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--- |
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license: apache-2.0 |
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tags: |
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- vision |
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datasets: |
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- imagenet-21k |
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inference: false |
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--- |
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# Vision Transformer (large-sized model) |
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Vision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 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, this model is contributed by the Kakao Brain team and the weights were converted from their TensorFlow implementation to PyTorch by Alara Dirik. |
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## Model description |
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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. |
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Images are presented to the model as a sequence of fixed-size patches (resolution 32x32), 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. |
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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). |
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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. |
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## Intended uses & limitations |
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You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=google/vit) to look for |
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fine-tuned versions on a task that interests you. |
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### How to use |
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Here is how to use this model in PyTorch: |
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```python |
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from transformers import ViTImageProcessor, ViTModel |
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from PIL import Image |
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import requests |
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url = 'http://images.cocodataset.org/val2017/000000039769.jpg' |
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image = Image.open(requests.get(url, stream=True).raw) |
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processor = ViTImageProcessor.from_pretrained('kakaobrain/vit-large-patch16-384') |
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model = ViTModel.from_pretrained('kakaobrain/vit-large-patch16-384') |
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inputs = processor(images=image, return_tensors="pt") |
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outputs = model(**inputs) |
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last_hidden_state = outputs.last_hidden_state |
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``` |
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Refer to the [docs](https://huggingface.co/docs/transformers/model_doc/vit) for usage in TensorFlow and JAX/FLAX. |
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## Training data |
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The ViT model was pretrained on [ImageNet-21k](http://www.image-net.org/), a dataset consisting of 14 million images and 21k classes. |
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## Training procedure |
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### Preprocessing |
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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). |
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Images are resized/rescaled to the same resolution (384x384) and normalized across the RGB channels with mean (0.5, 0.5, 0.5) and standard deviation (0.5, 0.5, 0.5). |
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### BibTeX entry and citation info |
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```bibtex |
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@misc{wu2020visual, |
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title={Visual Transformers: Token-based Image Representation and Processing for Computer Vision}, |
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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}, |
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year={2020}, |
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eprint={2006.03677}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CV} |
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} |
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``` |
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```bibtex |
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@inproceedings{deng2009imagenet, |
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title={Imagenet: A large-scale hierarchical image database}, |
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author={Deng, Jia and Dong, Wei and Socher, Richard and Li, Li-Jia and Li, Kai and Fei-Fei, Li}, |
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booktitle={2009 IEEE conference on computer vision and pattern recognition}, |
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pages={248--255}, |
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year={2009}, |
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organization={Ieee} |
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} |
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``` |
