README.md

---
license: apache-2.0
tags:
- image-classification
- vision
datasets:
- imagenet
- imagenet-1k
---

# Data2Vec-Vision (base-sized model, fine-tuned on ImageNet-1k) 

BEiT model pre-trained in a self-supervised fashion and fine-tuned on ImageNet-1k (1,2 million images, 1000 classes) at resolution 224x224. It was introduced in the paper [data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language](https://arxiv.org/abs/2202.03555) by Alexei Baevski, Wei-Ning Hsu, Qiantong Xu, Arun Babu, Jiatao Gu, Michael Auli and first released in [this repository](https://github.com/facebookresearch/data2vec_vision/tree/main/beit). 

Disclaimer: The team releasing Facebook team did not write a model card for this model so this model card has been written by the Hugging Face team.

## Pre-Training method

![model image](https://raw.githubusercontent.com/patrickvonplaten/scientific_images/master/data2vec.png)

For more information, please take a look at the [official paper](https://arxiv.org/abs/2202.03555).

## Abstract

*While the general idea of self-supervised learning is identical across modalities, the actual algorithms and objectives differ widely because
they were developed with a single modality in
mind. To get us closer to general self-supervised
learning, we present data2vec, a framework that
uses the same learning method for either speech,
NLP or computer vision. The core idea is to predict latent representations of the full input data
based on a masked view of the input in a selfdistillation setup using a standard Transformer architecture. Instead of predicting modality-specific
targets such as words, visual tokens or units of
human speech which are local in nature, data2vec
predicts contextualized latent representations that
contain information from the entire input. Experiments on the major benchmarks of speech
recognition, image classification, and natural language understanding demonstrate a new state of
the art or competitive performance to predominant approaches.*

## Intended uses & limitations

You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=data2vec-vision) to look for
fine-tuned versions on a task that interests you.

### How to use

Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:

```python
from transformers import BeitFeatureExtractor, Data2VecVisionForImageClassification
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 = BeitFeatureExtractor.from_pretrained('facebook/data2vec-vision-base-ft1k')
model = Data2VecVisionForImageClassification.from_pretrained('facebook/data2vec-vision-base-ft1k')
inputs = feature_extractor(images=image, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
# model predicts one of the 1000 ImageNet classes
predicted_class_idx = logits.argmax(-1).item()
print("Predicted class:", model.config.id2label[predicted_class_idx])
```

Currently, both the feature extractor and model support PyTorch.

## Training data

The BEiT model was pretrained and fine-tuned on [ImageNet-1k](http://www.image-net.org/), a dataset consisting of 1,2 million images and 1k classes.

## Training procedure

### Preprocessing

The exact details of preprocessing of images during training/validation can be found [here](https://github.com/microsoft/unilm/blob/master/beit/datasets.py). 

Images are resized/rescaled to the same resolution (224x224) and normalized across the RGB channels with mean (0.5, 0.5, 0.5) and standard deviation (0.5, 0.5, 0.5).

### Pretraining

For all pre-training related hyperparameters, we refer to the [original paper](https://arxiv.org/abs/2106.08254) and the [original codebase](https://github.com/facebookresearch/data2vec_vision/tree/main/beit)

## Evaluation results

For evaluation results on several image classification benchmarks, we refer to tables 1 of the original paper. Note that for fine-tuning, the best results are obtained with a higher resolution. Of course, increasing the model size will result in better performance.

We evaluated the model on `ImageNet1K` and got top-1 accuracy = **83.97** while in the original paper it was reported top-1 accuracy = 84.2.
If you want to reproduce our evaluation process you can use [This Colab Notebook](https://colab.research.google.com/drive/1Tse8Rfv-QhapMEMzauxUqnAQyXUgnTLK?usp=sharing)

### BibTeX entry and citation info

```bibtex
@misc{https://doi.org/10.48550/arxiv.2202.03555,
  doi = {10.48550/ARXIV.2202.03555},
  url = {https://arxiv.org/abs/2202.03555},
  author = {Baevski, Alexei and Hsu, Wei-Ning and Xu, Qiantong and Babu, Arun and Gu, Jiatao and Auli, Michael},
  keywords = {Machine Learning (cs.LG), FOS: Computer and information sciences, FOS: Computer and information sciences},
  title = {data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language},
  publisher = {arXiv},
  year = {2022},
  copyright = {arXiv.org perpetual, non-exclusive license}
}
```