Prompt Engineering for Vision Models
A hands-on introduction to prompting and controlling vision models for image segmentation, object detection, image generation, in-painting, and personalized image generation.
About
Prompt Engineering for Vision Models is a beginner-level short course from DeepLearning.AI in collaboration with Comet. It extends prompt engineering beyond text-based models and demonstrates how vision models can be controlled using natural language, pixel coordinates, bounding boxes, segmentation masks, and generation parameters. Learners work with technologies including Meta's Segment Anything Model, OWL-ViT, Stable Diffusion, and DreamBooth while exploring image segmentation, object detection, image generation, in-painting, fine-tuning, and experiment tracking.
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Learning Outcomes
- Understand how prompting differs across text and vision models
- Prompt vision models using text, coordinates, and bounding boxes
- Adjust image generation parameters such as guidance scale, strength, and inference steps
- Use image segmentation models to isolate parts of an image
- Use natural-language prompts for zero-shot object detection
- Combine segmentation, object detection, and image generation for in-painting
- Understand how DreamBooth can personalize diffusion model outputs
- Use experiment tracking to compare visual prompting and hyperparameter configurations
Skills Covered
Prompt Engineering, Computer Vision, Multimodal Prompting, Image Generation, Image Segmentation, Object Detection, Diffusion Models, Fine-Tuning, In-painting, AI Personalization, Experiment Tracking
Syllabus
- Introduction
Introduces visual prompt engineering and the goals of the course.
- Overview
Explains the vision models, prompting methods, and workflows used throughout the course.
- Image Segmentation
Explores prompting image segmentation models with positive and negative coordinates and bounding boxes.
- Object Detection
Uses natural-language prompts with object detection models to locate and isolate objects within images.
- Image Generation
Explores diffusion-based image generation and parameters such as guidance scale, strength, and inference steps.
- Fine-tuning
Introduces personalization of diffusion models using DreamBooth and demonstrates how fine-tuning can provide greater control over generated images.
- Conclusion
Reviews the main visual prompt engineering techniques and workflows covered in the course.
Prerequisites
- Python experience recommended
- No advanced computer vision expertise required
Target Audience
AI designers, Developers, Prompt engineers, Creative technologists, AI content creators, Computer vision beginners, Generative AI practitioners
Best For
- Learners following an AI Designer learning path
- Prompt engineers expanding into multimodal AI
- Developers interested in generative image applications
- Creators who want to understand the technical foundations of AI image generation
- Python users beginning to explore computer vision and diffusion models
Pros
- Hands-on introduction to visual prompt engineering
- Covers multiple types of vision models rather than only image generation
- Includes practical code examples
- Covers segmentation, object detection, image generation, and fine-tuning
- Introduces DreamBooth personalization
- Demonstrates experiment tracking for iterative visual prompting
Cons
- Python experience is recommended
- Some models and techniques covered are specific implementations rather than a complete survey of modern vision systems
- The short format limits the depth of advanced computer vision theory
- Does not provide comprehensive production deployment training
Course Facts
- Provider:
- DeepLearning.AI
- Instructor:
- Abby Morgan, Jacques Verré and Caleb Kaiser
- Duration:
- 1 hour 22 minutes
- Level:
- Beginner
- Language:
- English
Certification & Delivery
Certificate Available
Format: Online, Self-paced, Video lessons, Interactive code examples
Tools you can use with this course
Stable Diffusion
A state-of-the-art latent diffusion model capable of generating photo-realistic images from text prompts.
- This course directly references the tool
- The tool category matches this course
DALL-E
A sophisticated AI model that generates original, high-quality images from text descriptions.
- The tool category matches this course
Ideogram
Ideogram is a cutting-edge AI image generator that specializes in generating high-quality images with precise, integrated text.
- The tool category matches this course
Leonardo.ai
A versatile generative AI platform for creating high-quality visual assets, specifically optimized for game development and creative projects.
- The tool category matches this course
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Related Glossary Terms
Fine-Tuning
Fine-tuning is the process of further training an existing AI model on additional task-specific or domain-specific data to modify its behavior or capabilities.
Multimodal AI
Multimodal AI refers to systems that can process, understand, or generate more than one type of information, such as text, images, audio, video, or structured data.