Omkar2118/datasets huggingface dataset:
Bicycle Image Dataset Vehicle Detection

Description:

<a href="https://gts.ai/dataset-download/bicycle-image-dataset-vehicle-detection/" target="_blank">👉 Download the dataset here</a>

Our bicycle image dataset is a rich collection of over 5,000 images specifically curated to aid in the development of advanced computer vision algorithms. This dataset is uniquely diverse, with images captured from real-world environments, making it a valuable resource for researchers and developers working on bicycle detection and vehicle classification.

Dataset Overview

This dataset comprises high-quality images of bicycles in various orientations, lighting conditions, and environments. Each image has been manually verified by experts to ensure consistency and quality, making it suitable for a wide range of AI applications.

Download Dataset

Key Dataset Features

Dataset Size: 5,000+ high-resolution images

Contributors: Sourced from over 3,000 crowdsourcing participants globally

Resolution: HD and above, with most images at 1920×1080 resolution and higher

Geographic Diversity: Images collected from more than 3,000 distinct locations worldwide

Device Variety: Captured using smartphones, DSLR cameras, and other devices

Environmental Variations: Includes images in different lighting conditions (day/night, artificial/natural light), weather conditions, and perspectives (e.g., front view, side view, close-up, and distant shots)

Data Applications

This dataset is ideal for:

Bicycle Detection: Training AI models to recognize bicycles in urban, rural, and off-road environments

Vehicle Classification: Differentiating between two-wheelers and other vehicles

Autonomous Systems: Improving the detection algorithms for autonomous driving systems

Urban Planning: Analyzing bicycle traffic patterns and supporting city infrastructure development

Transportation Research: Understanding bicycle movement in different contexts, including city traffic, pedestrian paths, and remote areas

Unique Dataset Benefits

This dataset stands out due to its:

Crowdsourced Accuracy: Every image is carefully reviewed by computer vision professionals, ensuring high annotation quality and relevance.

Wide Variety of Scenarios: From busy urban streets to quiet country roads, this dataset covers an array of scenarios that challenge detection algorithms.

Scalability: The large number of contributors and varied locations make this dataset an invaluable resource for scalable machine learning models.

How It Can Be Used

This dataset is perfect for training deep learning models to recognize bicycles and other two-wheeled vehicles in complex environments. It’s a powerful tool for industries focused on smart cities, traffic management, autonomous vehicles, and public transportation.

This dataset is sourced from Kaggle.
Self Compounded Devanagari Characters

Description:

<a href="https://gts.ai/dataset-download/self-compounded-devanagari-characters/" target="_blank">👉 Download the dataset here</a>

The Self-Compounded Devanagari Characters dataset focuses on a crucial aspect of Optical Character Recognition (OCR) for Devanagari script, essential for preserving ancient scriptures and making them more accessible in the digital age. By leveraging this dataset, researchers can enhance AI systems to recognize complex Devanagari characters accurately. Digitalization also makes handwritten text “web-friendly” and searchable, preserving knowledge that would otherwise risk being lost or damaged.

Purpose of the Dataset

While OCR technology for Devanagari exists, one major gap has been the recognition of compound characters—those consisting of a half letter and a full letter. This dataset specifically addresses this gap, aiding researchers, developers, and academic institutions in creating systems that can accurately detect and digitize these characters, thus preserving not only the text but also the language itself.

Download Dataset

Dataset Composition

The dataset primarily consists of compound Devanagari characters, which are essential for enhancing OCR systems for Devanagari script. Each entry was carefully cleaned and validated before inclusion in the final dataset. This cleaning process ensures that the dataset is ready for immediate use in research and development of AI models, specifically those focused on Devanagari OCR.

Applications of the Dataset

This dataset has several potential applications:

Academic Research: Aiding studies in linguistics, script recognition, and AI for ancient language preservation.

AI & Machine Learning: Training OCR models to improve recognition of complex Devanagari script.

Language Digitization: Helping in the digital preservation of sacred texts, manuscripts, and other handwritten documents.

Collaborative Development: Open-source software available for expansion and adaptation to other languages, enabling a wide range of future applications.

Future Scope

As the dataset continues to grow, contributions from researchers, developers, and data scientists are encouraged. Future extensions could include additional languages, more complex ligature combinations, and larger sample sizes to create an even more comprehensive resource for OCR development. With the open-source data collection tool, the research community is invited to expand the dataset and collaborate in furthering OCR technology for a wide range of scripts.

Conclusion

The Self-Compounded Devanagari Characters Dataset fills a crucial gap in OCR technology for Devanagari script. Created during a challenging global situation, this project has laid the foundation for the digital preservation of ancient texts. With continued collaboration and contributions, it will serve as an invaluable resource for linguistic and AI research, helping to preserve cultural heritage in the digital age.

This dataset is sourced from Kaggle.
Spinal Vertebrae Segmentation Dataset

Description:

<a href="https://gts.ai/dataset-download/spinal-vertebrae-segmentation-dataset/" target="_blank">👉 Download the dataset here</a>

This dataset is comprised of high-resolution MRI scans of the spine, showcasing various degenerative and dystrophic changes, including vertebral fractures, osteophytes, and disc protrusions. It’s an invaluable resource for the development of machine learning models aimed at classifying and segmenting spinal abnormalities.

Download Dataset

Key Features:

Comprehensive Imaging: The dataset includes 5 studies, captured from different angles, covering a range of spinal anomalies such as a focal mass in the Th7 vertebral body, fluid masses, and spondyloarthrosis.

Clinical Annotations: All images are thoroughly labeled by experienced medical professionals and come with corresponding PDF reports, allowing for accurate diagnosis and reference.

Diverse Conditions: The scans highlight both common and complex conditions like osteophytes, fluid masses on the anterior contour of the vertebrae, and dorsal protrusions of the 
Th11-Th12 disc. These features make it highly applicable for detecting fractures and classifying various spine-related abnormalities.

Applications in AI and Healthcare:

Fracture Detection Models: This dataset is particularly useful for training deep learning algorithms to detect vertebral fractures, nerve compression, and other dystrophic changes.

Segmentation and Classification: The detailed imaging of vertebrae, intervertebral discs, and surrounding tissues enables the segmentation of critical regions for AI-driven diagnostic tools.

Training Data for Degenerative Disease Detection: With a focus on conditions like spondyloarthrosis, the dataset provides robust training material for detecting degenerative spinal diseases, enhancing the accuracy of automated systems.

Use Case for AI Training:

Researchers and healthcare professionals can leverage this dataset to develop machine learning models for improved diagnostics in spinal health, particularly focusing on dystrophic conditions and spine anomaly classification.

This dataset is sourced from Kaggle.
Lyme Disease Erythema Migrans Rashes

Description:

<a href="https://gts.ai/dataset-download/lyme-disease-erythema-migrans-rashes/" target="_blank">👉 Download the dataset here</a>

Lyme Disease, a growing yet underfunded field, impacts over 300,000 people each year. Erythema Migrans (EM), known as the “Bull’s Eye Rash,” is a key early indicator of Lyme Disease. This dataset is aimed at supporting healthcare AI research and improving diagnostic accuracy by providing a comprehensive collection of EM and non-EM rashes, as these conditions are often confused by healthcare professionals.


Download Dataset

Dataset Utility

This dataset is particularly useful for:

Machine Learning Research: For training and evaluating AI models focused on image recognition for Lyme Disease detection.

Medical Training: For doctors and dermatologists to differentiate between EM rashes and other similar conditions.

Public Health: Raising awareness and educating patients on the identification of Lyme Disease symptoms.

New Additions

The dataset includes an expanded range of metadata, such as:

Time Duration: How long each rash has been present.

Environmental Factors: Potential exposure to Lyme Disease vectors (e.g., geographical data, tick bite history).

Medical History: Details on whether the patient had previous Lyme Disease diagnosis or other conditions.

Additionally, the dataset is enriched with expert commentary, providing insights into:

How EM rash varies with different stages of Lyme Disease.

Typical vs. atypical EM presentations that may confuse diagnosis.

Acknowledgements

The dataset is compiled using web scraping and manual filtering, ensuring the data is accurate, clean, and relevant. Medical professionals have reviewed the data, contributing expert insights to improve its usability for research and diagnosis purposes.

Use Case Suggestions

AI Model Training: Can be used to build or refine AI models for automatic detection of Lyme Disease.

Misdiagnosis Prevention: The inclusion of similar non-EM rashes helps create models that reduce the risk of misdiagnosis by providing comparative datasets.

Medical Research: Provides valuable resources for researching the manifestation of Lyme Disease across diverse populations.

This dataset is sourced from Kaggle.
Stable Diffusion Face Dataset

Description:

<a href="https://gts.ai/dataset-download/stable-diffusion-face-dataset/" target="_blank">👉 Download the dataset here</a>

This dataset contains AI-generated human faces created using various versions of Stable Diffusion models. The primary goal is to create highly realistic human faces without adhering to any specific style, primarily focusing on facial realism. The dataset includes:

Images in 512x512px resolution, generated using Stable Diffusion 1.5.

Images in 768x768px resolution, created with Stable Diffusion 2.1.

Images in 1024x1024px, produced by SDXL 1.0.

Download Dataset

This dataset provides a wide array of human faces that can be employed for AI training, machine learning models, facial recognition, and various computer vision tasks. The collection represents diverse human appearances, including different ages, ethnicities, and genders, making it ideal for applications requiring human likeness simulations, security algorithms, and research into AI biases. The focus is on achieving photorealism while utilizing the generative power of modern AI models like Stable Diffusion. The images have been meticulously curated to ensure high quality, versatility, and a wide scope of applications across industries such as entertainment, marketing, security, and virtual avatars.

Potential Applications:

Training deep learning models for facial recognition.

Enhancing synthetic media and virtual avatar creation.

Researching biases in AI-generated content.

Improving performance in security algorithms that require realistic face datasets.

This dataset is particularly useful for:

AI Research & Development: Train machine learning models to recognize and classify human faces across diverse parameters.

Synthetic Media Creation: Leverage this dataset to create lifelike characters for games, movies, and virtual environments.

Security Applications: Enhance facial recognition systems by training them on AI-generated faces that mirror real-world complexities.

AI Ethics & Bias Testing: Evaluate how well AI models handle diversity in age, gender, and ethnicity with a balanced dataset.

This dataset is sourced from Kaggle.
Coffee Roasting Agtron Dataset

Description:

<a href="https://gts.ai/dataset-download/coffee-roasting-agtron-dataset/" target="_blank">👉 Download the dataset here</a>

To ensure uniformity in coffee roasting grades, the Specialty Coffee Association of America (SCAA) and Agtron Inc. have developed a globally accepted scale. This scale ranges from 0 to 100, with higher values indicating lighter roasts and lower values signifying darker roasts.

Dataset Content

The dataset includes images of coffee beans roasted at various Agtron levels, captured using multiple mobile devices. Each image set is accompanied by a reference photo representing the lighting conditions, ensuring standardization across all samples.

Download Dataset

Data Capture Conditions

Device Variability: Coffee bean images are taken using different mobile devices to account for varying camera qualities and resolutions.

Lighting Reference: Alongside the coffee images, a photo of a blank white sheet is included to serve as a lighting reference. This allows for normalization of lighting conditions, making 
the dataset reliable for visual analysis.

Agtron Scale and Its Importance

The Agtron scale is the industry standard for measuring the degree of coffee roasting. As the roast becomes darker, the value on the scale decreases. This scale is crucial in both quality control and research, allowing coffee professionals to consistently classify and compare roast levels.

Applications

This dataset is useful for several applications, including:

Roast Level Analysis: Training machine learning models to identify and classify coffee roast levels based on visual inputs.

Quality Control: Automating the analysis of roast consistency in coffee production.

Research & Development: Coffee researchers and companies can utilize the dataset to innovate new roasting techniques or improve product consistency.

Potential Use in AI and Machine Learning

The dataset serves as a foundation for AI development, specifically in image recognition. With the reference lighting condition and varied device capture, this dataset is ideal for developing models that can automate the classification of coffee roast levels based on the Agtron scale.

This dataset is sourced from Kaggle.