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--- |
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license: apache-2.0 |
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tags: |
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- dino |
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- vision |
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datasets: |
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- imagenet-1k |
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--- |
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# Vision Transformer (base-sized model, patch size 16) trained using DINO |
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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). |
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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. |
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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 self-supervised fashion, namely ImageNet-1k, at a resolution of 224x224 pixels. |
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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. |
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Note that this model does not include any fine-tuned heads. |
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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: |
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```python |
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from transformers import ViTFeatureExtractor, 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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feature_extractor = ViTFeatureExtractor.from_pretrained('facebook/dino-vitb16') |
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model = ViTModel.from_pretrained('facebook/dino-vitb16') |
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inputs = feature_extractor(images=image, return_tensors="pt") |
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outputs = model(**inputs) |
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last_hidden_states = outputs.last_hidden_state |
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``` |
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### BibTeX entry and citation info |
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```bibtex |
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@article{DBLP:journals/corr/abs-2104-14294, |
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author = {Mathilde Caron and |
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Hugo Touvron and |
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Ishan Misra and |
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Herv{\'{e}} J{\'{e}}gou and |
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Julien Mairal and |
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Piotr Bojanowski and |
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Armand Joulin}, |
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title = {Emerging Properties in Self-Supervised Vision Transformers}, |
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journal = {CoRR}, |
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volume = {abs/2104.14294}, |
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year = {2021}, |
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url = {https://arxiv.org/abs/2104.14294}, |
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archivePrefix = {arXiv}, |
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eprint = {2104.14294}, |
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timestamp = {Tue, 04 May 2021 15:12:43 +0200}, |
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biburl = {https://dblp.org/rec/journals/corr/abs-2104-14294.bib}, |
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bibsource = {dblp computer science bibliography, https://dblp.org} |
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} |
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``` |
