Omkar2118/datasets huggingface dataset:
InCrowd-VI

## About 
A realistic visual-inertial dataset with 58 sequences spanning 5km of trajectories and 1.5 hours of recordings, designed for evaluating SLAM systems in indoor pedestrian-rich environments. The dataset is particularly aimed at advancing navigation technologies for visually impaired individuals.

Introducing paper: InCrowd-VI: A Realistic Visual-Inertial Dataset for Evaluating SLAM in Indoor Pedestrian-Rich Spaces for Human Navigation
TSFM-ScalingLaws-Dataset

TSFM-ScalingLaws-Dataset

This is the dataset for the paper Towards Neural Scaling Laws for Time Series Foundation Models.

We selected some high-quality (SNR > 20), domain-balanced, and low-redundancy data from Lotsa and UTS datasets for TSFM pre-training. These datasets contain three sizes: 16B, 1B, 100M, and 10M.

Code: https://github.com/Qingrenn/TSFM-ScalingLaws

Well-trained models: https://huggingface.co/PeacefulData/TSFM-ScalingLaws-Checkpoints

Introducing paper: Towards Neural Scaling Laws for Time Series Foundation Models