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.

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

• 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