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README.md

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+---
+license: apache-2.0
+tags:
+datasets:
+- imagenet-1k
+---
+
+# Vision Transformer (base-sized model, patch size 16) trained using DINO 
+
+Vision Transformer (ViT) model trained using the DINO method. It was introduced in the paper [Emerging Properties in Self-Supervised Vision Transformers](https://arxiv.org/abs/2010.11929) by Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jégou, Julien Mairal, Piotr Bojanowski, Armand Joulin and first released in [this repository](https://github.com/facebookresearch/dino). 
+
+Disclaimer: The team releasing DINO 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 self-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 Transformer encoder.
+
+Note that this model does not include any fine-tuned heads. 
+
+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:
+
+```python
+from transformers import ViTFeatureExtractor, ViTModel
+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('facebook/dino-vitb16')
+model = ViTModel.from_pretrained('facebook/dino-vitb16')
+inputs = feature_extractor(images=image, return_tensors="pt")
+outputs = model(**inputs)
+last_hidden_states = outputs.last_hidden_state
+```
+
+### BibTeX entry and citation info
+
+```bibtex
+@article{DBLP:journals/corr/abs-2104-14294,
+  author    = {Mathilde Caron and
+               Hugo Touvron and
+               Ishan Misra and
+               Herv{\'{e}} J{\'{e}}gou and
+               Julien Mairal and
+               Piotr Bojanowski and
+               Armand Joulin},
+  title     = {Emerging Properties in Self-Supervised Vision Transformers},
+  journal   = {CoRR},
+  volume    = {abs/2104.14294},
+  year      = {2021},
+  url       = {https://arxiv.org/abs/2104.14294},
+  archivePrefix = {arXiv},
+  eprint    = {2104.14294},
+  timestamp = {Tue, 04 May 2021 15:12:43 +0200},
+  biburl    = {https://dblp.org/rec/journals/corr/abs-2104-14294.bib},
+  bibsource = {dblp computer science bibliography, https://dblp.org}
+}
+```