Back to Transformers

FLAVA

docs/source/en/model_doc/flava.md

5.8.03.3 KB
Original Source
<!--Copyright 2022 The HuggingFace Team. All rights reserved. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. ⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be rendered properly in your Markdown viewer. -->

This model was released on 2021-12-08 and added to Hugging Face Transformers on 2022-05-11.

FLAVA

<div class="flex flex-wrap space-x-1"> </div>

Overview

The FLAVA model was proposed in FLAVA: A Foundational Language And Vision Alignment Model by Amanpreet Singh, Ronghang Hu, Vedanuj Goswami, Guillaume Couairon, Wojciech Galuba, Marcus Rohrbach, and Douwe Kiela and is accepted at CVPR 2022.

The paper aims at creating a single unified foundation model which can work across vision, language as well as vision-and-language multimodal tasks.

The abstract from the paper is the following:

State-of-the-art vision and vision-and-language models rely on large-scale visio-linguistic pretraining for obtaining good performance on a variety of downstream tasks. Generally, such models are often either cross-modal (contrastive) or multi-modal (with earlier fusion) but not both; and they often only target specific modalities or tasks. A promising direction would be to use a single holistic universal model, as a "foundation", that targets all modalities at once -- a true vision and language foundation model should be good at vision tasks, language tasks, and cross- and multi-modal vision and language tasks. We introduce FLAVA as such a model and demonstrate impressive performance on a wide range of 35 tasks spanning these target modalities.

This model was contributed by aps. The original code can be found here.

FlavaConfig

[[autodoc]] FlavaConfig

FlavaTextConfig

[[autodoc]] FlavaTextConfig

FlavaImageConfig

[[autodoc]] FlavaImageConfig

FlavaMultimodalConfig

[[autodoc]] FlavaMultimodalConfig

FlavaImageCodebookConfig

[[autodoc]] FlavaImageCodebookConfig

FlavaProcessor

[[autodoc]] FlavaProcessor - call

FlavaImageProcessor

[[autodoc]] FlavaImageProcessor - preprocess

FlavaImageProcessorPil

[[autodoc]] FlavaImageProcessorPil - preprocess

FlavaForPreTraining

[[autodoc]] FlavaForPreTraining - forward

FlavaModel

[[autodoc]] FlavaModel - forward - get_text_features - get_image_features

FlavaImageCodebook

[[autodoc]] FlavaImageCodebook - forward - get_codebook_indices - get_codebook_probs

FlavaTextModel

[[autodoc]] FlavaTextModel - forward

FlavaImageModel

[[autodoc]] FlavaImageModel - forward

FlavaMultimodalModel

[[autodoc]] FlavaMultimodalModel - forward