Data2vec Vision Base Ft1k

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

A BEiT model pre-trained self-supervised and fine-tuned on ImageNet-1k for image classification.

🚀 Quick Start

The Data2Vec-Vision model is a BEiT model pre-trained in a self-supervised manner and fine-tuned on ImageNet-1k. It can be used for image classification tasks.

✨ Features

• Self-supervised Pre-training: The model is pre-trained in a self-supervised fashion, enabling it to learn general features from unlabeled data.
• Fine-tuned on ImageNet-1k: Fine-tuned on ImageNet-1k, which contains 1.2 million images and 1000 classes, making it suitable for image classification tasks.
• Same Learning Method across Modalities: Based on the data2vec framework, it uses the same learning method for speech, NLP, and computer vision.

💻 Usage Examples

Basic Usage

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.

📚 Documentation

Pre-Training method

For more information, please take a look at the official paper.

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 to look for fine-tuned versions on a task that interests you.

Training data

The BEiT model was pretrained and fine-tuned on ImageNet-1k, 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.

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 and the original codebase

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

BibTeX entry and citation info

@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}
}

📄 License

This model is released under the Apache-2.0 license.