From 0b126c52f8d2fb688735656b51c572daaa1843f5 Mon Sep 17 00:00:00 2001 From: Sefik Ilkin Serengil Date: Thu, 8 Aug 2024 10:30:49 +0100 Subject: [PATCH] Update README.md big data illustration added --- README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/README.md b/README.md index 206e7fc..e43e4f3 100644 --- a/README.md +++ b/README.md @@ -375,6 +375,8 @@ You can also run these commands if you are running deepface with docker. Please If your task requires facial recognition on large datasets, you should combine DeepFace with a vector index or vector database. This setup will perform [approximate nearest neighbor](https://youtu.be/c10w0Ptn_CU) searches instead of exact ones, allowing you to identify a face in a database containing billions of entries within milliseconds. Common vector index solutions include [Annoy](https://youtu.be/Jpxm914o2xk), [Faiss](https://youtu.be/6AmEvDTKT-k), [Voyager](https://youtu.be/2ZYTV9HlFdU), [NMSLIB](https://youtu.be/EVBhO8rbKbg), [ElasticSearch](https://youtu.be/i4GvuOmzKzo). For vector databases, popular options are [Postgres with its pgvector extension](https://youtu.be/Xfv4hCWvkp0) and [RediSearch](https://youtu.be/yrXlS0d6t4w). +

+ Conversely, if your task involves facial recognition on small to moderate-sized databases, you can adopt use relational databases such as [Postgres](https://youtu.be/f41sLxn1c0k) or [SQLite](https://youtu.be/_1ShBeWToPg), or NoSQL databases like [Mongo](https://youtu.be/dmprgum9Xu8), [Redis](https://youtu.be/X7DSpUMVTsw) or [Cassandra](https://youtu.be/J_yXpc3Y8Ec) to perform exact nearest neighbor search. ## Contribution