Omkar2118/datasets huggingface dataset:
PEnG
Full name: Pose-Enhanced Geo-Localisation

This dataset builds upon the SpaGBOL dataset - a graph-based dataset covering numerous cities across the globe for the purpose of structured city-scale Cross-View Geo-Localisation (CVGL).

We introduce the idea of Multi-View Geo-Localisation (MVGL) - localising in two steps within a graph. First using CVGL to retrieve the most likely primary nodes (road junctions), before performing relative pose estimation also candidate edges to increase precision. The corresponding initial network proposed reduced median error from hundreds of metres down to approximately 22m error across the entire dataset.

Introducing paper: PEnG: Pose-Enhanced Geo-Localisation
BRIGHT

BRIGHT is the first open-access, globally distributed, event-diverse multimodal dataset specifically curated to support AI-based disaster response. It covers five types of natural disasters and two types of man-made disasters across 14 disaster events in 23 regions worldwide, with a particular focus on developing countries.

It supports not only the development of supervised deep models, but also the testing of their performance on cross-event transfer setup, as well as unsupervised domain adaptation, semi-supervised learning, unsupervised change detection, and unsupervised image matching methods in multimodal and disaster scenarios.

Introducing paper: BRIGHT: A globally distributed multimodal building damage assessment dataset with very-high-resolution for all-weather disaster response
Prompted Textures Dataset

The Prompted Textures Dataset (PTD) is a synthetic texture image dataset consisting of 246,285 images across 56 different texture classes from the work [On Synthetic Texture Datasets: Challenges, Creation, and Curation](https://zenodo.org/records/14199831). PTD was created with the goal of better quantifying how models learn and respond to texture information when learning object classification or recognition tasks.

Introducing paper: On Synthetic Texture Datasets: Challenges, Creation, and Curation
Watch Your Mouth: Point Clouds based Speech Recognition Dataset

The Watch Your Mouth dataset is a custom silent speech dataset consisting of depth-only recordings of users silently mouthing full English sentences, captured using consumer-grade depth cameras such as the iPhone TrueDepth sensor. Sentences were carefully curated to cover diverse visemic and phonetic patterns, supporting the development of models capable of generalizing across varied speech content. Each sentence-level utterance provides a temporally aligned depth sequence and corresponding ground truth text. Please see more details in the paper Watch Your Mouth: Silent Speech Recognition with Depth Sensing.

Introducing paper: Watch Your Mouth: Silent Speech Recognition with Depth Sensing
Jute Pest

Description:

<a href="https://gts.ai/dataset-download/jute-pest/" target="_blank">👉 Download the dataset here</a>

This dataset comprises 17 distinct classes of agricultural pests, specifically targeting various insects and mites that affect Jute Pest The data is meticulously divided into three partitions: train, validation (val), and test sets, ensuring a robust framework for developing and evaluating machine learning models.

Download Dataset

Key Features

Comprehensive Coverage: The dataset includes images of 17 different pest classes, providing a broad spectrum for pest identification and classification.

Structured Partitions: Data is divided into training, validation, and testing sets, facilitating the development of accurate and generalizable models.

High-Quality Images: The dataset contains high-resolution images, ensuring the detailed features of each pest are captured, which is crucial for precise classification.

Usage

This dataset is ideal for:

Training Machine Learning Models: Suitable for developing and refining models aimed at pest detection and classification in agricultural settings.

Research on Pest Management: A valuable resource for studying pest behavior, distribution, and impact on crops, contributing to better pest management strategies.

Educational Purposes: Providing a rich dataset for educational projects in entomology, agriculture, and machine learning.

Additional Applications

Automated Pest Detection: Enhancing the capabilities of automated systems for early pest detection and management in agriculture.

Precision Agriculture: Supporting precision agriculture techniques by enabling targeted pest control measures based on accurate pest identification.

Cross-Domain Studies: Facilitating research on the generalization of pest detection models across different crops and agricultural environments.

This dataset is sourced from Kaggle.
PALMS

Data in this study come from western Ecuador's Choco tropical forest, including \textit{Fundación para la Conservación de los Andes Tropicales Reserve and adjacent Reserva Ecológica Mache-Chindul park} (FCAT; 00$^\circ$23'28'' N, 79$^\circ$41'05'' W), \textit{Jama-Coaque Ecological Reserve} (00$^\circ$06'57'' S, 80$^\circ$07'29'' W), \textit{Canande Reserve} (0$^\circ$31'34'' N 79$^\circ$12'47'' W), and \textit{Tesoro Escondido Reserve} (0$^\circ$33'16'' N 79$^\circ$10'31'' W). FCAT is a high diversity humid tropical forest at elevation $\sim$500m, receiving $\sim$3000 mm yr$^{-1}$ precipitation with persistent fog during drier period. Jama-Coaque ranges from the boundary of the tropical moist deciduous/tropical moist evergreen forest at the lower elevations ($\sim$1000 mm precipitation yr$^{-1}$, $\sim$250 m asl) to fog-inundated wet evergreen forests above 580m to 800m. Canande (350–500 m elevation) and Tesoro Escondido ($\sim$200 m elevation) are lowland everwet Choco forests, both receiving 4000–5000 mm yr$^{-1}$ precipitation with no month experiencing drought stress or precipitation below 100 mm. These forests host several palm species with exposed canopy crowns, including the economically important \textit{Iriartea deltoidea}, \textit{Socratea exorrhiza}, and \textit{Oenocarpus bataua}, with lesser amounts of \textit{Attalea colenda} and \textit{Astrocaryum standleyanum}, and species composition varying across study sites
Tech4Diet Cubes Dataset

This dataset consists of RGB-D images captured using 12 Intel RealSense cameras. Each image depicts a calibration cube placed in various positions and orientations within a controlled environment. The dataset is specifically designed for evaluating and benchmarking multi-view RGB-D camera calibration.

Introducing paper: Marker-Based Extrinsic Calibration Method for Accurate Multi-Camera 3D Reconstruction
C++ Codes and Complexities (TASTY)
Full name: C++ Codes and Complexities (TASTY)

This is the C++ dataset used in the [TASTY](https://arxiv.org/abs/2305.05379) research paper which was published at the ICLR DL4Code (Deep Learning for Code) workshop.

Introducing paper: TASTY: A Transformer based Approach to Space and Time complexity
RoboTAP

The RoboTAP dataset follows the same annotation format as TAP-Vid, but is released as an addition to TAP-Vid. In terms of domain, RoboTAP dataset is mostly similar to TAP-Vid-RGB-Stacking, with a key difference that all robotics videos are real and manually annotated. Video sources and object categories are also more diversified. The benchmark dataset includes 265 videos, serving for evaluation purpose only.

Introducing paper: RoboTAP: Tracking Arbitrary Points for Few-Shot Visual Imitation
Aneux

The AneuX morphology database includes data from 3 different data sources: AneuX, @neurIST and Aneurisk. The AneuX data consists of two portions AneuX1 and AneuX2, which have extracted by two different teams of data curators.

The datasets differ in the exact case selection, but represent a very similar patient cohort (patients examined or treated for ruptured or unruptured aneurysms at the

The dataset consists of totally 750 aneurysm domes. The geometries were all extracted from 3D rotational angiographies using different vessel lumen segmentation techniques.
RuTermEval (Track 1)
Full name: CL-RuTerm3

CL-RuTerm3 dataset is a novel resource featuring nested term annotations across six domains (the main one is computational linguistics, also mathematics, medicine, economics, literature studies, and agrochemistry), and the RuTermEval-2024 competition, designed to evaluate term extraction systems on this data. The CL-RuTerm3 dataset, comprising 1270 abstracts and 15 full-text articles (over 165k tokens with over 37k annotated entities), is the largest of its kind for Russian scientific texts. Terms are classified into three categories based on lexical and domain specificity: specific terms, common terms, and nomens. The dataset’s unique features, such as nested term markup and cross-domain coverage, enable more realistic evaluation of ATE systems. 

First track is devoted to Nested term extraction (in sequence labeling format) (also called Nested Term Identification).
RuTermEval (Track 2)
Full name: CL-RuTerm3

CL-RuTerm3 dataset is a novel resource featuring nested term annotations across six domains (the main one is computational linguistics, also mathematics, medicine, economics, literature studies, and agrochemistry), and the RuTermEval-2024 competition, designed to evaluate term extraction systems on this data. The CL-RuTerm3 dataset, comprising 1270 abstracts and 15 full-text articles (over 165k tokens with over 37k annotated entities), is the largest of its kind for Russian scientific texts. Terms are classified into three categories based on lexical and domain specificity: specific terms, common terms, and nomens. The dataset’s unique features, such as nested term markup and cross-domain coverage, enable more realistic evaluation of ATE systems. 

Second track is devoted to Nested term extraction (in sequence labeling format) and classification (labels are specific, common, nomen).
RuTermEval (Track 3)
Full name: CL-RuTerm3

CL-RuTerm3 dataset is a novel resource featuring nested term annotations across six domains (the main one is computational linguistics, also mathematics, medicine, economics, literature studies, and agrochemistry), and the RuTermEval-2024 competition, designed to evaluate term extraction systems on this data. The CL-RuTerm3 dataset, comprising 1270 abstracts and 15 full-text articles (over 165k tokens with over 37k annotated entities), is the largest of its kind for Russian scientific texts. Terms are classified into three categories based on lexical and domain specificity: specific terms, common terms, and nomens. The dataset’s unique features, such as nested term markup and cross-domain coverage, enable more realistic evaluation of ATE systems. 

Third track is devoted to Nested term extraction (in sequence labeling format) and classification (labels are specific, common, nomen) in cross-domain task.