Building an Image Similarity Search Using CLIP and FAISS
I’ve always found it fascinating how Google Images can find visually similar images almost instantly. It feels magical — you upload one photo, and in seconds, it understands and matches it to millions of others. This weekend, I wanted to explore: Could I build a tiny, minimal version of that, using only open-source tools?
Choosing the Tools
After some research, I decided to experiment with two open-source projects:
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CLIP (by OpenAI): a model that converts images and text into embeddings, placing them into the same vector space where they can be meaningfully compared.
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FAISS (by Meta): a library designed for fast similarity search over large collections of embeddings, helping you find the nearest matches efficiently. Both seemed lightweight enough for a small project, yet powerful enough to give meaningful results.
What I Built
Here’s a short demo of the image similarity tool I created:
The tool lets you:
- Upload images to create a searchable index
- Search by uploading another image
- Or even search by typing a text query
Behind the scenes, CLIP generates embeddings, and FAISS efficiently finds the nearest matches.
You can find the complete code here: GitHub Repository
A Quick Look at How It Works
- When a user uploads an image, I convert it into a CLIP embedding.
- I store these embeddings in FAISS for fast retrieval.
- During search, the uploaded image or text query is embedded and compared against the stored vectors.
- The closest matches are shown back to the user.
Here’s the rough architecture I followed:
The app itself runs on Streamlit, keeping it simple and easy to experiment with.
What I Learned
Working with CLIP was a great experience. It’s impressive how it generalizes, being able to search images using text without any retraining felt almost like cheating.
However, I also ran into a few limitations:
- FAISS always returns top K results, even if no truly close match exists. With a small index, this can sometimes return completely unrelated images as “similar.”
- CLIP embeddings are semantic, not instance-specific. For example, two photos of the same cat in different poses don’t necessarily come close in embedding space. CLIP understands the “idea” of a cat, but not necessarily the identity of this cat.
- Handling file uploads in Streamlit needed a bit of care to avoid processing the same file multiple times when switching tabs.
These are all natural limitations of the tools — and understanding them was part of the fun.
Reflections
This project reminded me how much can be achieved with simple, well-built tools. I didn’t train a single model. I didn’t build a database from scratch. I just connected two smart pieces — CLIP and FAISS — and a lot of the magic simply happened. It also made me think: Sometimes, creativity isn’t about building everything yourself, but knowing how to connect things meaningfully.
What’s Next?
This experiment opened up a few ideas I’d like to explore further:
- Fine-tuning a model for better instance-specific matching
- Experimenting with more advanced search methods (e.g., filtering by confidence score)
- Building a small backend to make this tool scalable beyond a weekend project
There’s a lot more to do if I want to make this production-grade. But for now, I’m happy with where this experiment landed.
Thanks for reading!
If you have thoughts, ideas, or just want to geek out about embeddings, feel free to connect!