facebookresearch/mmf

Health v2 · maintenance only

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Adoption not part of the score

5633 stars · 938 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

MMF is a modular PyTorch framework for vision and language multimodal research from Facebook AI Research. It ships reference implementations of state-of-the-art multimodal models, dataset tooling, and distributed training support, and serves as starter code for challenges like Hateful Memes, TextVQA, and VQA.

Use cases

• train a visual question answering model
• run pretrained multimodal baselines like ViLT or M4C
• bootstrap a new vision-and-language research project
• fine-tune models on the Hateful Memes dataset
• benchmark on TextVQA, TextCaps, or VQA challenges
• add a custom multimodal dataset or model with minimal boilerplate

When to choose

• you do vision-and-language multimodal research in PyTorch
• you want reference implementations of SOTA multimodal models
• you need distributed training and dataset/model zoos out of the box
• you are competing in VQA-family challenges

When to avoid

• you need production multimodal inference serving rather than research code
• you work outside vision-and-language modalities
• you want a lightweight library without framework conventions
• you need actively maintained support for the latest model architectures

Facets

framework · maturity maintenance

machine-learning deep-learning nlp image-processing benchmarking deep-learning machine-learning computer-vision python cross-platform multimodal vision-and-language pytorch vqa captioning pretrained-models facebook-ai-research natural-language-processing research gpu

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markdown · JSON · MCP: product_card(name="facebookresearch/mmf")