Omkar2118/datasets huggingface dataset:
Koalas in the Wild

Description:

<a href="https://gts.ai/dataset-download/koalas-in-the-wild/" target="_blank">👉 Download the dataset here</a>

The Koalas in the Wild dataset captures images of koalas across various states in Australia, providing a valuable resource for studying koalas in their natural habitat. Koalas, an iconic native species of Australia, have faced increasing threats over the years due to habitat destruction, climate change, and particularly the devastating bushfires that swept across the country in recent times.

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Data Collection Process

The images in this dataset were scraped from the iNaturalist platform, which is widely used by citizen scientists and wildlife enthusiasts to document and share observations of species around the world. Each image in this dataset represents unique sightings of koalas in the wild, offering diverse perspectives from different states of Australia.

To ensure high-quality annotations, the dataset includes manual labeling of the x and y coordinates for each koala in the images. These annotations provide bounding box coordinates, offering a precise localization of the koalas within each image. The annotations are stored in a CSV file that contains the minimum and maximum values of the x and y-axis for every image.

Dataset Structure

The dataset consists of:

Images: 2,100 images of koalas from different Australian states, each with varying backgrounds and settings, depicting koalas in the wild.

Annotations: A CSV file with manually annotated bounding boxes, specifying the minimum and maximum x and y coordinates for each koala in the corresponding image.

Each row in the CSV file corresponds to a unique image and includes:

Image ID: The identifier for the image.

Bounding Box Coordinates: Minimum and maximum x and y values, outlining the koala’s location in the image.

Use Cases

This dataset can be used in a variety of applications:

Wildlife Conservation: Assisting conservationists in tracking koala populations and understanding their movement patterns.

Machine Learning & Computer Vision: Training object detection models to identify koalas in the wild, even in complex environments.

Ecological Research: Analyzing the effects of environmental changes, particularly bushfires, on koala habitats and behavior.

This dataset is sourced from Kaggle.
Starjob

Dataset of NP-Hard Job Shop Scheduling Problem (JSSP), specifically designed for LLM fine-tuning.

Introducing paper: Starjob: Dataset for LLM-Driven Job Shop Scheduling
SolutionBench

https://huggingface.co/papers/2502.20730

Introducing paper: DeepSolution: Boosting Complex Engineering Solution Design via Tree-based Exploration and Bi-point Thinking
Smart Waste Management

Problem Statement

<a href="https://globaltechnosol.com/case_studies/smart-waste-management/" target="_blank">👉 Download the case studies here</a>

Municipalities and waste management companies faced inefficiencies in waste collection and recycling processes. Fixed collection schedules often resulted in overflowing bins in some areas and underutilized resources in others, leading to increased operational costs and reduced recycling effectiveness. The organization sought a solution to optimize waste collection routes and enhance recycling practices using real-time data.

Challenge

Implementing a smart waste management system involved addressing several challenges:

Collecting and analyzing real-time data from waste bins to identify fill levels and waste types.

Optimizing collection routes dynamically to reduce fuel consumption and operational costs.

Promoting effective recycling by categorizing waste and monitoring disposal patterns.

Solution Provided

A smart waste management system was developed using IoT-enabled waste bins and machine learning algorithms. The solution was designed to:

Monitor waste bin fill levels and categorize waste types in real time using IoT sensors.

Optimize waste collection routes dynamically based on bin status and location.

Provide actionable insights to improve recycling rates and waste management efficiency.

Development Steps

Data Collection

Installed IoT sensors in waste bins to capture data on fill levels, waste composition, and disposal patterns.

Preprocessing

Standardized and cleaned data to ensure accurate input for route optimization and recycling analytics.

Model Development

Built machine learning algorithms to predict optimal collection times and routes. Developed recycling analytics models to identify trends and improve waste segregation.

Validation

Tested the system in pilot areas to ensure accuracy in fill level detection, route optimization, and recycling recommendations.

Deployment

Implemented the solution across the waste management network, integrating it with fleet management systems for real-time routing.

Continuous Monitoring & Improvement

Established a feedback loop to refine models using new data and evolving waste management patterns.

Results

Increased Efficiency in Waste Collection

Dynamic routing reduced unnecessary trips, ensuring timely collection and preventing overflowing bins.

Reduced Operational Costs

Optimized collection routes and schedules minimized fuel consumption and labor costs.

Promoted Effective Recycling Practices

Real-time waste categorization and insights supported improved segregation and recycling efforts, reducing landfill waste.

Enhanced Sustainability

Efficient waste management contributed to lower carbon emissions and aligned with the organization’s environmental goals.

Scalable and Future-Ready Solution

The system scaled seamlessly to cover larger areas and adapted to new waste management regulations and practices.
Smart Retail Shelf Management

Problem Statement

<a href="https://globaltechnosol.com/case_studies/Smart-Retail-Shelf-Management/" target="_blank">👉 Download the case studies here</a>

A retail chain faced challenges in managing shelf inventory efficiently. Frequent out-of-stock situations and poor shelf space utilization led to lost sales and decreased customer satisfaction. Traditional manual inventory checks were time-consuming and error-prone, requiring a smarter solution to monitor shelf inventory in real time and optimize product availability.

Challenge

Implementing a smart retail shelf management system required addressing the following challenges:

Capturing and analyzing real-time data on product availability and shelf space utilization.

Ensuring the system’s scalability to manage multiple store locations and diverse product categories.

Providing actionable insights to store managers for proactive inventory management.

Solution Provided

A smart shelf management system was developed using AI-powered computer vision and IoT sensors. The solution was designed to:

Monitor retail shelves in real time using cameras and IoT-enabled weight sensors.

Analyze data to detect low stock levels, misplaced items, and shelf space inefficiencies.

Provide automated alerts and recommendations for restocking and space optimization.

Development Steps

Data Collection

Deployed cameras and IoT sensors on retail shelves to collect data on product presence, weight, and positioning.

Preprocessing

Processed and structured image and sensor data to identify patterns in stock levels and shelf utilization.

Model Development

Built computer vision models to recognize products and detect empty shelf spaces. Developed analytics algorithms to calculate optimal restocking schedules and shelf layouts.

Validation

Tested the system in pilot stores to ensure accurate detection of out-of-stock items and actionable insights for inventory management.

Deployment

Implemented the solution across the retail chain, integrating it with inventory management systems for real-time synchronization.

Continuous Monitoring & Improvement

Established a feedback loop to refine AI models and adapt to new product categories and store layouts.

Results

Reduced Out-of-Stock Instances

Real-time monitoring and automated alerts minimized instances of products going out of stock, improving sales and customer satisfaction.

Optimized Shelf Space Utilization

The system provided data-driven recommendations to maximize shelf space efficiency, ensuring better product placement and visibility.

Enhanced Shopping Experience

Improved product availability and well-organized shelves created a more satisfying and convenient shopping experience for customers.

Increased Operational Efficiency

Automation reduced the need for manual inventory checks, allowing staff to focus on value-added tasks.

Scalable and Future-Ready Solution

The system scaled effortlessly to multiple stores and adapted to new product lines, ensuring long-term relevance.
Nomeroff Russian License Plates

Description:

<a href="https://gts.ai/dataset-download/nomeroff-russian-license-plates/" target="_blank">👉 Download the dataset here</a>

This dataset consists of high-resolution photographs of Russian vehicle license plates captured in diverse environments and lighting conditions. It is designed for image classification and object detection tasks, particularly aimed at automatic license plate recognition (ALPR) systems. The dataset features vehicles in urban, rural, and highway settings, with a wide range of vehicle models and plate formats that adhere to Russian vehicle registration standards.

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Dataset Overview:

A collection of JPG images with varying resolutions, capturing license plates from multiple angles (frontal, side, rear) and distances.
Environments include daytime and nighttime scenes, varying weather conditions (sunny, rainy, snowy), and different lighting (natural and artificial).

CSV Columns:

file_name: The name of the image file (str, unique values).

plate_number: The alphanumeric license plate number present in the image (str).

vehicle_type: The type of vehicle (e.g., sedan, SUV, truck, etc.) – this allows for classification beyond just license plate recognition (str).

angle_of_view: The angle at which the image was captured (e.g., frontal, side, rear) – this provides additional context for training models to handle various perspectives (str).

weather_condition: The weather conditions during image capture (e.g., sunny, rainy, snowy, etc.), offering insights into how weather impacts recognition performance (str).

time_of_day: Whether the image was captured during the day, evening, or night, useful for models dealing with varying lighting conditions (str).

occlusion_level: A numeric rating (0-3) representing the level of obstruction on the license plate (e.g., due to dirt, damage, or angle), helping models adapt to real-world imperfections 
(int).

Applications:

License Plate Recognition (LPR) Systems.

Vehicle Tracking and Identification.

Traffic Monitoring and Enforcement.

Smart City Solutions and Automated Toll Systems.

This dataset is sourced from Kaggle.
Smart Logistics and Route Optimization

Problem Statement

<a href="https://globaltechnosol.com/case_studies/Smart-Logistics-and-Route-Optimization/" target="_blank">👉 Download the case studies here</a>

A logistics company faced challenges in managing delivery routes efficiently across a vast network. Inefficient route planning resulted in higher fuel consumption, delayed deliveries, and dissatisfied customers. The company needed an intelligent solution to optimize delivery routes, reduce operational costs, and ensure timely deliveries.

Challenge

Implementing a smart logistics and route optimization system involved addressing several challenges:

Analyzing real-time traffic conditions, weather data, and delivery constraints.

Creating dynamic, efficient routes that adapt to changing conditions during transit.

Integrating the system seamlessly with existing logistics and fleet management operations.

Solution Provided

A smart logistics and route optimization system was developed using AI-based algorithms and GPS tracking. The solution was designed to:

Analyze real-time traffic, weather, and delivery schedules to generate optimal routes.

Provide dynamic re-routing capabilities to respond to unexpected delays or disruptions.

Offer actionable insights for fleet managers to improve overall logistics efficiency.

Development Steps

Data Collection

Collected historical and real-time data, including traffic patterns, fuel consumption rates, weather forecasts, and delivery schedules.

Preprocessing

Cleaned and structured the data to identify trends, delivery constraints, and optimal route parameters.

Model Development

Built AI algorithms to analyze data and create efficient delivery routes. Developed predictive models to anticipate delays and recommend alternative routes in real time.

Validation

Tested the system on simulated and live logistics scenarios to evaluate its accuracy and effectiveness in optimizing routes.

Deployment

Integrated the solution with the company’s GPS tracking and logistics management systems, enabling real-time route optimization for the fleet.

Continuous Monitoring & Improvement

Established a feedback loop to refine algorithms based on delivery outcomes and new data.

Results

Reduced Fuel Costs

Optimized routes decreased fuel consumption by 15%, contributing to significant cost savings.

Improved Delivery Efficiency

Timely deliveries improved operational efficiency, reducing average delivery times and increasing the number of deliveries completed daily.

Enhanced Customer Satisfaction

Accurate and on-time deliveries resulted in higher customer satisfaction and loyalty.

Real-Time Adaptability

Dynamic re-routing allowed the fleet to respond effectively to unexpected traffic or weather conditions, ensuring reliability.

Scalable and Sustainable Solution

The system scaled effortlessly across different regions and fleets, while promoting environmentally sustainable practices through reduced emissions.
Automated Financial Reporting

Problem Statement

<a href="https://globaltechnosol.com/case_studies/Automated-Financial-Reporting/" target="_blank">👉 Download the case studies here</a>

A financial services firm faced inefficiencies in generating accurate and timely financial reports. The manual reporting process was labor-intensive, prone to errors, and delayed decision-making. With increasing data complexity and regulatory requirements, the firm sought an automated solution to streamline financial reporting while maintaining high accuracy.

Challenge

Implementing an automated financial reporting system involved addressing the following challenges:

Aggregating and consolidating large volumes of financial data from disparate sources in real time.

Ensuring compliance with regulatory standards and industry practices.

Generating detailed, accurate reports with contextual analysis and insights.

Solution Provided

An AI-powered financial reporting system was developed using advanced data aggregation tools and Natural Language Generation (NLG) technology. The solution was designed to:

Collect and consolidate financial data from multiple systems and databases.

Analyze data to detect trends, anomalies, and key performance indicators (KPIs).

Generate professional-quality financial reports in real time with contextual narratives.

Development Steps

Data Collection

Connected to financial systems, ERP platforms, and databases to aggregate data related to revenue, expenses, assets, and liabilities.

Preprocessing

Standardized data formats, resolved inconsistencies, and ensured compliance with financial reporting standards.

Model Development

Built AI models to analyze financial data, identify patterns, and calculate KPIs. Integrated NLG algorithms to transform data into coherent and contextually accurate narratives.

Validation

Tested the system by comparing generated reports with manually created ones to ensure accuracy and reliability.

Deployment

Implemented the solution across the organization, providing real-time reporting capabilities to finance teams and executives.

Continuous Monitoring & Improvement

Established a feedback loop to refine algorithms based on user inputs and evolving reporting requirements.

Results

Reduced Reporting Time

Automated workflows reduced the time required to generate financial reports by 70%, enabling faster decision-making.

Minimized Human Errors

AI-driven data aggregation and analysis eliminated manual errors, ensuring consistent and accurate reporting.

Real-Time Financial Insights

The system provided instant access to financial metrics and trends, supporting proactive business strategies.

Improved Compliance

Automated checks ensured compliance with regulatory standards and reduced the risk of reporting inaccuracies.

Enhanced Productivity

Finance teams were freed from repetitive tasks, allowing them to focus on strategic financial planning and analysis.
Healthcare Patient Monitoring

Problem Statement

<a href="https://globaltechnosol.com/case_studies/Healthcare-Patient-Monitoring/" target="_blank">👉 Download the case studies here</a>

Hospitals and healthcare providers faced challenges in ensuring continuous monitoring of patient vitals, especially for high-risk patients. Traditional monitoring methods often lacked real-time data processing and timely alerts, leading to delayed responses and increased hospital readmissions. The healthcare provider needed a solution to monitor patient health continuously and deliver actionable insights for improved care.

Challenge

Implementing an advanced patient monitoring system involved overcoming several challenges:

Collecting and analyzing real-time data from multiple IoT-enabled medical devices.

Ensuring accurate health insights while minimizing false alarms.

Integrating the system seamlessly with hospital workflows and electronic health records (EHR).

Solution Provided

A comprehensive patient monitoring system was developed using IoT-enabled medical devices and AI-based monitoring systems. The solution was designed to:

Continuously collect patient vital data such as heart rate, blood pressure, oxygen levels, and temperature.

Analyze data in real-time to detect anomalies and provide early warnings for potential health issues.

Send alerts to healthcare professionals and caregivers for timely interventions.

Development Steps

Data Collection

Deployed IoT-enabled devices such as wearable monitors, smart sensors, and bedside equipment to collect patient data continuously.

Preprocessing

Cleaned and standardized data streams to ensure accurate analysis and integration with hospital systems.

AI Model Development

Built machine learning models to analyze vital trends and detect abnormalities in real-time

Validation

Tested the system in controlled environments to ensure accuracy and reliability in detecting health issues.

Deployment

Implemented the solution in hospitals and care facilities, integrating it with EHR systems and alert mechanisms for seamless operation.

Continuous Monitoring & Improvement

Established a feedback loop to refine models and algorithms based on real-world data and healthcare provider feedback.

Results

Enhanced Patient Care

Real-time monitoring and proactive alerts enabled healthcare professionals to provide timely interventions, improving patient outcomes.

Early Detection of Health Issues

The system detected potential health complications early, reducing the severity of conditions and preventing critical events.

Reduced Hospital Readmissions

Continuous monitoring helped manage patient health effectively, leading to a significant decrease in readmission rates.

Improved Operational Efficiency

Automation and real-time insights reduced the burden on healthcare staff, allowing them to focus on critical cases.

Scalable Solution

The system adapted seamlessly to various healthcare settings, including hospitals, clinics, and home care environments.
Smart Manufacturing with Digital Twins

Problem Statement

<a href="https://globaltechnosol.com/case_studies/Smart-Manufacturing-with-Digital-Twins/" target="_blank">👉 Download the case studies here</a>

A leading manufacturing company faced challenges in optimizing its operations and minimizing downtime. Traditional methods of monitoring and improving processes were reactive, leading to inefficiencies and increased costs. The company sought a solution to simulate and optimize manufacturing workflows, enabling proactive maintenance and real-time process improvements.

Challenge

Implementing smart manufacturing with digital twins presented several challenges:

Integrating IoT sensors across complex machinery and processes to collect real-time data.

Creating accurate digital replicas of physical manufacturing operations for simulation and analysis.

Analyzing large volumes of data to identify inefficiencies and predict potential failures.

Solution Provided

A comprehensive digital twin system was developed using IoT sensors, AI analytics, and digital twin technology. The solution was designed to:

Create digital replicas of physical manufacturing processes to simulate operations and test optimizations.

Monitor real-time data from IoT sensors to detect anomalies and inefficiencies.

Enable predictive maintenance by analyzing equipment performance and predicting failures before they occur.

Development Steps

Data Collection

Deployed IoT sensors across manufacturing equipment to collect data on machine performance, environmental conditions, and workflow metrics.

Preprocessing

Standardized and cleaned data to ensure accuracy and compatibility for simulation and analytics.

Digital Twin Development

Created digital replicas of manufacturing processes, integrating real-time data streams for continuous updates. Built simulation models to test workflow optimizations and predict the outcomes of process changes.

Validation

Tested the system in simulated environments to ensure accuracy in predicting outcomes and optimizing processes.

Deployment

Implemented the digital twin system across manufacturing facilities, integrating it with existing management tools for seamless operation.

Continuous Monitoring & Improvement

Established a feedback loop to refine simulations and analytics based on new data and operational changes.

Results

Improved Operational Efficiency

The system optimized workflows, reducing bottlenecks and improving overall efficiency in manufacturing processes.

Enabled Proactive Maintenance

Predictive maintenance minimized downtime by identifying potential equipment failures before they occurred.

Optimized Resource Usage

Simulations allowed the company to test process changes virtually, reducing waste and optimizing resource allocation.

Enhanced Decision-Making

Real-time insights and simulations enabled data-driven decisions, accelerating innovation and process improvements.

Scalable and Future-Ready

The digital twin system scaled effortlessly across facilities and adapted to new manufacturing technologies, ensuring long-term viability.