YOLO AI technology page Top Builders

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

YOLO

YOLO (You Only Look Once) is a state-of-the-art, real-time object detection algorithm that can quickly detect and locate objects within an image or video. The YOLO architecture works by taking an input and separating it into a grid of cells and each of these cells is in charge of detecting objects within that region. YOLO returns the bounding boxes containing all the objects in the image and predicts the probability of an object being in each of the boxes and also predicts a class probability to help identify the type of object it is. YOLO is a highly effective object detection algorithm and making YOLO and open-source project led the community to make several improvements in such a limited time.

General
Relese date	2015
Author	Joseph Redmon, Santosh Divvala, Ross Girshick, and Ali Farhadi
Paper	(https://arxiv.org/abs/1506.02640)
Type	Object detection algorithm

YOLO - Resources

Learn even more about YOLO!

v7 Labs Blog "YOLO: Algorithm for Object Detection Explained".
YOLOv5 Repository Object detection architectures and models pretrained on the COCO dataset.
YOLOv6 Web demo Gradio demo for YOLOv6 for object detection on videos.
Hugging Face Spaces Test YOLOv7 in the browser with Hugging Face Spaces.

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YOLO AI technology page Hackathon projects

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

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.

DishForge

DishForge is an innovative AI-powered application revolutionizing cooking experiences by seamlessly merging AI capabilities with culinary artistry. It harnesses Llama 2's natural language processing and Clarifai's computer vision, resulting in an unparalleled recipe generation experience. Users interact with DishForge through a user-friendly interface, inputting the desired type of meal or beverage, their preferences, dietary restrictions, available ingredients, and cooking appliances. Llama 2 processes these inputs to create coherent and personalized recipes. Simultaneously, Stable Diffusion presents the ingredients visually, ensuring a comprehensive understanding of the recipe components and the dish visuals. DishForge addresses practical challenges in meal planning by going beyond being just a recipe generator. It caters to users seeking kitchen convenience by considering preferences, available ingredients, and time constraints. The app's ability to generate recipes tailored to dietary needs, ingredient availability, and cooking resources enhances its value proposition, making it perfect for various culinary skill levels. What sets DishForge apart is its holistic approach to recipe generation. By combining Llama 2's language understanding and generative and recognition image models, it provides a comprehensive culinary solution. The app's ability to generate recipes in diverse formats, visualize ingredients, and accommodate specific preferences positions it as an innovative tool that promotes creativity and experimentation in the kitchen. In summary, DishForge represents the cutting-edge fusion of AI and culinary expertise. It showcases a deep understanding of Llama 2 and Clarifai's capabilities, resulting in a solution that transforms recipe generation, meal planning, and culinary exploration. With its user-friendly interface and personalized recipes, DishForge sets a new standard for AI-driven kitchen innovation.

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

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.

CookPal

Welcome to CookPal, the food identification software created with cutting-edge technology like Vertex AI and object detection. CookPal is a cutting-edge food management program created to decrease food waste and improve user convenience. Those uncertain times when you're examining the leftover food in your fridge are over, so say goodbye to them. CookPal saves the day! You can use the phone's camera or manual text entry to identify your food products with CookPal. CookPal's cutting-edge object identification technology will do its magic as soon as you take a picture or enter the ingredients you have on hand. CookPal, however, doesn't end there. It involves more than just recognizing your ingredients. It elevates your culinary experience by giving you access to a huge library of mouthwatering recipes that are specially adapted to use the products you have on hand. There is no need to waste time browsing innumerable recipe websites or cookbooks. CookPal's recipe recommendations are only a few clicks away, saving you time and energy. CookPal invites you to try out new flavors, play around with flavor pairings, and maximize the ingredients in your pantry. It is simple to navigate and locate the ideal recipe for your culinary trip thanks to its user-friendly layout and intuitive design. So whether you're an experienced cook or a kitchen newbie, CookPal is your go-to partner for excellent meals made with products you already own. Join us in our effort to reduce food waste and create a more sustainable future with CookPal. Try CookPal today and begin on a voyage of culinary inventiveness. Your kitchen's best buddy is CookPal.

AI Home Design

AI Home Design is an interior design assistant powered by Stable Diffusion and YOLO to solve pain points felt by homeowners. It differs from other AI interior design apps out there because 1) it addresses pain points in homeowners' entire user journey, 2) functions as a social sharing platform, and 3) is an aide to augment, not replace home decor professionals and designers. FIRSTLY, the flagship "Create With AI" feature guides the user in prompt engineering to convert their design hunches from text to actual image, helping them overcome creative blockages. This also improves communication with interior designers, since words can be subjective, but images are direct. SECONDLY, homeowners may want to reimagine a space even after initial fittings like paneling and paint jobs are already done. They cannot tear down these fittings in real life, but they can use Stable Diffusion's image-to-image functionality to reimagine the space. THIRD, AI Home Design also functions as a social sharing platform where users can draw inspiration and start conversations with one another. FINALLY, homeowners still need to furnish and populate their spaces even after they have decided on their designs. This is where YOLO comes in, helping the user to recognise objects, and creating outbound e-commerce links for them to buy items. AI is thus used here to smoothen homeowner-professional interactions and engender connections. Even after the hackathon, I am continually improving the app by creating new features (see slide deck!), such as tools to facilitate discussions, recommenders, or improved object detection. On the technical front, I aim to infuse more powerful models like CLIP, Segment Anything or YOLOv8. On the business front, I am building in-app services to serve new target groups like real estate agents and elder-friendly/disability-friendly retrofitting specialists. Join me on this journey to make interior design more seamless for users, and to use AI in a coherent, impactful way.