Data efficient Image Transformer (tiny sized model) Data efficient Image Transformer (DeiT) model pre trained and fine tuned on ImageNet 1k (1 million images, 1,000 classes) at resolution 224x224. It was first introduced in the paper Training data efficient image transformers & distillation through attention by Touvron et al. and first released in this repository. However, the weights were converted from the timm repository by Ross Wightman. Disclaimer: The team releasing DeiT did not write a model card for this model so this model card has been written by the Hugging Face team. Model description This model is actually a more efficiently trained Vision Transformer (ViT). The Vision Transformer (ViT) is a transformer encoder model (BERT like) pre trained and fine tuned on a large collection of images in a 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 Tra…
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