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MMF

Accelerate multimodal AI research with pre-built models and training pipelines

BSD-3-Clause

ABOUT

Multimodal AI research — combining vision and language — requires coordinating multiple model architectures (vision encoders, language models, fusion layers) with complex training pipelines across datasets like VQA, COCO Captions, and NLVR2. Reproducing results from papers requires stitching together disparate codebases, preprocessing scripts, and hyperparameters. MMF provides a unified platform with reference implementations of the latest multimodal models, standardized dataset interfaces, distributed training, and built-in evaluation metrics. Researchers can reproduce published results, swap model components, and fine-tune pretrained multimodal models without rewriting infrastructure from scratch.

INSTALL
pip install --upgrade --pre mmf

INTEGRATION GUIDE

1. Fine-tune a pretrained vision-language model on a custom VQA dataset for domain-specific question answering 2. Benchmark multiple fusion architectures (M4C, LoRRA, Pythia) on the same dataset without changing the data pipeline 3. Train a multimodal hate speech classifier using the Hateful Memes benchmark with built-in evaluation 4. Reproduce published multimodal paper results from a single config — no scraping code from multiple repos 5. Build a multimodal dialog system that answers questions about images with context from conversation history

TAGS

multimodalvision-languagepytorchvqacaptioningfine-tuningfairresearch