Omkar2118/datasets huggingface dataset:
InfraParis

InfraParis is a novel and versatile dataset supporting multiple tasks across three modalities: RGB, depth, and infrared.  From the city to the suburbs, it contains a variety of styles in different areas of the greater Paris area, providing rich semantic information. InfraParis contains 7301 images with bounding boxes and full semantic (19 classes) annotations.
We assess various state-of-the-art baseline techniques, encompassing models for the tasks of semantic segmentation, object detection, and depth estimation.

Introducing paper: InfraParis: A multi-modal and multi-task autonomous driving dataset
UIIS
Full name: General Underwater Image Instance Segmentation dataset

This is  the first general Underwater Image Instance Segmentation (UIIS) dataset containing 4,628 images for 7 categories with pixel-level annotations for underwater instance segmentation task

Introducing paper: WaterMask: Instance Segmentation for Underwater Imagery
Multi-Sensor Calibration

Two separate datasets of calibration runs in front of a calibration board:
- 4IMUs+3Cams
-4IMUs+4Cams 


sensor	hz	topic	resolution
MicroStrain GX3-25	500hz	/gx3_25/data	
MicroStrain GX3-25	100hz	/gx3_35/imudata	
Xsens MTI-100	400hz	/imu/data	
RealSense T265 IMU	200hz	/t265/imu	
RealSense T265 Left	30hz	/t265/fisheye2/image_raw	848x800
RealSense T265 Right	30hz	/t265/fisheye2/image_raw	848x800
ELP Left Rolling Shutter	25hz	/elpsplit_sync_image_node/left/image_raw	640x480
ELP Right Rolling Shutter 25hz	/elp/split_sync_image_node/right/image_raw	640x480

Introducing paper: Multi-Visual-Inertial System: Analysis, Calibration and Estimation
BirdSoundsDenoising: Deep Visual Audio Denoising for Bird Sounds

This is the dataset for BirdSoundsDenoising including training, validation and test.

Introducing paper: BirdSoundsDenoising: Deep Visual Audio Denoising for Bird Sounds
SaGA
Full name: The Bielefeld Speech and Gesture Alignment Corpus (SaGA)

The primary data of the SaGA corpus are made up of 25 dialogs of interlocutors (50), who engage in a spatial communication task combining direction-giving and sight description. Six of those dialogues with data only from the direction giver are available including audio (*.wav) and video (*.mp4) data. The secondary data consists of annotations (*.eaf) of gestures and speech-gesture referents, which have been completely and systematically annotated based on an annotation grid (cf. the SaGA documentation). The corpus is comprised of of 9881 isolated words and 1764 isolated gestures. The stimulus is a model of a town presented in a Virtual Reality (VR) environment. Upon finishing a "bus ride" through the VR town along five landmarks, a router explained the route as well as the wayside landmarks to an unknown and naive follower. The SaGA Corpus was curated for CLARIN as part of the Curation Project "Editing and Integration of Multimodal Resources in CLARIN-D" by the CLARIN-D Working Group 6 "Speech and Other Modalities".
OPFLearnData
Full name: OPFLearnData: Dataset for Learning AC Optimal Power Flow

The datasets are resulting from OPFLearn.jl, a Julia package for creating AC OPF datasets. The package was developed to provide researchers with a standardized way to efficiently create AC OPF datasets that are representative of more of the AC OPF feasible load space compared to typical dataset creation methods. The OPFLearn dataset creation method uses a relaxed AC OPF formulation to reduce the volume of the unclassified input space throughout the dataset creation process.
The dataset contains load profiles and their respective optimal primal and dual solutions. Load samples are processed using AC OPF formulations from PowerModels.jl. More information on the dataset creation method can be found in our publication, "OPF-Learn: An Open-Source Framework for Creating Representative AC Optimal Power Flow Datasets" and in the package website: https://github.com/NREL/OPFLearn.jl.

Introducing paper: OPF-Learn: An Open-Source Framework for Creating Representative AC Optimal Power Flow Datasets
UV6K
Full name: Urban Vehicle Segmentation Dataset

UV6K is a high-resolution remote sensing urban vehicle segmentation dataset.

- Images: 6,313
- Vehicle: 245,141
- Resolution: 0.1m
- Image Size: 1024x1024