Omkar2118/datasets huggingface dataset:
DBLP (Heterogeneous Node Classification)

A popular dataset for node classification on heterogeneous graphs.

Introducing paper: Are we really making much progress? Revisiting, benchmarking, and refining heterogeneous graph neural networks
IMDB (Heterogeneous Node Classification)

A popular dataset for node classification on heterogeneous graphs.

Introducing paper: Are we really making much progress? Revisiting, benchmarking, and refining heterogeneous graph neural networks
ACM (Heterogeneous Node Classification)

A popular dataset for node classification on heterogeneous graphs.

Introducing paper: Are we really making much progress? Revisiting, benchmarking, and refining heterogeneous graph neural networks
Freebase (Heterogeneous Node Classification)

A popular dataset for node classification on heterogeneous graphs.

Introducing paper: Are we really making much progress? Revisiting, benchmarking, and refining heterogeneous graph neural networks
OAG-Venue

A popular dataset for node classification on heterogeneous graphs.

Introducing paper: Heterogeneous Graph Transformer
OAG-L1-Field

A popular dataset for node classification on heterogeneous graphs.
LayoutBench-COCO - Number

LayoutBench-COCO is a diagnostic benchmark that examines layout-guided image generation models on arbitrary, unseen layouts. Unlike [LayoutBench](https://huggingface.co/datasets/j-min/layoutbench), LayoutBench-COCO consists of OOD layouts of real objects and suports zero-shot evaluation. LayoutBench-COCO measures 4 skills (Number, Position, Size, Combination), whose objects are from MS COCO. The new 'combination’ split consists of layouts with two objects in different spatial relations, and the remaining three splits are similar to those of LayoutBench. Download dataset at: https://huggingface.co/datasets/j-min/layoutbench-coco

Introducing paper: Diagnostic Benchmark and Iterative Inpainting for Layout-Guided Image Generation
LayoutBench-COCO - Position

LayoutBench-COCO is a diagnostic benchmark that examines layout-guided image generation models on arbitrary, unseen layouts. Unlike LayoutBench, LayoutBench-COCO consists of OOD layouts of real objects and suports zero-shot evaluation. LayoutBench-COCO measures 4 skills (Number, Position, Size, Combination), whose objects are from MS COCO. The new 'combination’ split consists of layouts with two objects in different spatial relations, and the remaining three splits are similar to those of LayoutBench. Download dataset at: https://huggingface.co/datasets/j-min/layoutbench-coco

Introducing paper: Diagnostic Benchmark and Iterative Inpainting for Layout-Guided Image Generation
LayoutBench-COCO - Size

LayoutBench-COCO is a diagnostic benchmark that examines layout-guided image generation models on arbitrary, unseen layouts. Unlike LayoutBench, LayoutBench-COCO consists of OOD layouts of real objects and suports zero-shot evaluation. LayoutBench-COCO measures 4 skills (Number, Position, Size, Combination), whose objects are from MS COCO. The new 'combination’ split consists of layouts with two objects in different spatial relations, and the remaining three splits are similar to those of LayoutBench. Download dataset at: https://huggingface.co/datasets/j-min/layoutbench-coco

Introducing paper: Diagnostic Benchmark and Iterative Inpainting for Layout-Guided Image Generation
ViMedical_Disease

This dataset contains over 12K+ questions and symptoms related to various common diseases in Vietnamese. It's designed to aid in the classification of medical symptoms and provide preliminary disease identification. The dataset covers a wide range of diseases, including cardiovascular, digestive, neurological, dermatological, endocrine, and others.

For more information and updates about the dataset, please refer to the **main repository** [here](https://github.com/PB3002/ViMedical_Disease).

## This dataset can be used for:
- Data analysis
- Building disease prediction models
- Creating chatbots
- Providing information to users

### The dataset has two columns:
**`Disease`**:  The name of the disease in Vietnamese.
**`Question`**:  Questions and descriptions of disease symptoms in Vietnamese, often posed as a query seeking information about a possible diagnosis.

## **Important Notes:**
This dataset provides information on disease symptoms, not official medical diagnoses.
Users should consult a doctor for proper diagnosis and treatment.
LayoutBench-COCO - Combination

LayoutBench-COCO is a diagnostic benchmark that examines layout-guided image generation models on arbitrary, unseen layouts. Unlike LayoutBench, LayoutBench-COCO consists of OOD layouts of real objects and suports zero-shot evaluation. LayoutBench-COCO measures 4 skills (Number, Position, Size, Combination), whose objects are from MS COCO. The new 'combination’ split consists of layouts with two objects in different spatial relations, and the remaining three splits are similar to those of LayoutBench. Download dataset at: https://huggingface.co/datasets/j-min/layoutbench-coco

Introducing paper: Diagnostic Benchmark and Iterative Inpainting for Layout-Guided Image Generation
FDMSE-ISL

A large-scale isolated Indian sign language dataset. It contains 2002 common words, used in daily communications among Indian deaf community.  The dataset contains 40033  videos across  2002  words. The total duration of the dataset is around 36.2 hours with 7.8 Million frames.

Introducing paper: Hierarchical Windowed Graph Attention Network and a Large Scale Dataset for Isolated Indian Sign Language Recognition
ViMirr

we construct the ViMirr dataset, which has 19,255 frames from 276 videos
UT-GLOBUS
Full name: GLObal Building heights for Urban Studies

We introduce GLObal Building heights for Urban Studies (UT-GLOBUS), a dataset providing building heights and urban canopy parameters (UCPs) for major cities worldwide. UT-GLOBUS combines open-source spaceborne altimetry (ICESat-2 and GEDI) and coarse resolution urban canopy elevation data with a random forest model to estimate building-level information. Validation using LiDAR data from six U.S. cities showed UT-GLOBUS-derived building heights had an RMSE of 9.1 meters, and mean building height within 1-km² grid cells had an RMSE of 7.8 meters. Testing the UCPs in the urban Weather Research and Forecasting (WRF-Urban) model resulted in a significant improvement (~55% in RMSE) in intra-urban air temperature representation compared to the existing table-based local climate zone approach in Houston, TX. Additionally, we demonstrated the dataset's utility for simulating heat mitigation strategies and building energy consumption using WRF-Urban, with test cases in Chicago, IL, and Austin, TX. Street-scale mean radiant temperature simulations using the SOlar and LongWave Environmental Irradiance Geometry (SOLWEIG) model, incorporating UT-GLOBUS and LiDAR-derived building heights, confirmed the dataset’s effectiveness in modeling human thermal comfort at Baltimore, MD (daytime RMSE = 2.85°C). Thus, UT-GLOBUS can be used for modeling urban hazards with significant socioeconomic and ecological risks, enabling finer scale urban climate simulations and overcoming previous limitations due to the lack of building information.

Introducing paper: GLObal Building heights for Urban Studies (UT-GLOBUS) for city- and street- scale urban simulations: Development and first applications
Paderbone University Bearing Fault Benckmark

This paper presents a benchmark data set for condition monitoring of rolling bearings in combination with an extensive description of the corresponding bearing damage, the data set generation by experiments and results of datadriven classifications used as a diagnostic method. The diagnostic method uses the motor current signal of an electromechanical drive system for bearing diagnostic. The advantage of this approach in general is that no additional sensors are required, as current measurements can be performed in existing frequency inverters. This will help to reduce the cost of future condition monitoring systems. A particular novelty of the present approach is the monitoring of damage in external bearings which are installed in the drive system but outside the electric motor. Nevertheless, the motor current signal is used as input for the detection of the damage. Moreover, a wide distribution of bearing damage is considered for the benchmark data set. The results of the classifications show that the motor current signal can be used to identify and classify bearing damage within the drive system. However, the classification accuracy is still low compared to classifications based on vibration signals. Further, dependency on properties of those bearing damage that were used for the generation of training data are observed, because training with data of artificially generated and real bearing damages lead to different accuracies. Altogether a verified and systematically generated data set is presented and published online for further research
Hawk Annotation Dataset

Hawk Annotation Dataset includes language descriptions specifically for anomaly scenes in seven existing video anomaly datasets. These seven datasets include a variety of anomalous scenarios such as crime (UCF-Cirme), campus (ShanghaiTech and CUHK Avenue), pedestrian walkways (UCSD Ped1 and Ped2), traffic (DoTA), and human behavior (UBnormal). With the support of these visual scenarios, this dataset can perform comprehensive fine-tuning for various abnormal scenarios, being closer to open-world scenarios.

Introducing paper: Hawk: Learning to Understand Open-World Video Anomalies
MultiOOD
Full name: Multimodal Out-of-Distribution Detection Benchmark

MultiOOD is the first benchmark for Multimodal OOD Detection and covers diverse dataset sizes and modalities. MultiOOD comprises five video datasets with over 85, 000 video clips in total. The datasets vary in the number of classes, ranging from 7 to 229, and in size, spanning from 3k to 57k. Video, optical flow, and audio are used as different types of modalities.

Introducing paper: MultiOOD: Scaling Out-of-Distribution Detection for Multiple Modalities
ENST Drums
Full name: ENST-Drums: an extensive audio-visual database for drum signals processing

ENST-Drums: an extensive audio-visual database for drum signals processing
Olivier Gillet and Gaël Richard
GET / ENST, CNRS LTCI, 37 rue Dareau, 75014 Paris, France

The ENST-Drums database is a large and varied research database for automatic drum transcription and processing:

Three professional drummers specialized in different music genres were recorded.
Total duration of audio material recorded per drummer is around 75 minutes.
Each drummer played his own drum kit.
Each sequence used either sticks, rods, brushes or mallets to increase the diversity of drum sounds.
The drum kits themselves are varied, ranging from a small, portable, kit with two toms and 2 cymbals, suitable for jazz and latin music ; to a larger rock drum set with 4 toms and 5 cymbals.
Each sequence is recorded on 8 individual audio channels, is filmed from two angles, and is fully annotated
MuSoHu
Full name: Toward human-like social robot navigation: A large-scale, multi-modal, social human navigation dataset

A large-scale, egocentric, multimodal, and context-aware dataset of human demonstrations of social navigation.

Provide:

MuSoHu contains approximately 20 hours, 300 trajectories, 100 kilometers of socially compliant navigation demonstrations collected by 13 human demonstrators that comprise multimodal data streams from different sensors, in both indoor and outdoor environments within the George Mason University campus and the Washington DC metropolitan area. 
MuSoHu also provide annotations of interesting social interaction events and of the navigation contexts (i.e., “casual”,”neutral”, and “rush”) for each of the trials.
PetFace

PetFace is a large-scale animal face re-identification dataset that includes 257,484 unique individuals across 13 families and 319 breeds. 
PetFace has fine-grained annotation (sex, breeds, color, and patterns).

Introducing paper: PetFace: A Large-Scale Dataset and Benchmark for Animal Identification