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Fixes (#18117)

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* face library i18n fixes

* face library i18n fixes

* add ability to use ctrl/cmd S to save in the config editor

* Use datetime as ID

* Update metrics inference speed to start with 0 ms

* fix android formatted thumbnail

* ensure role is comma separated and stripped correctly

* improve face library deletion

- add a confirmation dialog
- add ability to select all / delete faces in collections

* Implement lazy loading for video previews

* Force GPU for large embedding model

* GPU is required

* settings i18n fixes

* Don't delete train tab

* webpush debugging logs

* Fix incorrectly copying zones

* copy path data

* Ensure that cache dir exists for Frigate+

* face docs update

* Add description to upload image step to clarify the image

* Clean up

---------

Co-authored-by: Nicolas Mowen <nickmowen213@gmail.com>
This commit is contained in:
Josh Hawkins
2025-05-09 08:36:44 -05:00
committed by GitHub
parent 52d94231c7
commit 8094dd4075
27 changed files with 402 additions and 195 deletions
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14
docs/docs/configuration/face_recognition.md
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@@ -34,7 +34,7 @@ All of these features run locally on your system.
The `small` model is optimized for efficiency and runs on the CPU, most CPUs should run the model efficiently.
The `large` model is optimized for accuracy, an integrated or discrete GPU is highly recommended. See the [Hardware Accelerated Enrichments](/configuration/hardware_acceleration_enrichments.md) documentation.
The `large` model is optimized for accuracy, an integrated or discrete GPU is required. See the [Hardware Accelerated Enrichments](/configuration/hardware_acceleration_enrichments.md) documentation.
## Configuration
@@ -107,17 +107,17 @@ When choosing images to include in the face training set it is recommended to al
### Step 1 - Building a Strong Foundation
When first enabling face recognition it is important to build a foundation of strong images. It is recommended to start by uploading 1-5 "portrait" photos for each person. It is important that the person's face in the photo is straight-on and not turned which will ensure a good starting point.
When first enabling face recognition it is important to build a foundation of strong images. It is recommended to start by uploading 1-5 photos containing just this person's face. It is important that the person's face in the photo is front-facing and not turned, this will ensure a good starting point.
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.
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 front-facing. Ignore images from cameras that recognize faces from an angle.
Aim to strike a balance between the quality of images while also having a range of conditions (day / night, different weather conditions, different times of day, etc.) in order to have diversity in the images used for each person and not have over-fitting.
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.
Once a person starts to be consistently recognized correctly on images that are front-facing, it is time to move on to the next step.
### 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 front-facing 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.
## FAQ
@@ -156,3 +156,7 @@ Face recognition does not run on the recording stream, this would be suboptimal
### I get an unknown error when taking a photo directly with my iPhone
By default iOS devices will use HEIC (High Efficiency Image Container) for images, but this format is not supported for uploads. Choosing `large` as the format instead of `original` will use JPG which will work correctly.
## How can I delete the face database and start over?
Frigate does not store anything in its database related to face recognition. You can simply delete all of your faces through the Frigate UI or remove the contents of the `/media/frigate/clips/faces` directory.