Omkar2118/datasets huggingface dataset:
WyzeRule

Wyze Rule Recommendation Dataset. It is a big dataset with 300,000 users. Please cite [1] if you used the dataset and cite [2] if you referenced the algorithm.

[1] Wyze Rule: Federated Rule Dataset for Rule Recommendation Benchmarking. NeurIPS Dataset Track 2023

[2] FedRule: Federated Rule Recommendation System with Graph Neural Networks. IoTDI 2023

Introducing paper: FedRule: Federated Rule Recommendation System with Graph Neural Networks
SceneEgo
Full name: SceneEgo Test Dataset

Evaluating human-scene interaction requires precise annotations for camera pose and scene geometry. However, such information is not available in existing datasets for egocentric human pose estimation. To solve this issue, we collected a new real-world dataset using a head-mounted fisheye camera combined with a calibration board. The ground truth scene geometry is obtained with the SfM method from a multi-view capture system with 120 synced 4K resolution cameras and the ground truth egocentric camera pose is obtained by localizing a calibration board rigidly attached to the egocentric camera. This dataset contains around 28K frames of two actors, performing various human-scene interacting motions such as sitting, reading a newspaper, and using a computer. This dataset is evenly split into training and testing splits. We fine-tuned the method on the training split before the evaluation. This dataset will be made publicly available and additional details of it are shown in the supplementary materials.

Introducing paper: Scene-aware Egocentric 3D Human Pose Estimation
EgoPW-Scene

## EgoPW training dataset with scene annotations

Since we want to generalize to data captured with a real head-mounted camera, we also extended the EgoPW training dataset. For this, we first reconstruct the scene geometry from the egocentric image sequences of the EgoPW training dataset with a Structure-from-Motion (SfM) algorithm. This step provides a dense reconstruction of the background scene. The global scale of the reconstruction is recovered from known objects present in the sequences, such as laptops and chairs. We further render the depth maps of the scene in the egocentric perspective based on the reconstructed geometry. Our EgoPW-Scene dataset contains 92 K frames in total, which are distributed in 30 sequences performed by 5 actors.

Introducing paper: Scene-aware Egocentric 3D Human Pose Estimation
GlobalEgoMocap Test Dataset

Egocentric motion capture dataset

Introducing paper: Estimating Egocentric 3D Human Pose in Global Space
PINS100

We assembled a benchmark of electronic component pinouts, PINS100, containing 100 common parts frequently used in circuits found on high-traffic electronic tutorial websites such as the ARDUINO PROJECT HUB and AUTODESK TINKERCAD CIRCUITS. Components range from 2 pins to 40 pins, and span a large assortment of part categories including passives (e.g. resistors/capacitors), input (e.g. switches), output (e.g. LEDs, motors, relays), sensors, integrated circuits, power regulators, logic (e.g. 7400- SERIES AND and OR gates), and microcontrollers (e.g ARDUINO, RASPBERRY PI ).

Introducing paper: From Words to Wires: Generating Functioning Electronic Devices from Natural Language Descriptions
MICRO25

To assess a model’s ability to create microcontroller-driven electronic devices, we developed a benchmark, MICRO25, that includes 25 tasks intended for the common ARDUINO microcontroller ecosystem.. These tasks, shown in Table 2, span 5 core categories including: input, interface protocols, output, sensors, and logic. Each task is either tailored to test a specific fundamental competency required to build basic microcontroller-driven electronic devices, or the integration of several competencies into larger design flows.

Introducing paper: From Words to Wires: Generating Functioning Electronic Devices from Natural Language Descriptions
CaBuAr
Full name: CaBuAr: California Burned Areas dataset

This dataset contains images from Sentinel-2 satellites taken before and after a wildfire. 
The ground truth masks are provided by the California Department of Forestry and Fire Protection and they are mapped on the images. 
The dataset is designed to do binary semantic segmentation of burned vs unburned areas.

Introducing paper: CaBuAr: California Burned Areas dataset for delineation