50 lines
2.5 KiB
Markdown
50 lines
2.5 KiB
Markdown
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<!--Copyright 2021 The HuggingFace Team. All rights reserved.
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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the License. You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
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specific language governing permissions and limitations under the License.
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⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
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rendered properly in your Markdown viewer.
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-->
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*This model was released on 2021-11-15 and added to Hugging Face Transformers on 2021-11-30.*
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# VisionTextDualEncoder
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<div class="flex flex-wrap space-x-1">
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<img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-DE3412?style=flat&logo=pytorch&logoColor=white">
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<img alt="FlashAttention" src="https://img.shields.io/badge/%E2%9A%A1%EF%B8%8E%20FlashAttention-eae0c8?style=flat">
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<img alt="SDPA" src="https://img.shields.io/badge/SDPA-DE3412?style=flat&logo=pytorch&logoColor=white">
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</div>
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## Overview
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The [`VisionTextDualEncoderModel`] can be used to initialize a vision-text dual encoder model with
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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
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to a shared latent space. The projection layers are randomly initialized so the model should be fine-tuned on a
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downstream task. This model can be used to align the vision-text embeddings using CLIP like contrastive image-text
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training and then can be used for zero-shot vision tasks such image-classification or retrieval.
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In [LiT: Zero-Shot Transfer with Locked-image Text Tuning](https://huggingface.co/papers/2111.07991) it is shown how
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leveraging pre-trained (locked/frozen) image and text model for contrastive learning yields significant improvement on
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new zero-shot vision tasks such as image classification or retrieval.
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## VisionTextDualEncoderConfig
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[[autodoc]] VisionTextDualEncoderConfig
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## VisionTextDualEncoderProcessor
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[[autodoc]] VisionTextDualEncoderProcessor
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## VisionTextDualEncoderModel
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[[autodoc]] VisionTextDualEncoderModel
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- forward
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