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Update docs sidebar name (#16370)

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* Clarify classification

* Fix face hierarchy as well
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Nicolas Mowen 2025-02-07 13:12:44 -07:00 committed by Blake Blackshear
parent f3485bfc13
commit 15472274ee
3 changed files with 3 additions and 3 deletions
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docs/docs/configuration/face_recognition.md
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@ -54,6 +54,6 @@ When first enabling face recognition it is important to build a foundation of st
Then it is recommended to use the `Face Library` tab in Frigate to select and train images for each person as they are detected. When building a strong foundation it is strongly recommended to only train on images that are straight-on. Ignore images from cameras that recognize faces from an angle. Once a person starts to be consistently recognized correctly on images that are straight-on, it is time to move on to the next step. Then it is recommended to use the `Face Library` tab in Frigate to select and train images for each person as they are detected. When building a strong foundation it is strongly recommended to only train on images that are straight-on. Ignore images from cameras that recognize faces from an angle. Once a person starts to be consistently recognized correctly on images that are straight-on, it is time to move on to the next step.
# Step 2 - Expanding The Dataset ### Step 2 - Expanding The Dataset
Once straight-on images are performing well, start choosing slightly off-angle images to include for training. It is important to still choose images where enough face detail is visible to recognize someone. Once straight-on images are performing well, start choosing slightly off-angle images to include for training. It is important to still choose images where enough face detail is visible to recognize someone.

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docs/docs/configuration/semantic_search.md
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@ -1,6 +1,6 @@
--- ---
id: semantic_search id: semantic_search
title: Using Semantic Search title: Semantic Search
--- ---
Semantic Search in Frigate allows you to find tracked objects within your review items using either the image itself, a user-defined text description, or an automatically generated one. This feature works by creating _embeddings_ — numerical vector representations — for both the images and text descriptions of your tracked objects. By comparing these embeddings, Frigate assesses their similarities to deliver relevant search results. Semantic Search in Frigate allows you to find tracked objects within your review items using either the image itself, a user-defined text description, or an automatically generated one. This feature works by creating _embeddings_ — numerical vector representations — for both the images and text descriptions of your tracked objects. By comparing these embeddings, Frigate assesses their similarities to deliver relevant search results.

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docs/sidebars.ts
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@ -33,7 +33,7 @@ const sidebars: SidebarsConfig = {
'configuration/object_detectors', 'configuration/object_detectors',
'configuration/audio_detectors', 'configuration/audio_detectors',
], ],
'Semantic Search': [ Classifiers: [
'configuration/semantic_search', 'configuration/semantic_search',
'configuration/genai', 'configuration/genai',
'configuration/face_recognition', 'configuration/face_recognition',