Create README.md

README.md

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+---
+license: apache-2.0
+tags:
+- vision
+datasets:
+- imagenet-21k
+inference: false
+---
+
+# Vision Transformer (large-sized model) 
+
+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. 
+
+
+## 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. 
+
+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.
+
+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](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 in PyTorch:
+
+```python
+from transformers import ViTImageProcessor, ViTModel
+from PIL import Image
+import requests
+
+url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
+image = Image.open(requests.get(url, stream=True).raw)
+
+processor = ViTImageProcessor.from_pretrained('kakaobrain/vit-large-patch16-384')
+model = ViTModel.from_pretrained('kakaobrain/vit-large-patch16-384')
+
+inputs = processor(images=image, return_tensors="pt")
+outputs = model(**inputs)
+last_hidden_state = outputs.last_hidden_state
+```
+
+Refer to the [docs](https://huggingface.co/docs/transformers/model_doc/vit) for usage in TensorFlow and JAX/FLAX.
+
+## 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. 
+
+## 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 (384x384) and normalized across the RGB channels with mean (0.5, 0.5, 0.5) and standard deviation (0.5, 0.5, 0.5).
+
+### 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 and Peter Vajda},
+      year={2020},
+      eprint={2006.03677},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV}
+}
+```
+
+```bibtex
+@inproceedings{deng2009imagenet,
+  title={Imagenet: A large-scale hierarchical image database},
+  author={Deng, Jia and Dong, Wei and Socher, Richard and Li, Li-Jia and Li, Kai and Fei-Fei, Li},
+  booktitle={2009 IEEE conference on computer vision and pattern recognition},
+  pages={248--255},
+  year={2009},
+  organization={Ieee}
+}
+```