Bit 50

🚀 Big Transfer (BiT)

BiT is a simple recipe for scaling up pre - training of ResNet-like architectures, which significantly improves transfer learning performance.

🚀 Quick Start

The BiT model was proposed in Big Transfer (BiT): General Visual Representation Learning by Alexander Kolesnikov, Lucas Beyer, Xiaohua Zhai, Joan Puigcerver, Jessica Yung, Sylvain Gelly, Neil Houlsby. It simplifies hyper - parameter tuning and enhances sample efficiency for vision deep neural networks through pre - training and fine - tuning.

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

✨ Features

• Scalability: Scales up pre - training of ResNet-like architectures (specifically, ResNetv2).
• High Performance: Achieves strong performance on over 20 datasets, performing well across a wide range of data regimes.

📚 Documentation

Model description

The abstract from the paper is the following:

Transfer of pre - trained representations improves sample efficiency and simplifies hyperparameter tuning when training deep neural networks for vision. We revisit the paradigm of pre - training on large supervised datasets and fine - tuning the model on a target task. We scale up pre - training, and propose a simple recipe that we call Big Transfer (BiT). By combining a few carefully selected components, and transferring using a simple heuristic, we achieve strong performance on over 20 datasets. BiT performs well across a surprisingly wide range of data regimes -- from 1 example per class to 1M total examples. BiT achieves 87.5% top - 1 accuracy on ILSVRC - 2012, 99.4% on CIFAR - 10, and 76.3% on the 19 task Visual Task Adaptation Benchmark (VTAB). On small datasets, BiT attains 76.8% on ILSVRC - 2012 with 10 examples per class, and 97.0% on CIFAR - 10 with 10 examples per class. We conduct detailed analysis of the main components that lead to high transfer performance.

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 to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:

from transformers import BitImageProcessor, BitForImageClassification
import torch
from datasets import load_dataset

dataset = load_dataset("huggingface/cats-image")
image = dataset["test"]["image"][0]

feature_extractor = BitImageProcessor.from_pretrained("google/bit-50")
model = BitForImageClassification.from_pretrained("google/bit-50")

inputs = feature_extractor(image, return_tensors="pt")

with torch.no_grad():
    logits = model(**inputs).logits

# model predicts one of the 1000 ImageNet classes
predicted_label = logits.argmax(-1).item()
print(model.config.id2label[predicted_label])
>>> tabby, tabby cat

For more code examples, we refer to the documentation.

BibTeX entry and citation info

@misc{https://doi.org/10.48550/arxiv.1912.11370,
  doi = {10.48550/ARXIV.1912.11370},
  
  url = {https://arxiv.org/abs/1912.11370},
  
  author = {Kolesnikov, Alexander and Beyer, Lucas and Zhai, Xiaohua and Puigcerver, Joan and Yung, Jessica and Gelly, Sylvain and Houlsby, Neil},
  
  keywords = {Computer Vision and Pattern Recognition (cs.CV), Machine Learning (cs.LG), FOS: Computer and information sciences, FOS: Computer and information sciences},
  
  title = {Big Transfer (BiT): General Visual Representation Learning},
  
  publisher = {arXiv},
  
  year = {2019},
  
  copyright = {arXiv.org perpetual, non-exclusive license}
}

📄 License

This model is released under the Apache - 2.0 license.