YOLO YOLOv5 AI technology Top Builders

Explore the top contributors showcasing the highest number of YOLO YOLOv5 AI technology app submissions within our community.

YOLO v5

YOLOv5 is a family of object detection architectures and models pretrained on the COCO dataset and created By Ultralytics Team in 2020. This architecture uses single Neural Network to process entire image. It divides image into regions and predicts bounding boxes and probabilities for each region. These bounding boxes are weighted by the predicted probabilities. YOLOv5 is available in the Hub as a PyTorch module. You can use it directly in your code or in the Hub.

General
Relese date	June 25, 2020
Author	YOLO
Repository	https://github.com/ultralytics/yolov5
Type	Real time object detection

Libraries

Discover YOLOv5

YOLOv5 Repository Object detection architectures and models pretrained on the COCO data set
YOLOv5 Documentation GPT-4 Product description page
PyTorch Hub

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YOLO YOLOv5 AI technology Hackathon projects

Discover innovative solutions crafted with YOLO YOLOv5 AI technology, developed by our community members during our engaging hackathons.

CleanNet

CleanNet aims to develop a strategic framework for optimizing the deployment of 5G network nodes. The goal is to ensure comprehensive coverage while minimizing exposure to air pollution. By leveraging geospatial data, population density simulations, and air quality indices (AQI), the project identifies optimal node locations that balance network efficiency with environmental considerations. CleanNet aims to develop a strategic framework for optimizing the deployment of 5G network nodes. The goal is to ensure comprehensive coverage while minimizing exposure to air pollution. By leveraging geospatial data, population density simulations, and air quality indices (AQI), the project identifies optimal node locations that balance network efficiency with environmental considerations.

NetConnect

Public Sector Network Connectivity Analyzer The Public Sector Network Connectivity Analyzer is a comprehensive solution designed to address the critical need for reliable network monitoring across public institutions. Our application serves as an essential tool for IT administrators managing connectivity infrastructure for schools, healthcare facilities, government offices, libraries, and other public service organizations. Core Capabilities Real-Time Network Visualization Interactive diagrams and topology maps provide clear visibility into how public institutions are connected, displaying network elements, connection points, and infrastructure components with intuitive visualization tools. Performance Monitoring System Our platform continuously tracks vital network metrics including uptime percentages, latency measurements, bandwidth utilization, and connection status across the entire public sector network, enabling proactive management. Advanced Simulation Engine IT professionals can run comprehensive simulations to test network resilience under various scenarios such as increased user loads, infrastructure failures, or cyber incidents, helping identify vulnerabilities before they impact critical services. Institution Management Portal Administrators can efficiently manage information about connected institutions, monitor their connection status in real-time, and access detailed performance metrics through a unified dashboard interface. Geographic Mapping Integration Our system incorporates geographic visualization capabilities to display the physical distribution of institutions and network infrastructure across regions, facilitating better resource allocation and planning. Technical Implementation This solution addresses the unique challenges faced by public sector organizations that require reliable connectivity for delivering essential services to communities, while providing the tools needed to ensure network resilience, performance, and security.

AstroCleanAI -- AI-Powered Space Agent

AstroCleanAI is an advanced AI-powered space debris tracking and risk prediction system designed to enhance space safety. With the increasing amount of space junk threatening satellites and future missions, managing debris is more critical than ever. AstroCleanAI leverages artificial intelligence to detect, classify, and predict debris behavior, ensuring proactive collision avoidance and efficient satellite operations. 🛰️ Key Features: Real-Time Space Debris Detection & Classification – Uses deep learning models (YOLOv5, ResNet-50) to analyze satellite imagery and radar data, identifying debris based on size, trajectory, and risk level. AI-Powered Collision Risk Prediction – Implements an XGBoost-based risk assessment model to predict satellite-debris collision probabilities and suggest optimal avoidance maneuvers. Interactive Space Debris Simulation – Visualizes real-world debris movement and potential collision risks using 3D orbital mechanics modeling. Public Engagement Dashboard – Allows researchers, students, and enthusiasts to explore real-time space debris tracking and AI-driven insights. 🛠️ Technology Stack: AI & Machine Learning: TensorFlow, PyTorch, YOLOv5, ResNet-50, XGBoost Space Data & Analysis: NASA’s Open TLE Data, OpenCV for satellite imagery processing Web & Visualization: Streamlit, Matplotlib (3D trajectory simulation), Flask backend 🌍 Impact & Future Scope: AstroCleanAI contributes to sustainable space exploration by mitigating debris-related risks, preventing costly satellite damage, and optimizing space traffic management. Future enhancements may include integrating real-world satellite telemetry, collaborating with space agencies for improved predictions, and developing an autonomous debris mitigation system capable of orchestrating deorbiting maneuvers.

Pyropulse

Pyroplus presents a comprehensive solution to address the urgent challenges of wildfire management. Integrating IoT technology, AI algorithms, drones, and eco-friendly chemical materials, our system detects wildfires early using air quality sensors, analyzes data in real-time to guide response efforts, and employs drones for aerial surveillance and strategic firefighting. Our innovative chemical material, composed of cellulose, colloidal silica, sodium nitrite, and ammonium chloride, swiftly extinguishes fires while minimizing environmental impact. By combining cutting-edge technology with sustainable practices, Pyroplus aims to revolutionize wildfire management, protect ecosystems, and safeguard communities from the devastating effects of wildfires. Our LLM agent plays a pivotal role in enhancing public awareness and offering crucial instructions for fire protection. With a focus on preventing human-caused fires, which account for a staggering 95% of incidents, our agent acts as a proactive guide. Through informative content and clear directives, it empowers individuals with the knowledge needed to prevent wildfires, fostering a community-driven approach to fire safety.

Netra - AI Enabled Imaging device for MSMEs

How Netra Works Installation: Netra seamlessly integrates into your existing infrastructure. Data Collection: Netra gathers data over time, learning and adapting to your environment. Netra Edge Analytics (Cloud Platform): Our cloud platform processes the collected data, providing actionable insights. Proof of Concept (POC): We develop a POC to showcase the potential of Netra in your specific use case. Deployment & Testing: Once approved, Netra is deployed with rigorous testing to ensure reliability and accuracy. Continuous Improvement: We continuously refine and enhance Netra's capabilities based on feedback and real-world usage. Netra Edge Analytics Netra Edge Analytics Services Data Collection: Gather data from Netra devices securely and efficiently. Data Pre-processing: Clean, preprocess, and transform raw data for analysis. Data Annotation: Annotate data to train machine learning models effectively. Model Deployment: Deploy trained models onto Netra devices seamlessly. Supported Tasks Netra Functions Where? Manufacturing 🏭 Product Counting: Track inventory levels with precision. 🔍 Defect Detection: Identify flaws in products accurately. 📋 SOP Compliance: Ensure adherence to industry standards seamlessly. Logistics/Transportation 🚚 Worker Safety Monitoring: Ensure the safety of your workforce in real-time. 🛒 Vehicle Stability: Optimize loading operations and prevent accidents. Petrochemicals ⚠️ Accident Prevention: Mitigate risks associated with hazardous chemical handling. Security 🔒 Real-time Alerts: Stay ahead of potential threats with proactive notifications. Retail 🛍️ Customer Behavior Analysis: Gain insights into consumer behavior for informed decision-making. Custom Use Cases 🔧 Tailored Solutions: Adapt to your unique business needs with custom implementations.

Visionary Plates

Visionary Plates: Advancing License Plate Detection Models is a project driven by the ambition to revolutionize license plate recognition using cutting-edge object detection techniques. Our objective is to significantly enhance the accuracy and robustness of license plate detection systems, making them proficient in various real-world scenarios. By meticulously curating and labeling a diverse dataset, encompassing different lighting conditions, vehicle orientations, and environmental backgrounds, we have laid a strong foundation. Leveraging this dataset, we fine-tune the YOLOv8 model, an architecture renowned for its efficiency and accuracy. The model is trained on a carefully chosen set of parameters, optimizing it for a single class—license plates. Through iterative experimentation and meticulous fine-tuning, we address critical challenges encountered during this process. Our journey involves overcoming obstacles related to night vision scenarios and initial model performance, with innovative solutions like Sharpening and Gamma Control methods. We compare and analyze the performance of different models, including YOLOv5 and traditional computer vision methods, ultimately identifying YOLOv8 as the most effective choice for our specific use case. The entire training process, from dataset curation to model fine-tuning, is efficiently facilitated through the use of Lambda Cloud's powerful infrastructure, optimizing resources and time. The project's outcome, a well-trained model, is encapsulated for easy access and distribution in the 'run.zip' file. Visionary Plates strives to provide a reliable and accurate license plate detection system, with the potential to significantly impact areas such as traffic monitoring, parking management, and law enforcement. The project signifies our commitment to innovation, pushing the boundaries of object detection technology to create practical solutions that make a difference in the real world.