- 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.
-
+
{t("classification.semanticSearch.desc")}
- Read the Documentation
+ {t("classification.semanticSearch.readTheDocumentation")}
@@ -242,7 +243,9 @@ export default function ClassificationSettingsView({
}}
/>
-
+
@@ -259,31 +262,38 @@ export default function ClassificationSettingsView({
}}
/>
-
+
- Re-indexing will reprocess all thumbnails and descriptions (if
- enabled) and apply the embeddings on each startup.{" "}
- Don't forget to disable the option after restarting!
+
+ classification.semanticSearch.reindexOnStartup.desc
+
- The size of the model used for Semantic Search embeddings.
+
+ classification.semanticSearch.modelSize.desc
+
- Using small employs a quantized version of the
- model that uses less RAM and runs faster on CPU with a very
- negligible difference in embedding quality.
+
+ classification.semanticSearch.modelSize.small.desc
+
- Using large employs the full Jina model and will
- automatically run on the GPU if applicable.
+
+ classification.semanticSearch.modelSize.large.desc
+
- Face recognition allows people to be assigned names and when
- their face is recognized Frigate will assign the person's name
- as a sub label. This information is included in the UI, filters,
- as well as in notifications.
-
+
{t("classification.faceRecognition.desc")}
- Read the Documentation
+ {t("classification.faceRecognition.readTheDocumentation")}
@@ -361,7 +366,9 @@ export default function ClassificationSettingsView({
}}
/>
- Frigate can recognize license plates on vehicles and
- automatically add the detected characters to the
- recognized_license_plate field or a known name as a sub_label to
- objects that are of type car. A common use case may be to read
- the license plates of cars pulling into a driveway or cars
- passing by on a street.
-