Duplicate from facebook/dino-vitb16

Co-authored-by: Julien Chaumond <julien-c@users.noreply.huggingface.co>

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README.md

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+---
+license: apache-2.0
+tags:
+- dino
+- vision
+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/2104.14294) 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 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('facebook/dino-vitb16')
+model = ViTModel.from_pretrained('facebook/dino-vitb16')
+
+inputs = processor(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}
+}
+```

config.json

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+{
+  "architectures": [
+    "ViTModel"
+  ],
+  "attention_probs_dropout_prob": 0.0,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.0,
+  "hidden_size": 768,
+  "image_size": 224,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-12,
+  "model_type": "vit",
+  "num_attention_heads": 12,
+  "num_channels": 3,
+  "num_hidden_layers": 12,
+  "patch_size": 16,
+  "torch_dtype": "float32",
+  "transformers_version": "4.10.0.dev0"
+}

preprocessor_config.json

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+{
+  "do_normalize": true,
+  "do_resize": true,
+  "feature_extractor_type": "ViTFeatureExtractor",
+  "image_mean": [
+    0.485, 
+    0.456, 
+    0.406
+  ],
+  "image_std": [
+    0.229, 
+    0.224, 
+    0.225
+  ],
+  "resample": 2,
+  "size": 224
+}

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