Omkar2118/datasets huggingface dataset:
OptMATH-Train

URL:https://huggingface.co/datasets/Aurora-Gem/OptMATH-Train

Introducing paper: OptMATH: A Scalable Bidirectional Data Synthesis Framework for Optimization Modeling
Spanish Sign Language Alphabet

Description:

<a href="https://gts.ai/dataset-download/spanish-sign-language-alphabet/" target="_blank">👉 Download the dataset here</a>

The Spanish Sign Language dataset consists of 19 static letters and 8 dynamic movements. It was created with the aim of supporting machine learning models for sign language recognition, particularly the Spanish sign language alphabet.

Dataset Composition

The dataset contains around 100 images for each of the 19 static letters. Each image was captured over a white background with high-quality cameras. These images represent three different hands at a uniform distance, simulating the perspective from an internal smartphone camera.

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Expert Validation

To ensure high accuracy, the dataset was reviewed and validated by a certified expert in Spanish sign language. This guarantees that each letter is correctly represented, making the dataset a valuable resource for machine learning applications.

Applications

This dataset is ideal for training models in:

Gesture and static sign language recognition

Computer vision projects focused on human-computer interaction

Accessibility technology development for the hearing-impaired community

Benefits of the Dataset

High-quality, standardized images

Multiple hand perspectives ensure model robustness

Realistic simulation of smartphone camera view for practical applications

Suitable for researchers focusing on AI, sign language translation, and accessibility solutions

Potential Use Cases

AI and Machine Learning: Can be used to train AI models for recognizing static signs.

Computer Vision: Enhances projects in gesture recognition and human-computer interaction.

Accessibility Technology: Advances sign language translation for hearing-impaired users.

This dataset is sourced from Kaggle.
Khaadi Fashion Dataset

Description:

<a href="https://gts.ai/dataset-download/khaadi-fashion-dataset/" target="_blank">👉 Download the dataset here</a>

This dataset offers a detailed collection of Khaadi’s fashion line, providing a wide variety of high-quality images and metadata of clothing items such as dresses, shirts, and trousers. It is a powerful resource for exploring fashion trends, customer preferences, and product analysis.

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Applications in Fashion Industry

This dataset is ideal for applications in retail analytics, trend identification, and predictive modeling. It supports tasks like developing personalized recommendation systems, inventory management, and understanding customer buying behavior. Businesses can utilize it to optimize their fashion offerings, enhance customer satisfaction, and increase sales by aligning with trends and preferences.

Machine Learning Applications

This dataset supports various machine learning and AI models, especially in the areas of image classification, visual search engines, and style-based recommendations. It can be used for training convolutional neural networks (CNNs) to recognize patterns in clothing styles, materials, and other relevant features, helping to build automated systems that assist in inventory categorization, demand prediction, and customer recommendations.

Conclusion

The Khaadi Fashion Dataset is a versatile and powerful resource for both the fashion and tech industries. Whether you’re a retailer looking to enhance your customer offerings or a machine learning practitioner aiming to build robust models, this dataset equips you with the essential data for an in-depth analysis of fashion products.

This dataset is sourced from Kaggle.
Linear Equation Image Dataset

Description:

<a href="https://gts.ai/dataset-download/linear-equation-image-dataset/" target="_blank">👉 Download the dataset here</a>

The Linear Equation Image Dataset is designed to help solve high school-level algebraic problems using machine learning (ML). It provides extensive visual data, ideal for training models in equation recognition and solving.

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What’s New

Expanded Image Dataset: The dataset now contains over 30,000 images, covering a wide array of linear equations with varying complexities. The generation of equations follows multiple randomization techniques, ensuring diversity in the visual representation.

Data Diversity: Equations include both simple and complex forms, with some involving fractional coefficients, inequalities, or multi-variable formats to increase the challenge. The images 
also come in different resolutions, fonts, and formats (handwritten and digitally rendered) to further test ML algorithms’ robustness.

Possible Use Cases

Symbolic Equation Recognition: Train models to visually recognize equations and convert them into symbolic form.

Equation Solving: Create ML models capable of solving linear equations through image recognition.

Handwritten Recognition: Use this dataset for handwriting recognition, helping machines interpret handwritten linear equations.

Educational Tools: Develop AI tutors or mobile apps that assist students in solving linear equations by merely taking a photo of the problem.

Algorithm Training: Useful for those researching symbolic computation, this dataset allows for testing and improving various image-to-text and equation-solving algorithms.

Enhanced Research Opportunities

This dataset can be particularly useful for educational institutions, research teams, and AI developers focusing on enhancing problem-solving capabilities via machine learning and symbolic computation models.

This dataset is sourced from Kaggle.