Prompt Engineering for Vision Models - Syllabus

• Introduction
  • Overview of prompt engineering techniques for vision models
  • Course objectives and key concepts
• Overview
  • Exploring various vision models and their applications
  • Introduction to models like SAM, OWL-ViT, and Stable Diffusion
• Image Segmentation
  • Prompting with positive and negative coordinates
  • Using bounding boxes for precise segmentation
• Object Detection
  • Using natural language prompts for object detection
  • Generating bounding boxes for targeted object isolation
• Image Generation
  • Generating images with text prompts and adjusting hyperparameters
  • Using guidance scale, strength, and inference steps
• Fine-tuning
  • Personalizing image generation with DreamBooth
  • Creating custom images by associating text labels with objects
• Conclusion
  • Recap of vision model prompt engineering techniques
  • Best practices and tips for iterating and tracking experiments
• Appendix
  • Additional code examples and resources
  • Troubleshooting and tips for effective prompt engineering