This course offers a comprehensive introduction to the art of prompt engineering, particularly tailored to vision models. It covers various techniques such as text prompting, fine-tuning hyperparameters, image segmentation, and diffusion models for personalization. Through practical applications and the use of sophisticated models like SAM, OWL-ViT, and Stable Diffusion 2.0, participants will learn how to manipulate and generate images with high precision.
- Skills in prompting diverse vision models using text, coordinates, and bounding boxes
- Techniques for image in-painting and object detection
- Hyperparameter adjustment to fine-tune the results of vision models
- Personalized image generation using DreamBooth fine-tuning technique
- Methods for tracking experiments and optimizing the prompt engineering process using Comet
A basic understanding of Python and familiarity with fundamental concepts of machine learning and image processing is recommended.
- Introduction to Vision Models and Prompt Engineering
- Techniques in Image Generation: Usage of Text and Hyperparameter Adjustments
- Image Segmentation Methods: Utilization of Positive/Negative Coordinates and Bounding Boxes
- Object Detection Through Natural Language Prompts
- In-painting: Merging Detection, Segmentation, and Generation
- Personalization through Fine-tuning with DreamBooth
- Iterative Techniques and Experiment Tracking with Comet
Ideal for software developers, aspiring data scientists, and machine learning enthusiasts who possess a foundational understanding of Python and seek to delve into advanced image processing and generation using modern AI techniques.
Skills learned in this course can be applied in various domains such as developing advanced features in tech applications, creating personalized media content, enhancing visual data analysis, automating and refining user interactions in apps involving image data, and much more.