Omkar2118/datasets huggingface dataset:
WaterScenes

A Multi-Task 4D Radar-Camera Fusion Dataset for Autonomous Driving on Water Surfaces description of the dataset 

* WaterScenes, the first multi-task 4D radar-camera fusion dataset on water surfaces, which offers data from multiple sensors, including a 4D radar, monocular camera, GPS, and IMU. It can be applied in multiple tasks, such as object detection, instance segmentation, semantic segmentation, free-space segmentation, and waterline segmentation.
* Our dataset covers diverse time conditions (daytime, nightfall, night), lighting conditions (normal, dim, strong), weather conditions (sunny, overcast, rainy, snowy) and waterway conditions (river, lake, canal, moat). An information list is also offered for retrieving specific data for experiments under different conditions.
* We provide 2D box-level and pixel-level annotations for camera images, and 3D point-level annotations for radar point clouds. We also offer precise timestamps for the synchronization of different sensors, as well as intrinsic and extrinsic parameters.
* We provide a toolkit for radar point clouds that includes: pre-processing, labeling, projection and visualization, assisting researchers in processing and analyzing our dataset.

Introducing paper: WaterScenes: A Multi-Task 4D Radar-Camera Fusion Dataset and Benchmarks for Autonomous Driving on Water Surfaces
BDD-QA
Full name: BDD-QA

**BDD-QA** is distinguished by its encompassing range of traffic actions, crafted to rigorously evaluate a model's decision-making abilities in traffic scenario. This makes it a potent tool for high-level decision-making research within traffic contexts, including autonomous driving developments.

Introducing paper: A Study of Situational Reasoning for Traffic Understanding
HDT-QA
Full name: human driving test question answering dataset

HDT-QA, coupled with driving manuals, offers an extensive compendium of driving instructions and driving knowledge tests across all 51 states of the US. This resource is beneficial for assessing the incorporation and impact of traffic knowledge within intelligent driving systems, marking a crucial stride towards more advanced, informed, and safe autonomous driving technology.

Introducing paper: A Study of Situational Reasoning for Traffic Understanding
Complex-TV-QA
Full name: complex traffic video question answer datset

The Complex-TV-QA dataset, to our knowledge, is the inaugural resource that provides human-annotated, detailed video captions within traffic scenarios, alongside complex reasoning questions. This novel dataset not only stands as a vital tool for evaluating language models in real-world video-QA and video-reasoning research, but also offers valuable insights for the development and understanding of multi-modal video reasoning models and related works.

Introducing paper: A Study of Situational Reasoning for Traffic Understanding
NEU dataset

Data set used in the work One-Shot Recognition of Manufacturing Defects in Steel Surfaces
RidgeBase
Full name: RidgeBase: A Cross-Sensor Multi-Finger Contactless Fingerprint Dataset

Contactless fingerprint matching using smartphone cameras can alleviate major challenges of traditional fingerprint systems including hygienic acquisition, portability and presentation attacks. However, development of practical and robust contactless fingerprint matching techniques is constrained by the limited availability of large scale real-world datasets. To motivate further advances in contactless fingerprint matching across sensors, we introduce the RidgeBase benchmark dataset. RidgeBase consists of more than 15,000 contactless and contact-based fingerprint image pairs acquired from 88 individuals under different background and lighting conditions using two smartphone cameras and one flatbed contact sensor. Unlike existing datasets, RidgeBase is designed to promote research under different matching scenarios that include Single Finger Matching and Multi-Finger Matching for both contactless-to-contactless (CL2CL) and contact-to-contactless (C2CL) verification and identification. Furthermore, due to the high intra-sample variance in contactless fingerprints belonging to the same finger, we propose a set-based matching protocol inspired by the advances in facial recognition datasets. This protocol is specifically designed for pragmatic contactless fingerprint matching that can account for variances in focus, polarity and finger-angles. We report qualitative and quantitative baseline results for different protocols using a COTS fingerprint matcher (Verifinger) and a Deep CNN based approach on the RidgeBase dataset. The dataset can be downloaded here: \url{https://www.buffalo.edu/cubs/research/datasets/ridgebase-benchmark-dataset.html}

Introducing paper: RidgeBase: A Cross-Sensor Multi-Finger Contactless Fingerprint Dataset