readme: ---
  license: apache-2.0
  tags:
  - vision
  - image-classification
  datasets:
  - imagenet
  - imagenet-21k
  ---
  
  # Vision Transformer (base-sized model) 
  
  Vision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) at resolution 224x224, and fine-tuned on ImageNet 2012 (1 million images, 1,000 classes) at resolution 384x384. It was introduced in the paper [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://arxiv.org/abs/2010.11929) by Dosovitskiy et al. and first released in [this repository](https://github.com/google-research/vision_transformer). However, the weights were converted from the [timm repository](https://github.com/rwightman/pytorch-image-models) by Ross Wightman, who already converted the weights from JAX to PyTorch. Credits go to him. 
  
  Disclaimer: The team releasing ViT 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 supervised fashion, namely ImageNet-21k, at a resolution of 224x224 pixels. Next, the model was fine-tuned on ImageNet (also referred to as ILSVRC2012), a dataset comprising 1 million images and 1,000 classes, at a higher resolution of 384x384.
  
  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.
  
  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 standard classifier by placing a linear layer on top of the pre-trained encoder. One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image.
  
  ## Intended uses & limitations
  
  You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=google/vit) to look for
  fine-tuned versions on a task that interests you.
  
  ### How to use
  
  Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:
  
  ```python
  from transformers import ViTFeatureExtractor, ViTForImageClassification
  from PIL import Image
  import requests
  url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
  image = Image.open(requests.get(url, stream=True).raw)
  feature_extractor = ViTFeatureExtractor.from_pretrained('google/vit-base-patch16-384')
  model = ViTForImageClassification.from_pretrained('google/vit-base-patch16-384')
  inputs = feature_extractor(images=image, return_tensors="pt")
  outputs = model(**inputs)
  logits = outputs.logits
  # model predicts one of the 1000 ImageNet classes
  predicted_class_idx = logits.argmax(-1).item()
  print("Predicted class:", model.config.id2label[predicted_class_idx])
  ```
  
  Currently, both the feature extractor and model  support PyTorch. Tensorflow and JAX/FLAX are coming soon, and the API of ViTFeatureExtractor might change.
  
  ## Training data
  
  The ViT model was pretrained on [ImageNet-21k](http://www.image-net.org/), a dataset consisting of 14 million images and 21k classes, and fine-tuned on [ImageNet](http://www.image-net.org/challenges/LSVRC/2012/), a dataset consisting of 1 million images and 1k classes. 
  
  ## Training procedure
  
  ### Preprocessing
  
  The exact details of preprocessing of images during training/validation can be found [here](https://github.com/google-research/vision_transformer/blob/master/vit_jax/input_pipeline.py). 
  
  Images are resized/rescaled to the same resolution (224x224 during pre-training, 384x384 during fine-tuning) and normalized across the RGB channels with mean (0.5, 0.5, 0.5) and standard deviation (0.5, 0.5, 0.5).
  
  ### Pretraining
  
  The model was trained on TPUv3 hardware (8 cores). All model variants are trained with a batch size of 4096 and learning rate warmup of 10k steps. For ImageNet, the authors found it beneficial to additionally apply gradient clipping at global norm 1. Pre-training resolution is 224.
  
  ## Evaluation results
  
  For evaluation results on several image classification benchmarks, we refer to tables 2 and 5 of the original paper. Note that for fine-tuning, the best results are obtained with a higher resolution (384x384). Of course, increasing the model size will result in better performance.
  
  ### BibTeX entry and citation info
  
  ```bibtex
  @misc{wu2020visual,
        title={Visual Transformers: Token-based Image Representation and Processing for Computer Vision}, 
        author={Bichen Wu and Chenfeng Xu and Xiaoliang Dai and Alvin Wan and Peizhao Zhang and Zhicheng Yan and Masayoshi Tomizuka and Joseph Gonzalez and Kurt Keutzer 