Omkar2118/datasets huggingface dataset:
GenPhoto

dataset for Generative Photography

Introducing paper: Generative Photography: Scene-Consistent Camera Control for Realistic Text-to-Image Synthesis
RFUAV

In this paper, we propose RFUAV as a new benchmark dataset for radio-frequency based (RF-based) unmanned aerial vehicle (UAV) identification and address the following challenges: Firstly, many existing datasets feature a restricted variety of drone types and insufficient volumes of raw data, which fail to meet the demands of practical applications. Secondly, existing datasets often lack raw data covering a broad range of signal-to-noise ratios (SNR), or do not provide tools for transforming raw data to different SNR levels. This limitation undermines the validity of model training and evaluation. Lastly, many existing datasets do not offer open-access evaluation tools, leading to a lack of unified evaluation standards in current research within this field. RFUAV comprises approximately 1.3 TB of raw frequency data collected from 37 distinct UAVs using the Universal Software Radio Peripheral (USRP) device in real-world environments. Through in-depth analysis of the RF data in RFUAV, we define a drone feature sequence called RF drone fingerprint, which aids in distinguishing drone signals. In addition to the dataset, RFUAV provides a baseline preprocessing method and model evaluation tools. Rigorous experiments demonstrate that these preprocessing methods achieve state-of-the-art (SOTA) performance using the provided evaluation tools.

Introducing paper: RFUAV: A Benchmark Dataset for Unmanned Aerial Vehicle Detection and Identification
DroneVehicle
Full name: VisDrone-DroneVehicle

The DroneVehicle dataset consists of a total of 56,878 images collected by the drone, half of which are RGB images, and the resting are infrared images. We have made rich annotations with oriented bounding boxes for the five categories. Among them, car has 389,779 annotations in RGB images, and 428,086 annotations in infrared images, truck has 22,123 annotations in RGB images, and 25,960 annotations in infrared images, bus has 15,333 annotations in RGB images, and 16,590 annotations in infrared images, van has 11,935 annotations in RGB images, and 12,708 annotations in infrared images, and freight car has 13,400 annotations in RGB images, and 17,173 annotations in infrared image. This dataset is available on the download page.

In DroneVehicle, to annotate the objects at the image boundaries, we set a white border with a width of 100 pixels on the top, bottom, left and right of each image, so that the downloaded image scale is 840 x 712. When training our detection network, we can perform pre-processing to remove the surrounding white border and change the image scale to 640 x 512.

Introducing paper: Drone-based RGB-Infrared Cross-Modality Vehicle Detection via Uncertainty-Aware Learning
COLD: Causal Reasoning in Closed Daily Activities

The causal reasoning dataset is generated using the Causal Reasoning in Closed Daily Activities (COLD) framework that helps evaluate large language models (LLMs) on their causal reasoning abilities within real-world, everyday activities. This dataset provides causal questions that simulate common activities such as shopping, baking a cake, riding a bus, planting a tree, and going on a train ride. With approximately 9 million causal queries, the COLD dataset challenges LLMs to understand and reason about the causal relationships between events that are familiar and grounded in human experience.

Each query consists of a premise (an event) and a pair of choices representing possible causal effects. The goal of the model is to correctly identify which choice is the most plausible cause/effect of the given premise, testing the model's understanding of cause-and-effect relationships.

Key Features:
Activity Types: The dataset covers various everyday activities: shopping, cake baking, train ride, tree planting, and bus ride.
Causal Queries: Each query includes a premise and two possible causal events (choices). The model must decide which of the two choices is the more likely cause or effect.
Multiple-Choice Format: The queries can be formatted as multiple-choice questions (MCQA), where the model must choose between two options.

The dataset provides a valuable test for causal reasoning in NLP models, focusing on realistic, daily-life scenarios.

Introducing paper: COLD: Causal reasOning in cLosed Daily activities