#Benchmark Datasets

This comprehensive resource lists tools, datasets, and models for Large Language Model (LLM) evaluation, capturing the breadth of Generative AI capabilities. It is a central information point for researchers and developers to access state-of-the-art tools from Hugging Face, OpenAI, and Google. Features include performance metrics for inference speed and multi-modal evaluations, fostering transparent, community-driven advancements in AI assessment approaches.

LibMTL is an open-source library for Multi-Task Learning (MTL) using PyTorch, offering a unified code base and support for various MTL methods and optimization strategies. Recent updates include FairGrad and STCH support, making it a comprehensive resource for researchers and developers interested in MTL.

Discover a wide array of resources focused on image and video deblurring techniques, utilizing both deep learning and non-deep learning approaches. This curated selection features scholarly articles, project links, and code repositories, exploring various facets of deblurring, including single-image blind motion, non-blind, multi-image and video motion, and defocus deblurring. Keep informed on the newest developments, benchmark datasets, and AI photo enhancement tools. A valuable resource for researchers and developers focused on improving image clarity and pioneering new deblurring technologies.

This powerful toolbox leverages Python and PyTorch to offer efficient, GPU-accelerated image quality assessment. It supports a wide range of full reference and no-reference metrics, providing evaluations faster than those of traditional MATLAB scripts. The toolbox includes recent updates such as improved metrics and reduced GPU memory requirements. Users can install and utilize it via the command line or integrate it into larger projects with customizable settings and loss function integration. Benchmark performance comparisons and datasets are also provided for robust evaluations.