Meet OpenFlamingo: A Framework for Training and Evaluating Large Multimodal Models (LMMs) Capable of Processing Images and Text

OpenFlamingo is an open-source framework that aims to democratize access to state-of-the-art Large Multimodal Models (LMMs) by providing a system capable of handling various vision-language tasks. Developed as a reproduction of DeepMind’s Flamingo model, OpenFlamingo offers a Python framework to train Flamingo-style LMMs, a large-scale multimodal dataset, an in-context learning evaluation benchmark, and the first version of OpenFlamingo-9B model based on LLaMA.

The OpenFlamingo-9B checkpoint is trained on a massive dataset, including 5 million samples from the Multimodal C4 dataset and 10 million samples from LAION-2B. The Multimodal-C4 dataset is an extended version of the C4 dataset, which was used to train T5 models. It includes downloadable images for each document and has undergone data cleaning to remove non-safe for work (NSFW) and unrelated images such as advertisements. Face detection is carried out, and images with identifications are discarded. Images and sentences are interleaved using bipartite matching within a document, where CLIP ViT/L-14 image-text similarities serve as edge weights. The dataset comprises around 75 million documents, including approximately 400 million images and 38 billion tokens.

The project aims to make state-of-the-art LMMs more accessible by building fully open-source models. The community is encouraged to provide feedback and contribute to the repository, which is expected to have a full release with more details soon.

The release of OpenFlamingo is significant as it addresses the growing need for LMMs in various applications, including image and video captioning, image retrieval, question-answering, and more. The framework provides a flexible and scalable solution for training and evaluating LMMs, allowing researchers and practitioners to develop custom models for specific use cases.

Overall, OpenFlamingo is a promising development in the field of LMMs. Its open-source approach and large-scale dataset offer a way for researchers and practitioners to develop more sophisticated models for vision-language tasks. It will be exciting to see how the community contributes to the framework and how it evolves in the future.

Here are a few examples source-https://7164d2142d11.ngrok.app/

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