# AI3.2 Image Models

This skill introduces key concepts and architectures for image-based AI models, such as CNNs and vision transformers, along with their training and deployment in HPC environments. It emphasizes dataset handling, parallel processing, and acceleration techniques.

## Requirements

* External: Basic understanding of computer vision and convolutional neural networks
* Internal: None

## Learning Outcomes

* Identify common architectures used in image modeling (e.g., ResNet, EfficientNet, Vision Transformers).
* Describe the data pipeline requirements for large-scale image datasets.
* Apply techniques for distributed training of image models in HPC environments.
* Explain GPU/TPU acceleration strategies for image model training and inference.
* Evaluate performance trade-offs between model size, accuracy, and runtime efficiency.

** Caution: All text is AI generated **