Omkar2118/datasets huggingface dataset:
Corn Kernel Images Dataset

Description:
The Corn Kernel Images Dataset is a comprehensive collection of 78,881 images of individual corn kernels. These images were captured after the kernels were harvested, processed, and hand-shelled, primarily for agricultural research and data analysis.
<a href="https://gts.ai/dataset-download/corn-kernel-images-dataset/" target="_blank">👉 Download the dataset here</a>

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Image Capture Process:

The kernels were singulated using a seed meter and imaged under both front and back lighting conditions, enhancing visual detail.

This imaging method ensured high clarity and consistency for each kernel, capturing critical aspects like size, shape, and texture.

Applications:

Agriculture: The dataset can be used for machine learning models focused on crop health analysis, yield predictions, and kernel classification.

Automation: This dataset provides valuable input for automated systems in grain sorting and quality control.

Breeding Programs: Plant breeders can utilize the data for phenotypic analysis and crop improvement efforts.

Advanced Techniques and Data Augmentation: In addition to the core dataset, users can apply various data augmentation techniques, such as:

Rotation and Scaling: This enhances the robustness of AI models by providing variability in kernel orientation.

Lighting Adjustments: Simulating different lighting conditions can improve model performance in diverse real-world environments.

Texture and Shape Analysis: The high-resolution images allow for detailed texture mapping, which can be integrated into AI training pipelines for accurate kernel identification.

Conclusion:
This dataset offers extensive opportunities for researchers and professionals in the agriculture and AI domains. By providing high-quality images and comprehensive metadata, it serves as a valuable tool for studying corn kernel morphology, developing AI models, and improving agricultural practices. The potential for further data linkage with the Corn Ear Physical Measurements dataset amplifies its utility, especially for advanced phenotyping studies.

This dataset is sourced from Kaggle.
PS-Eval

The PS-Eval Dataset is a suite of polysemous and monosemous contexts extracted and filtered from the WiC dataset. It aims to evaluate the ability of Sparse Autoencoders (SAEs) to disentangle polysemantic activations into monosemantic features within large language models (LLMs).
Fighter Aircraft Image Dataset

Description:

<a href="https://gts.ai/dataset-download/fighter-aircraft-image-dataset/" target="_blank">👉 Download the dataset here</a>

This dataset is an extensive collection of high-quality images featuring various fighter aircraft, curated specifically for machine learning tasks such as classification, image recognition, and object detection. The images are systematically organized into folders, each labeled with the corresponding aircraft model name, offering a valuable resource for researchers and developers working on AI-driven aviation projects.

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Aircraft Models Included

The dataset includes a wide variety of modern and vintage fighter aircraft from around the world. Examples range from early 20th-century designs to cutting-edge jet fighters, making it suitable for research across various eras of aviation technology.

Examples of Fighter Models:

F-22 Raptor

Su-30MKI

Eurofighter Typhoon

F-16 Falcon

Each model folder contains a comprehensive set of images covering key visual aspects of the aircraft, such as wing design, fuselage structure, and engine placement, allowing for high-precision classification models.

Applications in Machine Learning

This dataset is an essential tool for tasks like:

Object Detection: Training models to detect fighter aircraft in aerial images or videos.

Image Classification: Creating models that can differentiate between various aircraft types based on visual features.

Feature Extraction: Studying unique design features that distinguish different fighter jets, assisting in AI-based image feature extraction.

Dataset Specifications

Format: JPEG/PNG files

Size: The dataset contains thousands of images with varying resolutions to fit different model requirements.

Annotations: Optional annotation files provide bounding boxes and additional metadata for object detection tasks.

Conclusion

The Fighter Aircraft Dataset offers a well-rounded collection of fighter aircraft visuals, enhancing machine learning efforts in both the defense and aviation sectors. Whether you’re building an AI model for image classification or exploring aviation research, this dataset serves as a critical resource for accurate and efficient learning models.

This dataset is sourced from Kaggle.
SensoDat

SensoDat is a dataset of self-driving car simulation data (30K executed simulations). Concretely, it contains:

Simulation description data in ASAM OpenDRIVE format
Sensor data as time series of 81 sensors/propertie
Indian Signboard Image Dataset

Description:

The Indian Traffic Signboard Image Dataset is a comprehensive collection of images aimed at advancing the classification and detection of traffic signs across India. Captured by a diverse range of contributors, the dataset includes images taken in urban and semi-urban settings, showcasing the unique challenges of sign recognition in varied environments.

<a href="https://gts.ai/dataset-download/indian-signboard-image-dataset/" target="_blank">👉 Download the dataset here</a>

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Key Features

Geographical Diversity: Collected by over 2,000 contributors from more than 20 cities across India, ensuring extensive representation of Indian signboards.

Device Variety: All images are captured using smartphones, adding variability in image quality and resolution, which better simulates real-world data.

Varied Lighting Conditions: The dataset includes images taken in different times of the day, including daylight, evening, and night shots, as well as in varied weather conditions like rain, fog, and clear skies.

Diverse Angles and Distances: Signboards are captured from multiple viewpoints, angles, and distances, including occluded and partially visible signs, which further challenges image classification models.

Traffic Sign Diversity: A wide range of Indian traffic signs are cover, including regulatory, warning, and informational signs from across India, representing both rural and urban settings.

Environmental Challenges: The images capture traffic signs amidst real-world obstructions like vehicles, pedestrians, and other road elements, contributing to unstructured image sets.

Dataset Structure and Annotations

Detailed Annotations: The dataset supports both classification and detection, making it suitable for a wide variety of computer vision tasks.

Multiple Formats: The annotations are available in widely-use formats, including COCO, PASCAL VOC, and YOLO, ensuring compatibility with a broad range of machine learning models 
and frameworks.

Multi-Category Labels: Each image can be annotate with multiple categories, enabling the detection of complex sign types within the same image.

Geo-Tagging: Certain images are geo-tag, making them useful for applications involving geographical localization or region-specific traffic sign recognition.

Applications

The Indian Traffic Signboard Image Dataset can be use in multiple fields such as:

Autonomous Driving: Developing intelligent navigation systems and improving signboard detection for self-driving cars.

Smart City Solutions: Enhancing traffic management and safety by recognizing and processing signboards in real-time.

Localization and Mapping: Supporting GPS and navigation systems by providing geo-tagged images for sign detection and mapping accuracy.

AI Training: Training AI models for complex, real-world scenarios involving unstructured, varied, and occluded traffic signs.

This dataset is sourced from Kaggle.
Solar Panel Bounding Boxes

Description:

<a href="https://gts.ai/dataset-download/solar-panel-bounding-boxes/" target="_blank">👉 Download the dataset here</a>

This dataset consists of 621 images of solar panels in various real-world scenarios, predominantly captured in diverse outdoor settings. The dataset is intended for training object detection models, particularly using the YOLO (You Only Look Once) format for bounding box detection. These images range from clear digital representations to in-the-wild captures, providing an extensive training base for solar panel detection tasks.


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Annotation Details

The images were manually annotated using Label Studio, a Python-based program known for its precision and flexibility in labeling tasks. This ensures the annotations meet high standards of accuracy. The variety of scenes includes diverse backgrounds such as residential areas, rooftops, and large solar farms, further enhancing the dataset’s practical application in solar panel detection.

Additional Enhancements

Diverse Environmental Conditions: Consider incorporating solar panel images under varying conditions such as cloudy, rainy, or snowy environments, as this would better equip models for real-world applications.

High-Resolution Images: To improve precision, high-resolution images can be added, ensuring that finer details are captured for more accurate bounding box annotations.

Geographic and Structural Variety: Expanding the dataset to include solar panels from diverse geographic regions (e.g., deserts, urban rooftops, or large industrial parks) would create a more robust training set, making the model suitable for a wide range of environments.

Occlusions and Obstructions: Including images where solar panels are partially covered by objects such as trees, dirt, or debris would challenge the model to detect panels even in less-than-ideal conditions, improving its resilience.

Potential Applications

This enriched dataset could be used in a variety of practical applications, including:

Satellite Imagery Analysis: Detecting solar panels from satellite or aerial imagery for renewable energy assessments.

Smart Grid Monitoring: Integrating with smart grid technologies to track solar energy generation.

Automated Solar Farm Surveillance: Using drones equipped with detection models trained on this dataset for the automated inspection of large-scale solar farms.

Conclusion

By adding more diverse examples, such as negative samples, high-resolution images, and varying weather conditions, this dataset can serve as a comprehensive resource for developing robust solar panel detection systems. Its flexibility and accuracy make it ideal for both research and real-world applications in the renewable energy sector.

This dataset is sourced from Kaggle.