Optimizing ViViT Video Classifiers with ONNX for CPU Inference

Deploying transformer-based video models often demands heavy compute resources, making efficient deployment a critical challenge. This written guide provides a practical path to converting Video Vision Transformer (ViViT) models into the Open Neural Network Exchange (ONNX) format for optimized CPU execution. You will build a thorough understanding of model exporting, graph optimizations, and runtime execution. Through structured explanations and clear code examples, you will learn how to prepare video inputs, export model weights, and benchmark CPU inference latency without sacrificing classification accuracy. What you'll learn: - Understand core concepts of Video Vision Transformers (ViViT) and inference optimization pipelines - Export PyTorch-based video classification models into the universal ONNX format - Configure ONNX Runtime execution providers for optimized CPU performance - Preprocess video frame sequences and manage multidimensional tensors for transformer architectures - Apply model quantization and graph optimization techniques to reduce compute overhead - Benchmark latency and evaluate model performance across real-world inference scenarios The material begins with key terminology and architectural fundamentals before moving step-by-step through practical export routines, performance profiling, and optimization workflows. This course is suitable for developers, data scientists, and machine learning practitioners who understand basic Python and want to deploy computer vision models efficiently on everyday hardware. Begin reading to streamline your video model deployment pipeline today.