YOLO YOLOv8 AI technology Top Builders

Explore the top contributors showcasing the highest number of YOLO YOLOv8 AI technology app submissions within our community.

YOLO v8

Ultralytics YOLOv8 is a cutting-edge, state-of-the-art (SOTA) model that builds upon the success of previous YOLO versions and introduces new features and improvements to further boost performance and flexibility. YOLOv8 is designed to be fast, accurate, and easy to use, making it an excellent choice for a wide range of object detection and tracking, instance segmentation, image classification and pose estimation tasks.

What's new in YOLOv8?

YOLOv8 supports a full range of vision AI tasks, including detection, segmentation, pose estimation, tracking, and classification. This versatility allows users to leverage YOLOv8's capabilities across diverse applications and domains.

General
Relese dateMay, 2023
Repositoryhttps://github.com/ultralytics/ultralytics
TypeReal time object detection

Libraries

Discover YOLOv8

Edit on Github

YOLO YOLOv8 AI technology Hackathon projects

Discover innovative solutions crafted with YOLO YOLOv8 AI technology, developed by our community members during our engaging hackathons.

Visionary Plates

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.

scene-e

scene-e

Aiming to streamline the creation of 3D mesh (.obj) files from a given picture - we first use a object detection model (yolo-v8) to extract each bounding box(region of interests). Then we remove background from each bounding box by using rembg library . Lastly we create a 3d model from the images produced using the shap-e library . This project can be used by artists / game developers to easily recreate 3d mesh of any object they have seen in a picture . Currently , command line app has been created . I plan to deploy a flask app with a Vue.js frontend in the future . Libraries used in the project - shap-e , ultralytics (yolo-v8) and rembg (background removal)

DublinByte Video Surveillance

DublinByte Video Surveillance

The core ethos of DublinByte's surveillance system lies in harnessing the power of AI to enhance safety and security across various domains. By employing sophisticated object detection algorithms, the system enables swift and precise identification of diverse elements, including people, animals, and objects. This real-time detection capability proves to be a game-changer, allowing immediate response to potential threats and suspicious activities, effectively mitigating risks and ensuring a safer environment. An intriguing aspect of DublinByte's creation is its seamless integration of text-to-speech models. This ingenious addition empowers the system to provide vocal notifications and alerts upon identifying specific objects, such as cups. The real-time audio feedback plays a crucial role in alerting security personnel, property owners, or relevant authorities, enabling rapid and decisive actions when needed. While their prototype focuses on detecting cups, the underlying AI architecture is inherently versatile and scalable. DublinByte's surveillance system holds immense promise to be customized for recognizing an array of objects, from weapons and hazardous items to missing individuals or potential intruders. This adaptability underscores the system's potential to revolutionize security protocols in various settings. The applications of DublinByte's AI-driven surveillance system are both comprehensive and far-reaching. In bustling public spaces like airports, train stations, and shopping centers, the system can act as an ever-vigilant guardian, ensuring the safety of commuters and shoppers alike. In private establishments, including offices, homes, and warehouses, it becomes an invaluable asset for preventing theft, monitoring crowd movements, and maintaining overall security.