Fine-Tuning Vision Transformers and ONNX Model Deployment

Adapting modern foundation models for specialized image classification tasks requires a clear understanding of fine-tuning techniques and production-ready export workflows. This text-based course guides you step by step through applying Vision Transformer (ViT) architectures, managing training pipelines, and converting trained models to the high-performance ONNX format. What you'll learn: - Understand core concepts behind Vision Transformers, attention mechanisms, and transfer learning - Prepare and preprocess specialized image datasets for classification tasks - Fine-tune pretrained vision models using structured PyTorch workflows - Evaluate model performance, manage training checkpoints, and track loss metrics - Convert and validate trained models in ONNX format for cross-platform deployment - Apply modern MLOps practices for model versioning, optimization, and inference testing You will start with essential terminology and fundamental deep learning principles before progressing through practical code walkthroughs, parameter adjustments, and export strategies. This course is designed for developers, data enthusiasts, and beginners interested in modern computer vision workflows with no advanced prerequisites required. Start reading today to master transformer-based image classification and deployment.