Vision Transformer (base sized model, patch size 16) trained using DINO Vision Transformer (ViT) model trained using the DINO method. It was introduced in the paper Emerging Properties in Self Supervised Vision Transformers by Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jégou, Julien Mairal, Piotr Bojanowski, Armand Joulin and first released in this repository. Disclaimer: The team releasing DINO 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) pretrained on a large collection of images in a self supervised fashion, namely ImageNet 1k, at a resolution of 224x224 pixels. Images are presented to the model as a sequence of fixed size patches (resolution 16x16), which are linearly embedded. One also adds a [CLS] token to the beginning of a sequence to use it for classification tasks. One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder. Note that this model does not include any fine tuned heads. By pre training the model, it learns an inner representation of images that can then b…
We use cookies for essential functionality and analytics. You can accept or reject analytics cookies.Cookie policy