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+*This model was published in HF papers on 2019-12-24 and contributed to Hugging Face Transformers on 2022-12-07.*
+
+# Big Transfer (BiT)
+
+
+## Overview
+
+The BiT model was proposed in [Big Transfer (BiT): General Visual Representation Learning](https://huggingface.co/papers/1912.11370) by Alexander Kolesnikov, Lucas Beyer, Xiaohua Zhai, Joan Puigcerver, Jessica Yung, Sylvain Gelly, Neil Houlsby.
+BiT is a simple recipe for scaling up pre-training of [ResNet](resnet)-like architectures (specifically, ResNetv2). The method results in significant improvements for transfer learning.
+
+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.*
+
+This model was contributed by [nielsr](https://huggingface.co/nielsr).
+The original code can be found [here](https://github.com/google-research/big_transfer).
+
+## Usage tips
+
+- BiT models are equivalent to ResNetv2 in terms of architecture, except that: 1) all batch normalization layers are replaced by [group normalization](https://huggingface.co/papers/1803.08494),
+
+2) [weight standardization](https://huggingface.co/papers/1903.10520) is used for convolutional layers. The authors show that the combination of both is useful for training with large batch sizes, and has a significant
+impact on transfer learning.
+
+## Resources
+
+A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with BiT.
+
+<PipelineTag pipeline="image-classification"/>
+
+- [`BitForImageClassification`] is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/pytorch/image-classification) and [notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/image_classification.ipynb).
+- See also: [Image classification task guide](../tasks/image_classification)
+
+If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.
+
+## BitConfig
+
+[[autodoc]] BitConfig
+
+## BitImageProcessor
+
+[[autodoc]] BitImageProcessor
+    - preprocess
+
+## BitImageProcessorPil
+
+[[autodoc]] BitImageProcessorPil
+    - preprocess
+
+## BitModel
+
+[[autodoc]] BitModel
+    - forward
+
+## BitForImageClassification
+
+[[autodoc]] BitForImageClassification
+    - forward