of placing a linear layer on top of the final hidden state of the token.īy 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 standard classifier by placing a linear layer on top of the pre-trained encoder. Images are presented to the model as a sequence of fixed-size patches (resolution 16x16), which are linearly embedded. 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. 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. 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 BEiT model is a Vision Transformer (ViT), which is a transformer encoder model (BERT-like). 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.ĭisclaimer: 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. BEiT (large-sized model, fine-tuned on ImageNet-1k)īEiT model pre-trained in a self-supervised fashion on ImageNet-21k (14 million images, 21,841 classes) at resolution 224x224, and fine-tuned on ImageNet 2012 (1 million images, 1,000 classes) at resolution 512x512.

Negligee patch resolution