🖼️Understand Image Embeddings and Visual Search
Bridge from text embeddings to image embeddings, then design a duplicate-photo finder for your own library — without ever reaching for perceptual hashes.
Phase 1Pictures as Vectors
See pictures the way a vector model does
An image embedding is just an address in a map
6 minAn image embedding is just an address in a map
A vision encoder turns pixels into a vector
7 minA vision encoder turns pixels into a vector
CLIP trained images and text in the same room
7 minCLIP trained images and text in the same room
Cosine similarity is the only operation that matters (again)
6 minCosine similarity is the only operation that matters (again)
Phase 2Embed and Rank
Embed ten photos and rank them by similarity
Embed ten photos with one model
7 minEmbed ten photos with one model
Rank photos against a query image
7 minRank photos against a query image
Calibrate a duplicate threshold
8 minCalibrate a duplicate threshold
Visualize the embedding cloud
8 minVisualize the embedding cloud
Text-to-image search across your photos
7 minText-to-image search across your photos
Phase 3Pick Your Model
Pick between CLIP, DINOv2, and SigLIP for your task
CLIP, DINOv2, SigLIP: the three you'll actually reach for
8 minCLIP, DINOv2, SigLIP: the three you'll actually reach for
Model size: bigger isn't automatically better
8 minModel size: bigger isn't automatically better
Fine-tuning vs prompting your image embeddings
8 minFine-tuning vs prompting your image embeddings
Embedding drift, version pinning, and re-indexing
8 minEmbedding drift, version pinning, and re-indexing
Phase 4Design the Finder
Sketch a duplicate-photo finder for a real library
Sketch a duplicate-photo finder for your library
20 minSketch a duplicate-photo finder for your library
Frequently asked questions
- What is an image embedding?
- This is covered in the “Understand Image Embeddings and Visual Search” learning path. Start with daily 5-minute micro-lessons that build from fundamentals to hands-on application.
- How are image embeddings different from perceptual hashes?
- This is covered in the “Understand Image Embeddings and Visual Search” learning path. Start with daily 5-minute micro-lessons that build from fundamentals to hands-on application.
- What does CLIP actually do?
- This is covered in the “Understand Image Embeddings and Visual Search” learning path. Start with daily 5-minute micro-lessons that build from fundamentals to hands-on application.
- When should I use DINOv2 instead of CLIP?
- This is covered in the “Understand Image Embeddings and Visual Search” learning path. Start with daily 5-minute micro-lessons that build from fundamentals to hands-on application.
- How do I build a duplicate-photo finder with embeddings?
- This is covered in the “Understand Image Embeddings and Visual Search” learning path. Start with daily 5-minute micro-lessons that build from fundamentals to hands-on application.
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