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# Vision Encoder Decoder Models

The [`VisionEncoderDecoderModel`] can be used to initialize an image-to-text-sequence model with any
pretrained Transformer-based vision autoencoding model as the encoder (*e.g.* [ViT](vit), [BEiT](beit), [DeiT](deit), [Swin](swin))
and any pretrained language model as the decoder (*e.g.* [RoBERTa](roberta), [GPT2](gpt2), [BERT](bert), [DistilBERT](distilbert)).

The effectiveness of initializing image-to-text-sequence models with pretrained checkpoints has been shown in (for
example) [TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models](https://arxiv.org/abs/2109.10282) by Minghao Li, Tengchao Lv, Lei Cui, Yijuan Lu, Dinei Florencio, Cha Zhang,
Zhoujun Li, Furu Wei.

An example of how to use a [`VisionEncoderDecoderModel`] for inference can be seen in [TrOCR](trocr).


## VisionEncoderDecoderConfig

[[autodoc]] VisionEncoderDecoderConfig

## VisionEncoderDecoderModel

[[autodoc]] VisionEncoderDecoderModel
    - forward
    - from_encoder_decoder_pretrained

## TFVisionEncoderDecoderModel

[[autodoc]] TFVisionEncoderDecoderModel
    - call
    - from_encoder_decoder_pretrained

## FlaxVisionEncoderDecoderModel

[[autodoc]] FlaxVisionEncoderDecoderModel
    - __call__
    - from_encoder_decoder_pretrained
