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Build an Image Search Engine with OpenAI's CLIP Embeddings

IntermediateGuided Project

Learn the fundamentals of building Google's reverse image search. Build your own embeddings-based implementation from scratch using OpenAI's CLIP model. Develop a recommendation system that uses semantic image search with CLIP's multimodal embedding architecture. Discover how to visualize high-dimensional vector spaces with spatial reduction algorithms. By the end, you will have created a beautiful semantic map revealing latent relationships across unlabeled datasets.

Language

  • English

Topic

  • Computer Vision

Skills You Will Learn

  • Machine Learning, Embeddable AI, Generative AI, Computer Vision, Python

Offered By

  • IBMSkillsNetwork

Estimated Effort

  • 30 minutes

Platform

  • SkillsNetwork

Last Update

  • June 11, 2025
About this Guided Project
With the rise of massive visual datasets and multimodal models like CLIP, we now have powerful tools to understand and organize visual media in entirely new ways. This guided project explores how to use CLIP image embeddings to create an  "flower map"—a visual map of relationships between images of flowers based on their semantic content. Instead of organizing images by filename or metadata, we’ll use deep learning to position similar images close to each other in a low-dimensional space, enabling intuitive and visually meaningful exploration. Whether you're a machine learning enthusiast, a botanist, or simply curious about how machines "see" images, this project offers a creative and practical application of state-of-the-art models for visual understanding.

What You'll Learn

By the end of this project, you will be able to:
  • Compute and explore image embeddings: Learn how to use the CLIP model to convert flower images into numerical vectors that capture their visual and conceptual features.
  • Visualize high-dimensional data: Apply dimensionality reduction techniques to project embeddings into 2D and create visually engaging plots that reveal clusters and relationships in your image dataset.
  • Build a semantic image map: Construct a visual map where similar images naturally group together, enabling intuitive exploration of large collections without labels or manual categorization.

Who Should Enroll

  • Researchers across disciplines interested in using visual semantic maps to automatically discover patterns in large datasets that would be impossible to detect manually. Scientists studying everything from medical imaging to archaeological artifacts can use these tools to identify clusters, outliers, and relationships that reveal new insights about their subjects.
  • Machine Learning Enthusiasts with a basic to intermediate understanding of ML concepts who want to experiment with powerful pretrained models like CLIP. This project will provide a practical and creative walkthrough of multimodal models and teach useful concepts like embedding generation, dimensionality reduction, and visualization.
  • Hobbyists of anything! Whether it's flowers, antique coins, or rocks, a semantic search engine lets hobbyists find specimens by visual similarity rather than keywords - they can upload a photo of an unknown flower or rock and instantly discover similar items in their collection or database.

Why Enroll

This project bridges the gap between advanced AI theory and practical application, giving you hands-on experience with one of today's most powerful multimodal models. You'll build genuinely useful personal tools that can organize and search any visual collection, from research datasets to personal photos. The skills you learn—embedding generation, dimensionality reduction, and semantic search—are foundational to modern computer vision and applicable far beyond this flower example. By the end, you'll have both working applications and the knowledge to adapt these techniques to your own visual data challenges.

What You'll Need

To follow along with this guided project, you should have a basic understanding of Python and some familiarity with libraries like matplotlib and PIL. Experience with Jupyter notebooks will be helpful, but not required. All necessary dependencies and datasets are available in the IBM Skills Network Labs environment. The platform works best with current versions of Chrome, Edge, Firefox, or Safari.

Instructors

Tenzin Migmar

Data Scientist

Hi, I'm Tenzin. I'm a data scientist intern at IBM interested in applying machine learning to solve difficult problems. Prior to joining IBM, I worked as a research assistant on projects exploring perspectivism and personalization within large language models. In my free time, I enjoy recreational programming and learning to cook new recipes.

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Contributors

Joseph Santarcangelo

Senior Data Scientist at IBM

Joseph has a Ph.D. in Electrical Engineering, his research focused on using machine learning, signal processing, and computer vision to determine how videos impact human cognition. Joseph has been working for IBM since he completed his PhD.

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Faranak Heidari

Data Scientist at IBM

Detail-oriented data scientist and engineer, with a strong background in GenAI, applied machine learning and data analytics. Experienced in managing complex data to establish business insights and foster data-driven decision-making in complex settings such as healthcare. I implemented LLM, time-series forecasting models and scalable ML pipelines. Enthusiastic about leveraging my skills and passion for technology to drive innovative machine learning solutions in challenging contexts, I enjoy collaborating with multidisciplinary teams to integrate AI into their workflows and sharing my knowledge.

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Karan Goswami

Data Scientist

I am a dedicated Data Scientist and an AI enthusiast, currently working at IBM's Skills Builder Network. Learning how some simple mathematical operations could be used to make predictions and discover patterns sparked my curiosity, leading me to explore the exciting world of AI. Over the years, I’ve gained hands-on experience in building scalable AI solutions, fine-tuning models, and extracting meaningful insights from complex datasets. I'm driven by a desire to apply these skills to solve real-world problems and make a meaningful impact through AI.

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