Image recognition with Python, OpenCV, OpenAI CLIP and pgvector
In this video you’ll learn how to build an offline face recognition pipeline to find faces on top of complex pictures. The full written explanation is available in the dedicated article
The pipeline will use:
Python and OpenCV to detect faces within complex pictures
Python and an OpenAI CLIP model to calculate the face embeddings
PostgreSQL and the pgvector extension to store the embeddings and calculate distance across them
Check out these resources to learn more:
Step by step article: https://aiven.io/developer/find-faces...
Pgvector blog post announcement: https://aiven.io/blog/aiven-for-postg...
OpenCV GitHub Repository: https://github.com/opencv/opencv/tree...
OpenAI CLIP model: https://aiven.io/developer/find-faces...
PGVector: https://github.com/pgvector/pgvector
CHAPTERS
01:04 What are embeddings?
02:40 Embeddings in images
03:15 The face recognition pipeline steps
04:45 Prepare the PostgreSQL database
06:20 Create the face recognition pipeline
07:22 Detect faces with OpenCV
09:08 Calculate embeddings with imgembeddings and OpenAI CLIP model
10:48 Calculate image similarity using pgvector distance
11:46 Summary
Follow Francesco Tisiot on social media:
Twitter: / ftisiot
LinkedIn: / francescotisiot
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