Omkar2118/datasets huggingface dataset:
Hilti-Oxford Dataset

This benchmark is based on the HILTI-OXFORD Dataset, which has been collected on construction sites as well as on the famous Sheldonian Theatre in Oxford, providing a large range of difficult problems for SLAM. 

All these sequences are characterized by featureless areas and varying illumination conditions that are typical in real-world scenarios and pose great challenges to SLAM algorithms that have been developed in confined lab environments. Accurate ground truth, at millimeter level, is provided for each sequence. The sensor platform used to record the data includes a number of visual, lidar, and inertial sensors, which are spatially and temporally calibrated.

Introducing paper: Hilti-Oxford Dataset: A Millimetre-Accurate Benchmark for Simultaneous Localization and Mapping
Newer College Dataset Extension
Full name: Multi-Camera LiDAR Inertial

In this expansion dataset, we have extended the ground truth pointcloud of New College to include the colllege’s cloister (featured in Harry Potter films) and the Monk’s Passage. We also introduce another outdoor large scale environment, Maths Institute at the University of Oxford, which is shown in the figure below (right).  Collection 1 and 2 contain datasets collected in New college, where the Park sequence mimics the original long sequence of the 2011 New College Dataset. Collection 3 contains three difficulty levels around the Maths Institute environment.
Note that in this dataset Lidar, Cameras and IMU are all hardware synchronised through PTP.
NL2GQL Dataset
Full name: NL2GQL  developped for R3-NL2GQL

A bilingual (English and Chinese natural language queries) dataset which has NL queries annotated with their corresponding GQL queries (i.e. nGQL). Each data sample in the train data contains 4 pieces of information: prompt represents a natural language query, content represents a standard nGQL, reason represents the inference part that needs to be output by the reranker, and schema represents the code structure schema corresponding to this sentence. Each data sample in the test data contains 6 pieces of information, prompt represents natural language query, content represents gold nGQL, text_schema is used for the vanilla experiment, schema represents the code structure schema corresponding to this sentence, class represents which graph database space this sentence corresponds to, and result represents the results obtained using gold nGQL.
geometricShapes14k
Full name: Images made up of geometric shapes - ControlNet

The dataset was curated from the 1% data sample file of the Wikipedia-based Image Text (WIT) Dataset. Captions for images were generated using the BLIP model. Control images were derived using the Primitive software, resulting in a dataset of 14,279 pairs of control and target images with captions.

Introducing paper: Abstract Art Interpretation Using ControlNet
ISTD+

ISTD+ consists of shadow images, shadow-free images, and shadow masks, with 1,330 training images and 540 testing images from 135 unique background scenes. ISTD suffers from color and luminosity inconsistencies between shadow and shadow-free images, which ISTD+ corrects with a color compensation mechanism to ensure uniform pixel colors across the ground-truth images.
SECURES-Met

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