Omkar2118/datasets huggingface dataset:
DATOR-ReID

A comprehensive object-instance ReID dataset with multiple indoor object instances under varying lighting conditions.

Introducing paper: Towards Global Localization using Multi-Modal Object-Instance Re-Identification
DATOR-lab

A real world dataset for benchmarking global localization in complex indoor environments.

Introducing paper: Towards Global Localization using Multi-Modal Object-Instance Re-Identification
DATOR-synth

A ProcTHOR created synthetic dataset for benchmarking global localization in complex indoor environments.

Introducing paper: Towards Global Localization using Multi-Modal Object-Instance Re-Identification
GAIA
Full name: a benchmark for general AI assistants

We introduce GAIA, a benchmark for General AI Assistants that, if solved, would represent a milestone in AI research. GAIA proposes real-world questions that require a set of fundamental abilities such as reasoning, multi-modality handling, web browsing, and generally tool-use proficiency. GAIA questions are conceptually simple for humans yet challenging for most advanced AIs: we show that human respondents obtain 92% vs. 15% for GPT-4 equipped with plugins. This notable performance disparity contrasts with the recent trend of LLMs outperforming humans on tasks requiring professional skills in e.g. law or chemistry. GAIA’s philosophy departs from the current trend in AI benchmarks suggesting to target tasks that are ever more difficult for humans. We posit that the advent of Artificial General Intelligence (AGI) hinges on a system’s capability to exhibit similar robustness as the average human does on such questions. Using GAIA’s methodology, we devise 466 questions and their answer. We release our questions while retaining answers to 300 of them to power a leader-board accessible at https://huggingface.co/gaia-benchmark.

Introducing paper: GAIA: a benchmark for General AI Assistants