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We're here to help you find itUnlock the power of visual AI with our Prompt Engineering for Vision Models course. In just one day, dive into the latest techniques that are revolutionizing image generation, segmentation, and object detection. You'll gain hands-on experience with cutting-edge models like Meta's SAM, OWL-ViT, and Stable Diffusion 2.0. Learn how to tailor these models to your needs using DreamBooth for fine-tuning, enhancing personalization in your projects. Whether it’s generating unique images or refining AI's understanding through iterative prompting and experiment tracking with Comet, this course prepares you to implement practical, impactful AI solutions in various visual tasks. Equip yourself to lead in the AI-driven visual landscape!
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♱ Excluding VAT/GST
Classroom Training price is on request
You can request classroom training in any city on any date by Requesting More Information
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:
These prerequisites are intended to ensure you have a smooth learning experience and can fully engage with the advanced content of the course.
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:
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:
OWL-ViT (Object-Attribute Learning through Vision Transformer) is a computer vision model that enhances the understanding of objects in images. It utilizes a Vision Transformer architecture, a model that has gained popularity for its proficiency in handling image-related tasks. By learning to recognize both the objects and their attributes, OWL-ViT facilitates better image recognition and detailed image descriptions. This model is commonly implemented using frameworks like PyTorch and TensorFlow, which are tools that provide libraries for building and training advanced machine learning and computer vision models efficiently.
Stable Diffusion 2.0 is an advanced computer vision model that leverages deep learning techniques to generate high-quality images based on text descriptions. Built using PyTorch, a popular framework for model training, this technology demonstrates significant capabilities in understanding and visualizing complex concepts from simple inputs. Its functionality is crucial in fields requiring detailed visual output from textual data, enhancing creative processes and automating content generation efficiently while ensuring a high level of detail and contextual accuracy.
Image generation in computer vision is a technique used to create new images from existing data. It often involves training models like PyTorch or TensorFlow, which are frameworks that provide tools for computer vision tasks. These models learn from large datasets to generate images that can be realistic or abstract, depending on the training. They are used in various applications such as art creation, enhancing image resolution, or generating training images for further computer vision model training. This allows for continuous improvement and application of generated images across different fields.
Object detection is a technology within computer vision that enables computers and systems to identify and locate objects within an image or video. Using models trained via platforms like TensorFlow or PyTorch, this technology can differentiate and classify various objects, from pedestrians in autonomous driving applications to defects in manufacturing. The training involves showing the computer vision model numerous examples so it can learn and improve its accuracy over time in detecting and classifying objects correctly. This capability is fundamental in applications ranging from security surveillance to interactive gaming.
Meta's SAM, or Sample Adaptive Multiple (SAM) optimization, is a method used primarily to enhance the training of computer vision models. It focuses on refining the model's accuracy by dynamically adjusting the model parameters during training. Unlike traditional methods, SAM anticipates possible future errors and adjusts accordingly, leading to more robust models. This optimization technique is particularly influential in the fields of PyTorch computer vision and TensorFlow computer vision, where precision and error minimization are crucial for tasks like image recognition and automated driving systems.
Segmentation in computer vision is a process where an image is divided into parts to simplify and/or change the representation of an image into something that is more meaningful and easier to analyze. It's extensively used in computer vision model training to help machines recognize objects, boundaries, and scenes. Technologies like PyTorch and TensorFlow are popular tools for implementing segmentation techniques due to their powerful libraries and frameworks that assist in developing sophisticated computer vision models efficiently. These technologies enable precise and effective model training for tasks such as image classification, object detection, and more.
DreamBooth is a technology that enhances the capabilities of generative AI models to produce customized and personalized images by training on specific subjects using PyTorch or TensorFlow. It allows users to teach a computer vision model to recognize and replicate the style and details of specific objects or characters, significantly improving the quality and relevance of the generated images. This customization deepens the connection between advanced computer vision tasks and user-specific requirements, bridging the gap between general content creation and personalized user experiences in digital media.
Iterative prompting in the context of advanced machine learning involves progressively refining the input prompts provided to a model to generate more accurate and relevant outputs. This technique is particularly useful in computer vision model training, where each iteration helps improve the model's ability to interpret visual data more effectively. Iterative prompting can be implemented using frameworks like PyTorch or TensorFlow, which are optimized for building sophisticated computer vision systems, thereby enhancing the model's learning from each successive prompt.
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:
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:
