CPU Inference with Florence-2 and ONNX in C#

Deploying modern multimodal AI models on local CPU environments enables cost-effective, private, and portable application workflows. This text-based course guides you through running Florence-2 vision models using ONNX Runtime within C# and .NET applications. You will understand how to optimize vision-language pipelines for CPU inference, manage model inputs and tokenization, and integrate inference logic directly into enterprise .NET solutions. What you'll learn: - Understand foundational concepts of vision-language models and ONNX execution - Configure the ONNX Runtime engine inside modern C# applications - Process images and text inputs for Florence-2 model pipelines - Execute lightweight CPU-based inference without requiring dedicated GPUs - Parse model outputs, bounding boxes, and generated captions in C# - Apply basic LLMOps best practices for local model versioning and performance monitoring The course starts with essential terminology, architectural foundations, and setup guidelines before guiding you through structured written code implementations and optimization techniques. This course is designed for C# developers, software engineers, and beginners looking to integrate local multimodal AI inference into their applications without complex cloud dependencies. Start reading today to bring efficient local computer vision and language inference into your C# projects.