At a Glance

Name: Build an Image Search Engine with OpenAI's CLIP Embeddings
Price: Free CAD
Rating: 0.0 (0 reviews)
Author: tenzin_migmar, joseph_santarcangelo, faranak_heidari, karan_goswami

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.

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.

Offered By: IBMSkillsNetwork

Build an Image Search Engine with OpenAI's CLIP Embeddings

At a Glance

What You'll Learn

Who Should Enroll

Why Enroll

What You'll Need