What is LAVIS?

LAVIS is a Python deep learning library for LAnguage-and-VISion research and applications. It features a unified design to access state-of-the-art foundation language-vision models (ALBEF, BLIP, ALPRO, CLIP), common tasks (retrieval, captioning, visual question answering, multimodal classification etc.) and datasets (COCO, Flickr, Nocaps, Conceptual Commons, SBU, etc.).

This library aims to provide engineers and researchers with a one-stop solution to rapidly develop models for their specific multimodal scenarios, and benchmark them across standard and customized datasets.

Key features of LAVIS include:

• Modular and Extensible Library Design: facilitating to easily utilize and repurpose existing modules (datasets, models, preprocessors), also to add new modules.
• Easy Off-the-shelf Inference and Feature Extraction: readily available pre-trained models let you take advantage of state-of-the-art multimodal understanding and generation capabilities on your own data.
• Reproducible Model Zoo: provided training/pre-training recipies to easily replicate and extend state-of-the-art models.
• Dataset Zoo and Automatic Downloading Tools: it can be a hassle to prepare the many language-vision datasets. LAVIS provides automatic downloaing scripts to help prepare a large variety of datasets and their annotations.

Other features include:

• Distributed Training using multiple GPUs on one machine or across multiple machines.
• Web Demo: try supported models on your own pictures, questions etc.
• Leaderboard: comparing state-of-the-art models across standard datasets.
• Dataset Explorer: help browse and understand language-vision datasets.

Supported Tasks, Models and Datasets

The following table shows the supported models and language-vision tasks by LAVIS. Adapting existing models to more tasks is possible and next to come in future releases.

Library Design

LAVIS has six key modules.

• lavis.runners manages the overall training and evaluation lifecycle. It is also responsible for creating required components lazily as per demand, such as optimizers, learning rate schedulers and dataloaders. Currently RunnerBase implements epoch-based training and RunerIters implements iteration-based training.

• lavis.tasks implements concrete training and evaluation logic per task. A task could be, for example, retrieval, captioning, pre-training. The rationale to have an abstraction of task is to accommodate task-specific training and evaluation. For example, evaluating a retrieval model is different from a classification model.

• lavis.datasets is responsible for creating datasets, where lavis.datasets.builders loads dataset configurations, downloads annotations and returns a dataset object; lavis.datasets.datasets defines the supported datasets, each is a torch.utils.data.Dataset instance. We also provide automatic dataset downloading tools in datasets/download_scripts to help prepare common public datasets.

• lavis.models holds definition for the supported models and shared model layers.

• lavis.processors handles preprocessing of text and images/videos before feeding the model. For images and videos, a processor can be thought as transfroms in torchvision; for text input, this may include lowering case, truncation etc.

• lavis.common module contains shared classes and methods used by multiple other modules. For example,

  • lavis.common.config contains classes to store and manipulate configuration files used by LAVIS. In particular, we use a hierarchical configuration design, to allow highly customizable training and evaluation.
  • lavis.common.registry serves as a centralized place to manage modules that share the same functionalities. It allows building datasets, models, tasks, and learning rate schedulers during runtime, by specifying their names as string in the configuration file.
  • lavis.common.optims contains definitions of learning rate schedulers.
  • lavis.common.dist_utils contains utilities for distributed training and evaluation.
  • lavis.common.utils contains miscellaneous utilities, mostly IO-related helper functions.

Installation

1. (Optional) Creating conda environment

conda create -n lavis python=3.8
conda activate lavis

2. Cloning and building from source

git clone https://github.com/salesforce/LAVIS.git
cd LAVIS
pip install .

If you would like to develop on LAVIS, you may find it easier to build with editable mode:

pip install -e .