facebookresearch/multimodal

What it solves

TorchMultimodal provides a scalable PyTorch library for training state-of-the-art multimodal models. It simplifies the process of building these models by providing modular building blocks, pretrained weights for common configurations, and example scripts for various multimodal tasks.

How it works

The library is organized into several key components:

• Modular Building Blocks: Includes fusion layers, loss functions, datasets, and utilities that can be composed to create new architectures.
• Model Classes: Provides ready-to-use implementations of popular multimodal models such as ALBEF, BLIP-2, CLIP, CoCa, DALL-E 2, FLAVA, MAE/Audio MAE, and MDETR.
• Diffusion Labs: A dedicated section for components specifically designed for building diffusion models.
• Data Transforms: Includes common data transforms from popular models like CLIP, FLAVA, and MAE to ensure data consistency.
• Examples: Practical scripts for tasks like retrieval, visual question answering, phrase grounding, and text-to-video generation.

Who it’s for

This library is designed for researchers and developers who want to train, fine-tune, or evaluate multimodal models at scale, using them as baselines for research or as a starting point for future work.

Highlights

• Extensive Model Zoo: Supports a wide range of multimodal architectures including CLIP, BLIP-2, and DALL-E 2.
• Composable Architecture: Offers generic building blocks like ViT and BERT encoders, transformer encoder/decoders, and various fusion modules.
• Pretrained Weights: Provides pretrained weights for canonical configurations of common models.
• Crossover Tasks: Supports diverse tasks such as text-to-video generation and visual question answering.