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Code Issues Actions 3 Packages Projects Releases Wiki Activity

Classification Model UI Refactor (#20602)

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* Add cutoff for object classification

* Add selector for classifiction model type

* Improve model selection view

* Clean up design of classification card

* Tweaks

* Adjust button colors

* Improvements to gradients and making face library consistent

* Add basic classification model wizard

* Use relative coordinates

* Properly get resolution

* Clean up exports

* Cleanup

* Cleanup

* Update to use pre-defined component for image shadow

* Refactor image grouping

* Clean up mobile

* Clean up decision logic

* Remove max check on classification objects

* Increase default number of faces shown

* Cleanup

* Improve mobile layout

* Clenaup

* Update vocabulary

* Fix layout

* Fix page

* Cleanup

* Choose last item for unknown objects

* Move explore button

* Cleanup grid

* Cleanup classification

* Cleanup grid

* Cleanup

* Set transparency

* Set unknown

* Don't filter all configs

* Check length
This commit is contained in:
Nicolas Mowen
2025-10-22 07:36:09 -06:00
committed by GitHub
parent 9638e85a1f
commit d6f5d2b0fa
23 changed files with 666 additions and 434 deletions
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docs/docs/configuration/face_recognition.md
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@@ -70,7 +70,7 @@ Fine-tune face recognition with these optional parameters at the global level of
- `min_faces`: Min face recognitions for the sub label to be applied to the person object.
- Default: `1`
- `save_attempts`: Number of images of recognized faces to save for training.
- Default: `100`.
- Default: `200`.
- `blur_confidence_filter`: Enables a filter that calculates how blurry the face is and adjusts the confidence based on this.
- Default: `True`.
- `device`: Target a specific device to run the face recognition model on (multi-GPU installation).
@@ -114,9 +114,9 @@ When choosing images to include in the face training set it is recommended to al
:::
### Understanding the Train Tab
### Understanding the Recent Recognitions Tab
The Train tab in the face library displays recent face recognition attempts. Detected face images are grouped according to the person they were identified as potentially matching.
The Recent Recognitions tab in the face library displays recent face recognition attempts. Detected face images are grouped according to the person they were identified as potentially matching.
Each face image is labeled with a name (or `Unknown`) along with the confidence score of the recognition attempt. While each image can be used to train the system for a specific person, not all images are suitable for training.
@@ -140,7 +140,7 @@ Once front-facing images are performing well, start choosing slightly off-angle
Start with the [Usage](#usage) section and re-read the [Model Requirements](#model-requirements) above.
1. Ensure `person` is being _detected_. A `person` will automatically be scanned by Frigate for a face. Any detected faces will appear in the Train tab in the Frigate UI's Face Library.
1. Ensure `person` is being _detected_. A `person` will automatically be scanned by Frigate for a face. Any detected faces will appear in the Recent Recognitions tab in the Frigate UI's Face Library.
If you are using a Frigate+ or `face` detecting model:
@@ -186,7 +186,7 @@ Avoid training on images that already score highly, as this can lead to over-fit
No, face recognition does not support negative training (i.e., explicitly telling it who someone is _not_). Instead, the best approach is to improve the training data by using a more diverse and representative set of images for each person.
For more guidance, refer to the section above on improving recognition accuracy.
### I see scores above the threshold in the train tab, but a sub label wasn't assigned?
### I see scores above the threshold in the Recent Recognitions tab, but a sub label wasn't assigned?
The Frigate considers the recognition scores across all recognition attempts for each person object. The scores are continually weighted based on the area of the face, and a sub label will only be assigned to person if a person is confidently recognized consistently. This avoids cases where a single high confidence recognition would throw off the results.