To ensure you are well-prepared and can benefit maximally from the Prompt Engineering for Vision Models course at Koenig Solutions, here are the minimum required prerequisites:
- Basic understanding of artificial intelligence and machine learning concepts: Familiarity with foundational ideas in AI and ML will help you grasp the course content more effectively.
- Introductory knowledge of computer vision: Understanding basic concepts such as image recognition, object detection, and image processing will be beneficial.
- Experience with Python programming: Since the course involves practical training using Python libraries and frameworks, basic programming skills in Python are necessary.
- Familiarity with data handling and manipulation: Basic skills in handling datasets, especially images, will be helpful during the course exercises.
- Interest in AI-driven image processing: A keen interest in exploring how AI can be used to generate, modify, and enhance images will make the learning process more engaging and insightful.
These prerequisites are intended to ensure you have a smooth learning experience and can fully engage with the advanced content of the course.
Target Audience for Prompt Engineering for Vision Models
Learn essential skills in prompt engineering for vision models such as SAM, OWL-ViT, and Stable Diffusion 2.0, aimed at enhancing AI-driven image processing and customization.
Target Audience:
- Data Scientists
- Machine Learning Engineers
- AI Researchers
- Computer Vision Engineers
- Software Developers involved in AI and image processing
- Tech Product Managers
- AI Hobbyists and Tech Enthusiasts
- Academic Researchers in Computer Science
- Content Creators and Digital Artists
- IT Professionals looking to integrate AI vision capabilities into applications
Learning Objectives - What you will Learn in this Prompt Engineering for Vision Models?
Introduction to Course Learning Outcomes and Concepts:
In this one-day course, you will master prompt engineering for various vision models, employing techniques like image generation, segmentation, object detection, and fine-tuning with DreamBooth, enhanced by experiment tracking using Comet.
Learning Objectives and Outcomes:
- Master Image Generation: Learn to prompt vision models using text and manipulate results by adjusting key hyperparameters such as strength, guidance scale, and inference steps.
- Understand Image Segmentation: Gain skills in prompting models using both positive or negative coordinates, and employing bounding box coordinates for precise segmentation.
- Explore Object Detection: Develop the ability to use natural language prompts to accurately produce bounding boxes for isolating specific objects within images.
- Implement In-painting Techniques: Combine skills in generation, segmentation, and detection to replace objects within images with newly generated content.
- Personalize with DreamBooth: Use DreamBooth for fine-tuning models to generate custom imagery based on personal photos of people or places.
- Iterate Prompt Engineering Processes: Understand the iterative nature of prompt engineering and learn techniques for refining prompts to achieve desired outcomes.
- Experiment Tracking with Comet: Learn how to use Comet for tracking experiments, an essential tool to optimize
