IBM Maximo Visual Inspection

In this article, we will describe how we implemented a workplace safety use case involving the application and device layer of the edge computing architecture.

When you use Maximo Visual Inspection (MVI) for object detection, you might require additional categorization that is dependent on specific text on the object, such as recognizing brands or sub-brands. To help with this additional categorization, you can use MVI with an optical character recognition (OCR) solution if the confidence scores are low.

Combine the power of IBM intelligent solutions with the breadth and depth of AWS to unleash new levels of performance.

Learn how the IBM Maximo Visual Inspection intuitive toolset empowers subject-matter experts to label, train, and deploy computer vision models without coding or data science expertise.

Classify images of cats and dogs by extracting Histogram of Oriented Gradients features from them for a Support Vector Machine model! In particular, you will be able to feed an image to the model yourself and get a prediction.

Have you ever wanted to transform your photographs into an artistic sketch or painting to showcase your creativity?  In this Guided Project you''ll do just that! Transform your photographs to paintings, sketches and more using OpenCV in python.

Discover how to build a multi-agent app using CrewAI, Gradio, and multimodal AI. This app will analyze uploaded food images to extract nutrition data, and create personalized recipes. This guided project teaches skills in multi-agent systems, computer vision, LLMs, and agentic AI. Perfect for developers or AI engineers, you''ll integrate cutting-edge AI tools to design a user-friendly app that empowers users with actionable meal insights. Apply advanced AI methods to solve real-world challenges and expand your expertise in artificial intelligence.

Use the Llama-3.2-90B-Vision-Instruct Model and generative AI to build an AI Nutrition Coach that estimates caloric and nutritional content from food images! Learn to integrate visual recognition with nutritional databases to deliver instant calorie counts and personalized dietary advice. This guided project empowers you to use AI for promoting healthier lifestyle choices, offering hands-on experience in applying advanced AI models to real-world dietary management. Ideal for tech-savvy health enthusiasts and dietetics students, you can complete this project in just 60 minutes.

Learn how to make your object detection application available to the world by deploying to a serverless environment. Focus on building your app instead of buying, installing or configuring servers.

Learn how to code a simple image Q&A system using IBM watsonx and Llama 3.2 in this quick 30-minute project. You''ll learn how to set up and run a model that answers questions about images, making it easy to see how multimodal LLMs can bridge the gap between visuals and language. This project is straightforward and perfect for developers or AI enthusiasts who want to build practical, interactive tools with minimal effort.