Fine-Tuning ViViT Video Classifiers with Custom Datasets

Processing and classifying dynamic video content requires modern deep learning architectures capable of handling both spatial and temporal data. This text-based course guides you through customizing Video Vision Transformer (ViViT) models for specialized video classification tasks using your own datasets. You will build a solid understanding of transformer-based computer vision and gain practical skills in data preprocessing, model fine-tuning, and operationalizing machine learning pipelines. What you'll learn: - Understand core concepts and architectures behind Video Vision Transformers (ViViT) - Prepare, extract frames, and augment custom video datasets for model training - Fine-tune pretrained ViViT classifiers efficiently using modern Python libraries - Evaluate classification accuracy and spatio-temporal performance metrics - Implement foundational MLOps practices for experiment tracking and model versioning - Configure inference pipelines for automated video classification tasks The material starts with essential terminology and data handling fundamentals before advancing through step-by-step model training, evaluation, and deployment strategies. This course is suitable for developers, data scientists, and machine learning enthusiasts interested in video analysis. Basic familiarity with Python and fundamental machine learning concepts is recommended, but no prior transformer experience is required. Begin reading today to master custom video classification workflows with ViViT.