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*This model was published in HF papers on 2021-11-15 and contributed to Hugging Face Transformers on 2021-11-30.*

# VisionTextDualEncoder

<div class="flex flex-wrap space-x-1">
<img alt="FlashAttention" src="https://img.shields.io/badge/%E2%9A%A1%EF%B8%8E%20FlashAttention-eae0c8?style=flat">
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</div>

## Overview

The [`VisionTextDualEncoderModel`] can be used to initialize a vision-text dual encoder model with
any pretrained vision autoencoding model as the vision encoder (*e.g.* [ViT](vit), [BEiT](beit), [DeiT](deit)) and any pretrained text autoencoding model as the text encoder (*e.g.* [RoBERTa](roberta), [BERT](bert)). Two projection layers are added on top of both the vision and text encoder to project the output embeddings
to a shared latent space. The projection layers are randomly initialized so the model should be fine-tuned on a
downstream task. This model can be used to align the vision-text embeddings using CLIP like contrastive image-text
training and then can be used for zero-shot vision tasks such image-classification or retrieval.

In [LiT: Zero-Shot Transfer with Locked-image Text Tuning](https://huggingface.co/papers/2111.07991) it is shown how
leveraging pre-trained (locked/frozen) image and text model for contrastive learning yields significant improvement on
new zero-shot vision tasks such as image classification or retrieval.

## VisionTextDualEncoderConfig

[[autodoc]] VisionTextDualEncoderConfig

## VisionTextDualEncoderProcessor

[[autodoc]] VisionTextDualEncoderProcessor
    - __call__

## VisionTextDualEncoderModel

[[autodoc]] VisionTextDualEncoderModel
    - forward
    - get_text_features
    - get_image_features
