Computer Vision: Vision Transformers & Vision Language Model

This course contains the use of free artificial intelligence risks in cybersecurity course

Disclosure: AI tools were used only to assist in creating the course outline and course thumbnail. All instructional content, explanations, and project walkthroughs were fully created manually by the instructor.

Welcome to Computer Vision: Vision Transformers & Vision Language Model course. This is a comprehensive project based course where you will learn how to build modern computer vision applications using Vision Transformers, Segment Anything Model, Contrastive Language Image Pre Training, attention mechanism, and other AI models. This course is a perfect combination between artificial intelligence and computer vision, making it an ideal opportunity for you to practice your programming skills while improving your technical knowledge in deep learning. In the introduction session, you will learn the basic fundamentals of Vision Transformers and Vision Language Model, such as getting to know its use cases and how the system works. Then, in the next section, we will start the projects, in the first project, we are going to build a satellite image classification system using Vision Transformers. This system will be able to analyze satellite images and categorize different land types, for example, forests, rivers, residential areas, industrial areas, and highways. Then, in the second project, we are going to build a soil type classification system using Vision Transformers. This system will enable us to analyze soil images and classify different soil categories like black soil, clay soil, red soil, and other soil types. Afterward, in the third project, we are going to perform image segmentation using the Segment Anything Model. Firstly, we will remove product backgrounds by isolating the main object from its surrounding environment to create clean product images for e-commerce. After that, we will also segment flood areas by identifying and separating water affected regions from aerial images to support disaster monitoring and analysis. Then, in the fourth project, we are going to categorize product images using Contrastive Language–Image Pre-Training. By doing so, we will be able to automate product categorization for inventory management by analyzing product images and assigning them to the relevant categories. Additionally, we will also build a visual search engine for fashion product recommendations, where users can upload a product photo and the system will be able to find and recommend visually similar products based on image pattern. Next, in the fifth project, we are going to build a multi object tracking system using ByteTrack and attention mechanisms. Specifically, the system will track multiple drones in video footage by detecting and maintaining the identity of each drone across different frames. In the sixth project, we are going to use Vision Language Models such as Gemini and Mistral to build a smart home security system that is able to analyze CCTV footage, understand the surrounding environment, identify objects and activities, and generate detailed descriptions or security alerts based on what is happening in the scene. Additionally, we are also going to build a property description generator that is able to analyze real estate images and automatically create detailed property descriptions. Then, in the seventh project, we are going to build a Visual Question Answering system for a retail inventory assistant. The system will allow us to upload inventory images and ask questions about stock availability, product quantity, and shelf conditions, and the AI will be able to provide answers based on the given image. In the eight project, we are going to build an free object detection from zero to hero course system using Retina Net. This model is a pre-trained model that does not require additional training. Lastly, at the end of the course, we are going to perform optical character recognition using the GPT model. We will upload an image and the model will extract text from the image.

First of all, before getting into the course, we need to ask this question to ourselves. Why should we use Vision Transformers and Vision Language Models? Well, here is my answer. These models have become the foundation of many modern computer vision applications because they can understand visual information with remarkable accuracy and flexibility. As AI continues to evolve, learning how to build applications with Vision Transformers and Vision Language Models will equip you with valuable skills.

Below are things that you can expect to learn from this course:

• Learn the basic fundamentals of Vision Transformers and Vision Language Model