Omkar2118/datasets huggingface dataset:
Indonesian Wayang Type

Description:

<a href="https://gts.ai/dataset-download/indonesian-wayang-type/" target="_blank">👉 Download the dataset here</a>

Indonesia’s wayang tradition is a testament to its deep cultural history. However, with modern influences and a fading connection to traditional art forms, wayang puppetry risks being forgotten. The “Dalang” project seeks to bridge the gap between tradition and technology, making it easier to classify and understand the various types of wayang through machine learning.

The Importance of Wayang

Wayang is more than just puppetry; it’s a representation of Indonesian folklore, philosophy, and societal values. Each type of wayang tells a different story and has distinct characteristics, making its preservation essential for future generations. Wayang performances are intricate, often combining shadow play, narration, and music to depict age-old legends.

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Dataset Goals

This dataset is specifically designed to aid in the classification and recognition of six prominent types of wayang. The project aims to collect diverse and high-quality images for each class, providing a comprehensive resource for machine learning applications and cultural studies.


Dataset Collection Process

The dataset was carefully curated from public sources such as Google Images and Bing Images. Our objective was to gather high-resolution, diverse images to ensure accurate classification across different wayang types. All images are strictly for educational purposes, acknowledging the ownership of their respective sources.


Usage and Application

This dataset can be utilized to train AI models that help automate the recognition and classification of Indonesian wayang puppets. In addition to its cultural preservation mission, the dataset has broader applications in educational tools, virtual museums, and academic research into Southeast Asian culture.


Technological Approach

Using the “Dalang” system, machine learning models analyze wayang images to classify them based on type, origin, and other metadata. The system uses deep learning to recognize distinct patterns in the puppets, such as shape, design, and features, contributing to a comprehensive cultural database.

 
Conclusion

The Indonesian wayang dataset is not just a collection of images; it represents a critical step in preserving and reviving a fading tradition. By leveraging modern technology, we aim to keep wayang puppetry relevant and accessible for generations to come, fostering global recognition and appreciation.


This dataset is sourced from
Cow Segmentation Dataset

Description:

<a href="https://gts.ai/dataset-download/cow-segmentation-dataset/" target="_blank">👉 Download the dataset here</a>

The Cow Segmentation Dataset is a comprehensive resource designed for segmentation tasks in machine learning. It features a wide variety of cow images annotated in the COCO format, ensuring compatibility with a range of popular machine learning models, including YOLOv8, Mask R-CNN, and others. This dataset empowers researchers and developers to train robust models for recognizing and segmenting cows in various contexts, revolutionizing AI-driven applications in agriculture.

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The dataset consists of high-resolution images that capture cows from different angles, poses, and environments. With detailed annotations in the COCO format, each image is segmented to highlight the cow’s body, enabling precise object recognition. The segmentation data is easily adaptable to models used in advanced image processing tasks, making it a highly flexible resource.

Applications in Agriculture

This dataset offers diverse applications in smart farming, such as automated cow monitoring, health diagnostics, and livestock management. It supports real-time systems for tracking cow behavior, analyzing health indicators, and managing livestock populations effectively. The segmentation accuracy helps in building AI models that can contribute to precision farming, reducing manual efforts and improving overall productivity.

Use Cases and Future Potential

Livestock Management: Automating tasks like cow counting, posture analysis, and herd management using AI-driven systems.

Health Monitoring: Identifying physical conditions like lameness or injury through detailed image segmentation.

Herd Behavior Analysis: Real-time behavior tracking using models trained on various cow positions and movements.

Benefits of Using the Dataset

Diverse Annotations: A rich set of segmentation masks for various cow breeds and environments.

Model Compatibility: Ready-to-use COCO annotations for easy integration into advanced machine learning models.

Real-World Applications: Supports the development of AI systems for real-time livestock monitoring and analysis.

This dataset is sourced from Kaggle.
Drone Garbage Detection Dataset H

Description:

<a href="https://gts.ai/dataset-download/drone-garbage-detection-dataset/" target="_blank">👉 Download the dataset here</a>

This comprehensive dataset is designed for the classification, detection, and analysis of waste materials found in coastal areas, specifically for environmental monitoring and recycling efforts. It contains drone-captured images of various waste types scattered on beaches, making it an invaluable resource for training machine learning models. The dataset is structured to seamlessly integrate with the YOLOv8 object detection model.

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Key Applications

Recycling Automation: The dataset supports the development of systems that can automate the sorting of recyclable waste materials, significantly reducing manual labor and enhancing recycling efforts.

Environmental Monitoring: The drone-captured images provide real-time data that can be used for monitoring pollution levels in coastal areas, helping organizations to track waste accumulation and its environmental impact.

Sustainability Research: With detailed waste categorization, researchers can use this dataset to analyze trends in waste generation, informing future waste management strategies.
Dataset Structure

The dataset is divided into three key subsets:

Training Set: Includes the majority of the images, used to train machine learning models.

Testing Set: Reserved for testing the trained models to assess performance.

Validation Set: Used for fine-tuning models and preventing overfitting during training.

Image and Annotation Details

Format: All images are in JPEG format for easy integration with image-processing libraries.

Resolution: The images are high-resolution, allowing for detailed object detection.

Annotation: Each image comes with corresponding labels that define the waste category, making it suitable for object detection models.

Naming Convention: The image file names follow a structured format, indicating the source video and frame number from which they were extracted. This allows users to easily trace the original context of the waste material captured by the drone.

Conclusion

This drone garbage detection dataset is a valuable resource for both AI researchers and environmental scientists. By enabling accurate waste classification and trend analysis, it paves the way for better waste management strategies and contributes to global sustainability efforts.

This dataset is sourced from Kaggle.
PrideMM

PrideMM comprising 5,063 text-embedded images associated with the LGBTQ+ Pride movement

Introducing paper: MemeCLIP: Leveraging CLIP Representations for Multimodal Meme Classification
Harm-P

Harm-P contains around 3,000 memes related to US politics.

Introducing paper: MOMENTA: A Multimodal Framework for Detecting Harmful Memes and Their Targets
Street360Loc

Text-Vison Cross-Modal Place Recognition Dataset

Introducing paper: Bridging Text and Vision: A Multi-View Text-Vision Registration Approach for Cross-Modal Place Recognition