Vision Transformer (base-sized model)

Vision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) at resolution 224×224. It was introduced in the paper An Image is Worth 16×16 Words: Transformers for Image Recognition at Scale by Dosovitskiy et al. and first released in this repository. However, the weights were converted from the timm repository 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 224×224 pixels.
Images are presented to the model as a sequence of fixed-size patches (resolution 16×16), 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 provide any fine-tuned heads, as these were zero’d by Google researchers. However, the model does include the pre-trained pooler, which can be used for downstream tasks (such as image classification).
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 to look for
fine-tuned versions on a task that interests you.

How to use

Here is how to use this model in PyTorch:
from transformers import ViTImageProcessor, ViTModel<br /> from PIL import Image<br /> import requests<br /> url = '/wp-content/uploads/2023/05/http://openi.cn/wp-content/uploads/2023/05/20230526095402-647081ba87f76.jpg'<br /> image = Image.open(requests.get(url, stream=True).raw)<br /> processor = ViTImageProcessor.from_pretrained('google/vit-base-patch16-224-in21k')<br /> model = ViTModel.from_pretrained('google/vit-base-patch16-224-in21k')<br /> inputs = processor(images=image, return_tensors="pt")<br /> outputs = model(**inputs)<br /> last_hidden_states = outputs.last_hidden_state<br />

Here is how to use this model in JAX/Flax:
from transformers import ViTImageProcessor, FlaxViTModel<br /> from PIL import Image<br /> import requests<br /> url = '/wp-content/uploads/2023/05/http://openi.cn/wp-content/uploads/2023/05/20230526095402-647081ba87f76.jpg'<br /> image = Image.open(requests.get(url, stream=True).raw)<br /> processor = ViTImageProcessor.from_pretrained('google/vit-base-patch16-224-in21k')<br /> model = FlaxViTModel.from_pretrained('google/vit-base-patch16-224-in21k')<br /> inputs = processor(images=image, return_tensors="np")<br /> outputs = model(**inputs)<br /> last_hidden_states = outputs.last_hidden_state<br />

Training data

The ViT model was pretrained on ImageNet-21k, a dataset consisting of 14 million images and 21k classes.

Training procedure

Preprocessing

The exact details of preprocessing of images during training/validation can be found here.
Images are resized/rescaled to the same resolution (224×224) 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 (384×384). Of course, increasing the model size will result in better performance.

BibTeX entry and citation info

@misc{wu2020visual,<br /> title={Visual Transformers: Token-based Image Representation and Processing for Computer Vision},<br /> 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 and Peter Vajda},<br /> year={2020},<br /> eprint={2006.03677},<br /> archivePrefix={arXiv},<br /> primaryClass={cs.CV}<br /> }<br />

@inproceedings{deng2009imagenet,<br /> title={Imagenet: A large-scale hierarchical image database},<br /> author={Deng, Jia and Dong, Wei and Socher, Richard and Li, Li-Jia and Li, Kai and Fei-Fei, Li},<br /> booktitle={2009 IEEE conference on computer vision and pattern recognition},<br /> pages={248--255},<br /> year={2009},<br /> organization={Ieee}<br /> }<br />