Omkar2118/datasets huggingface dataset:
TURSpider
Full name: TURSpider: A Turkish Text-to-SQL Dataset

TURSpider is a novel Turkish Text-to-SQL dataset that includes complex queries, akin to those in the original Spider dataset. TURSpider dataset comprises two main subsets: a dev set and a training set, aligned with the structure and scale of the popular Spider dataset. The dev set contains 1034 data rows with 1023 unique questions and 584 distinct SQL queries. In the training set, there are 8659 data rows, 8506 unique questions, and corresponding SQL queries.
LeafNet
Full name: LeafNet: A large-scale dataset for training image-text models in leaf disease identification

The PlantVillage dataset, with over 54,000 images spanning 14 plant species and 26 disease types, has been widely used for leaf disease classification. However, it is limited in both scale and diversity. To address these limitations, we developed LeafNet, a large-scale dataset designed to support foundation models for leaf disease diagnosis. LeafNet comprises over 186,000 images from 22 crop species, covering 43 fungal diseases, 8 bacterial diseases, 2 mould (oomycete) diseases, 6 viral diseases, and 3 mite-induced diseases, categorized into 97 classes. The dataset was meticulously collected and processed to minimize intra-class variations while ensuring clarity by maintaining a consistent imaging distance. The disease symptom descriptions were curated from reputable sources, including UME, NIH, and published studies, providing high-quality annotations to support AI-driven plant pathology research.

Introducing paper: A Vision-Language Foundation Model for Leaf Disease Identification
Tiny Terrors: A Collection of Ticks and Mites

Description:

<a href="https://gts.ai/dataset-download/tiny-terrors-a-collection-of-ticks-and-mites/" target="_blank">👉 Download the dataset here</a>

This dataset features high-quality images of ticks and mites, providing a valuable resource for researchers, educators, and enthusiasts. Each image highlights the unique characteristics and diversity of these significant yet tiny arthropods.

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Dataset Contents

Ticks: These small arachnids are known for their role as vectors of various diseases. They attach themselves to hosts to feed on blood.

Mites: Mites are incredibly diverse and can be found in various habitats. Some are free-living, while others are parasitic.

Usage

This dataset is ideal for:

Machine learning and computer vision projects

Entomological and acarological studies

Educational purposes

Public health research

Data Structure

This dataset contains the following subsets:

train: This directory contains training images organized into subfolders. Each subfolder represents a class, with the class name as the directory name.

test: This directory contains testing images organized into subfolders. Each subfolder represents a class, with the class name as the directory name.

Source

The images in this dataset were sourced from insectimages.org, ensuring a rich and diverse collection.

This dataset is sourced from Kaggle.
FLUXSynID

The FLUXSynID Dataset comprises 14,889 high-resolution synthetic face identities, each uniquely represented with paired images: document-style images and trusted live-capture images. The document-style images are designed to simulate ID photos, while the live-capture images are generated to reflect real-world conditions, including variations in lighting, facial expressions, and camera angles. This dual representation facilitates robust benchmarking for face recognition, morphing attack detection, and cross-domain identity verification tasks. FLUXSynID leverages cutting-edge generative models to ensure high fidelity and diversity across identities, enabling comprehensive analysis of biometric systems under varied conditions.

For more information and to generate your own custom datasets using the FLUXSynID framework, visit the [FLUXSynID GitHub repository](https://github.com/Raul2718/FLUXSynID).

Introducing paper: FLUXSynID: A Framework for Identity-Controlled Synthetic Face Generation with Document and Live Images
Retinal Disease Classification

Description:

<a href="https://gts.ai/dataset-download/retinal-disease-classification/" target="_blank">👉 Download the dataset here</a>

Retinal Disease Classification According to the WHO’s 2019 World Report on Vision, an estimated 2.2 billion people worldwide suffer from visual impairments, with at least 1 billion cases being preventable or yet to be addressed. The global eye care landscape faces significant challenges, including disparities in the accessibility and quality of prevention, treatment, and rehabilitation services. Early detection and diagnosis of ocular pathologies are crucial for preventing visual impairment.

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To address this need, we have developed the Retinal Fundus Multi-disease Image Dataset (RFMiD), comprising 3,200 fundus images captured with three different fundus cameras. This dataset includes annotations for 46 conditions, verified through the consensus of two senior retinal experts. This resource aims to support advancements in the early detection and diagnosis of eye diseases, ultimately improving eye care outcomes worldwide.

This dataset is sourced from Kaggle.
Potato Plant Diseases Data

Description:

<a href="https://gts.ai/dataset-download/potato-plant-diseases-data/" target="_blank">👉 Download the dataset here</a>

This dataset comprises high-resolution images of Potato Plants Diseases Data, capturing various conditions including early blight, late blight, and healthy leaves. Curated specifically for research and development, it serves as a valuable resource for creating and testing image recognition models aimed at accurate disease detection and classification. The dataset is designed to facilitate advancements in agricultural diagnostics, enabling more efficient and precise disease management in potato crops.

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Key Features of the Potato Plant Disease Dataset:

High-Resolution Images: The dataset includes clear, detailed images that provide an excellent foundation for training robust image recognition models.

Diverse Conditions: It covers a range of disease states, including early blight and late blight, alongside images of healthy leaves for comprehensive analysis.

Expert Curation: The images have been carefully selected and labeled by agricultural experts to ensure accuracy and reliability.

Versatile Applications: Ideal for use in developing machine learning models, this dataset can be utilized for various applications, such as disease classification, severity assessment, and early detection systems.

Promoting Agricultural Innovation: By providing a high-quality resource for researchers and developers, this dataset supports innovations in agricultural technology, contributing to better crop management and increased yields.

Applications of the Dataset:

Machine Learning Research: Develop and refine image recognition algorithms for disease detection in potato plants.

Precision Agriculture: Implement advanced diagnostic tools in agricultural practices to monitor plant health and manage diseases effectively.

Educational Purposes: Use the dataset in academic settings to teach and demonstrate image processing and machine learning techniques in agriculture.

Extension Services: Assist farmers and agricultural advisors in identifying and managing potato diseases through mobile applications and other diagnostic tools.

This dataset is sourced from Kaggle.
Sokoto Coventry Fingerprint Dataset (SOCOFing)

Description:
 
<a href="https://gts.ai/dataset-download/sokoto-coventry-fingerprint-dataset-socofing/" target="_blank">👉 Download the dataset here</a>

The Sokoto Coventry Fingerprint Dataset (SOCOFing) is a comprehensive biometric fingerprint database created specifically for academic research. It consists of 6,000 fingerprint images collected from 600 African subjects. Each fingerprint is meticulously labeled with unique attributes, including gender, hand, and finger name. Additionally, the dataset features synthetically altered versions of the fingerprints, offering three distinct levels of alteration: obliteration, central rotation, and z-cut.

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Key Features of SOCOFing:

Diverse Subject Pool: The dataset includes fingerprints from 600 subjects, providing a rich variety of biometric data for research.

Detailed Labeling: Each fingerprint image is labeled with specific metadata, such as gender, hand (left or right), and finger name (e.g., thumb, index).

Synthetic Alterations: To facilitate robust testing and analysis, the dataset includes synthetically modified fingerprints. These alterations simulate real-world challenges in fingerprint recognition:

Obliteration: Partial removal or smudging of the fingerprint pattern.

Central Rotation: Rotation of the central part of the fingerprint to mimic common distortions.

Z-Cut: A synthetic cut through the fingerprint pattern to test resilience against physical damage.

Applications of SOCOFing:

Biometric System Development: Researchers can use this dataset to develop and enhance fingerprint recognition algorithms.

Security and Forensics: The dataset is valuable for studying the effects of alterations on fingerprint recognition, aiding in the development of more secure and reliable forensic tools.

Academic Research: SOCOFing serves as a benchmark for academic research in biometrics, providing a standardized dataset for testing new methodologies and comparing results.

This dataset is sourced from Kaggle.
StoryReasoning

Visual storytelling systems struggle to maintain character identity across frames and link actions to appropriate subjects, frequently leading to referential hallucinations. These issues can be addressed through grounding of characters, objects, and other entities on the visual elements. We propose StoryReasoning, a dataset containing 4,178 stories derived from 52,016 movie images, with both structured scene analyses and grounded stories. Each story maintains character and object consistency across frames while explicitly modeling multi-frame relationships through structured tabular representations. Our approach features cross-frame object re-identification using visual similarity and face recognition, chain-of-thought reasoning for explicit narrative modeling, and a grounding scheme that links textual elements to visual entities across multiple frames. We establish baseline performance by fine-tuning Qwen2.5-VL 7B, creating Qwen Storyteller, which performs end-to-end object detection, re-identification, and landmark detection while maintaining consistent object references throughout the story. Evaluation demonstrates a reduction from 4.06 to 3.56 (-12.3%) hallucinations on average per story when compared to a non-fine-tuned model.

Introducing paper: StoryReasoning Dataset: Using Chain-of-Thought for Scene Understanding and Grounded Story Generation
Landslide-dam-dataset-for-LDBF

A landslide dataset for LDBF predictions.
bespokelabs/Bespoke-Stratos-17k

Bespoke-Stratos-17k
We replicated and improved the Berkeley Sky-T1 data pipeline using SFT distillation data from DeepSeek-R1 to create Bespoke-Stratos-17k -- a reasoning dataset of questions, reasoning traces, and answers.

This data was used to train:

Bespoke-Stratos-32B, a 32B reasoning model which is a fine-tune of Qwen-2.5-32B-Instruct
Bespoke-Stratos-7B, a 7B reasoning model which is a fine-tune of Qwen-2.5-7B-Instruct.
NuminaMath-TIR
Full name: AI-MO/NuminaMath-7B-TIR

Model Card for NuminaMath 7B TIR
NuminaMath is a series of language models that are trained to solve math problems using tool-integrated reasoning (TIR). NuminaMath 7B TIR won the first progress prize of the AI Math Olympiad (AIMO), with a score of 29/50 on the public and private tests sets.
SynthCheX-75K

A dataset consisting of high-quality, synthetic chest X-rays from the CheXGenBench-benchmark leading model, Sana (0.6B). The dataset has been filtered to contain using High Quality samples using HealthGPT.

Introducing paper: CheXGenBench: A Unified Benchmark For Fidelity, Privacy and Utility of Synthetic Chest Radiographs