Vision Transformer (small-sized model, patch size 8) trained using DINO

Vision Transformer (vit免费的ai工具) model trained using the DINO method. It was introduced in the paper Emerging Properties in Self-Supervised Vision Transformersal一键脱装入口 by Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jégou, Julien Mairal, Piotr Bojanowski, Armand Joulin and first released in this repository.

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.百度流畅ai制作

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 224×224 pixels.有戏ai

Images are presented to the model as a sequence of fixed-size patches (resolution 8×8), 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.即梦下载官方

Note that this model does not include any fine-tuned heads.grok中文版下载

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.百度ai智能云

Intended uses & limitations

You can use the raw model for image classification. See the model hub to look for
fine-tuned versions on a task that interests you.

How to use

Here is how to use this model:grok中文版下载

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-vits8')
model = ViTModel.from_pretrained('facebook/dino-vits8')
inputs = feature_extractor(images=image, return_tensors="pt")
outputs = model(**inputs)
last_hidden_states = outputs.last_hidden_state

BibTeX entry and citation info

@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}
}