#Vision-Language Models

Discover an inclusive platform evaluating large multimodal models in visual question-answering. Access the OmniMedVQA dataset with 118,010 medical images, and explore the Tiny LVLM-eHub with initial experiments using Bard. The platform includes a leaderboard highlighting visual perception and reasoning, providing insights into 12 varied models across fields. Explore benchmarks and performance data for improved model evaluation in vision-language integration.

This open-source repository, based on PyTorch, presents methods for online test-time adaptation using datasets like CIFAR10-C, ImageNet-C, and DomainNet-126. It explores techniques in self-training, style transfer, and resilient teacher models for adaptation across different domains. Additionally, it includes universal strategies such as weight ensembling. It supports mixed precision training and CLIP models, offering tutorials for practical application across domain variations. The repository's modular design facilitates the addition of new methods, supporting research in adaptive learning.