ViViT Video Classifier Deployment with ONNX for CPU Inference

Deploying complex vision transformer models for video classification often leads to high latency and intensive hardware demands. This course guides you through the practical process of converting Video Vision Transformers (ViViT) into the open ONNX format, enabling fast and resource-efficient inference on standard CPU hardware. Through structured readings and code examples, you will explore model optimization workflows from initial setup to production-ready deployment pipelines. You will discover how ONNX Runtime accelerates execution without requiring dedicated GPUs, making video understanding models accessible and cost-effective. What you'll learn: - Understand the core architecture and operational principles of Video Vision Transformers (ViViT) - Export PyTorch-based ViViT models into the optimized ONNX format - Configure ONNX Runtime to execute high-throughput video inference on standard CPUs - Apply post-training quantization techniques to reduce model footprint and improve execution speed - Preprocess video frames and manage dynamic input shapes effectively - Validate exported model accuracy against baseline PyTorch outputs The course begins with fundamental concepts of vision transformers and ONNX before transitioning into model conversion, runtime tuning, and performance benchmarking. Designed for aspiring machine learning practitioners and developers, no previous MLOps or model optimization experience is required. Start reading today to streamline your video classification deployments.