19,997 machine learning datasets
19,997 dataset results
Description: The Anime Style Dataset is ideal for training AI models to perform style transformation between real human faces and anime-style illustrations. It contains two main folders:
Description: <a href="https://gts.ai/dataset-download/horse-racing-photo-dataset/" target="_blank">👉 Download the dataset here</a> This dataset provides a detailed collection of horse race photo finishes from PMU events. It is ideal for machine learning and computer vision research, particularly in image recognition and sports analytics.
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IOV
Description: The Corn Kernel Images Dataset is a comprehensive collection of 78,881 images of individual corn kernels. These images were captured after the kernels were harvested, processed, and hand-shelled, primarily for agricultural research and data analysis. <a href="https://gts.ai/dataset-download/corn-kernel-images-dataset/" target="_blank">👉 Download the dataset here</a>
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Data Set Structure Fluid Structure Interaction(NS +Elastic wave) The TF_fsi2_results folder contains simulation data organized by various parameters (mu, x1, x2) where mu determines the viscosity and x1 and x2 are the parameters of the inlet condition. The dataset includes files for mesh, displacement, velocity, and pressure.
IQ testing has served as a foundational methodology for evaluating human cognitive capabilities, deliberately decoupling assessment from linguistic background, language proficiency, or domain-specific knowledge to isolate core competencies in abstraction and reasoning. Yet, artificial intelligence research currently lacks systematic benchmarks to quantify these critical cognitive dimensions in multimodal systems. To address this critical gap, we propose MM-IQ, a comprehensive evaluation framework comprising 2,710 meticulously curated test items spanning 8 distinct reasoning paradigms.
One of the most important aspects of robot scene understanding is semantic segmentation of external environments. Urban environment semantic segmentation has been extensively investigated by researchers and many real-world and synthetic datasets have been utilised to develop highly accurate segmentation results. However, the number of off-road datasets available for robot navigation research remains limited. To address this, we introduce a novel framework [1] to generate varied photorealistic synthetic off-road datasets capable of supporting multiple sensor modalities.
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