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All datasets matching “x.ai”X-Atlas-Orion
X-Atlas/Orion
X-Atlas: Orion edition (X-Atlas/Orion) is a Perturb-seq atlas containing two genome-wide Fix-Cryopreserve-ScRNAseq (FiCS) Perturb-seq screens that target all human
protein-coding genes (n = 18,903 genes). The dataset is comprised of eight million HCT116 and HEK293T cells, each deeply sequenced to a median of 16,000 unique molecular
identifiers (UMIs) per cell. The median on-target knockdown efficiency is 75.4% in HCT116 cells and 51.5% in HEK293T cells, with a median… See the full description on the dataset page: https://huggingface.co/datasets/Xaira-Therapeutics/X-Atlas-Orion.RealworldQA
RealWorldQA
RealWorldQA is a benchmark designed for real-world understanding. The dataset consists of anonymized images taken from vehicles, in addition to other real-world images. We are excited to release RealWorldQA to the community, and we intend to expand it as our multimodal models improve.
The initial release of the RealWorldQA consists of over 700 images, with a question and easily verifiable answer for each image. See the announcement of Grok-1.5 Vision Preview.… See the full description on the dataset page: https://huggingface.co/datasets/xai-org/RealworldQA.vlmsareblindArXiv - Website
X-AIGD
X-AIGD
X-AIGD is a fine-grained benchmark designed for eXplainable AI-Generated image Detection. It provides pixel-level human annotations of perceptual artifacts in AI-generated images, spanning low-level distortions, high-level semantics, and cognitive-level counterfactuals, aiming to advance robust and explainable AI-generated image detection methods.
For more details, please refer to our paper: Unveiling Perceptual Artifacts: A Fine-Grained Benchmark for Interpretable… See the full description on the dataset page: https://huggingface.co/datasets/Coxy7/X-AIGD.xAI_Aurora_t2i_human_preferences
Rapidata Aurora Preference
This T2I dataset contains over 400k human responses from over 86k individual annotators, collected in just ~2 Days using the Rapidata Python API, accessible to anyone and ideal for large scale evaluation.
Evaluating Aurora across three categories: preference, coherence, and alignment.
Explore our latest model rankings on our website.
If you get value from this dataset and would like to see more in the future, please consider liking it.… See the full description on the dataset page: https://huggingface.co/datasets/Rapidata/xAI_Aurora_t2i_human_preferences.Project-Imaging-X
Project Imaging-X is a strategic initiative to consolidate 1000+ open medical imaging datasets worldwide, breaking down data silos through systematic integration to build the foundational infrastructure for next-generation medical AI models.
Challenge: Medical imaging lacks large-scale unified datasets due to clinical expertise requirements and privacy constraints, limiting the development of powerful medical foundation models.
Solution: We surveyed 1000+… See the full description on the dataset page: https://huggingface.co/datasets/General-Medical-AI/Project-Imaging-X.