From 4b94463be7fbc7b397a639241ce684c4484d4b55 Mon Sep 17 00:00:00 2001 From: Josh Hawkins <32435876+hawkeye217@users.noreply.github.com> Date: Sat, 20 Dec 2025 09:23:53 -0600 Subject: [PATCH] docs --- .../custom_classification/object_classification.md | 8 ++++++-- .../custom_classification/state_classification.md | 2 ++ 2 files changed, 8 insertions(+), 2 deletions(-) diff --git a/docs/docs/configuration/custom_classification/object_classification.md b/docs/docs/configuration/custom_classification/object_classification.md index 70fd1fbbd..da8c4d887 100644 --- a/docs/docs/configuration/custom_classification/object_classification.md +++ b/docs/docs/configuration/custom_classification/object_classification.md @@ -33,9 +33,9 @@ For object classification: - Example: `cat` → `Leo`, `Charlie`, `None`. - **Attribute**: - - Added as metadata to the object (visible in /events): `: `. + - Added as metadata to the object, visible in the Tracked Object Details pane in Explore, `frigate/events` MQTT messages, and the HTTP API response as `: `. - Ideal when multiple attributes can coexist independently. - - Example: Detecting if a `person` in a construction yard is wearing a helmet or not. + - Example: Detecting if a `person` in a construction yard is wearing a helmet or not, and if they are wearing a yellow vest or not. :::note @@ -81,6 +81,8 @@ classification: classification_type: sub_label # or: attribute ``` +An optional config, `save_attempts`, can be set as a key under the model name. This defines the number of classification attempts to save in the Recent Classifications tab. For object classification models, the default is 200. + ## Training the model Creating and training the model is done within the Frigate UI using the `Classification` page. The process consists of two steps: @@ -89,6 +91,8 @@ Creating and training the model is done within the Frigate UI using the `Classif Enter a name for your model, select the object label to classify (e.g., `person`, `dog`, `car`), choose the classification type (sub label or attribute), and define your classes. Include a `none` class for objects that don't fit any specific category. +For example: To classify your two cats, create a model named "Our Cats" and create two classes, "Charlie" and "Leo". Create a third class, "none", for other neighborhood cats that are not your own. + ### Step 2: Assign Training Examples The system will automatically generate example images from detected objects matching your selected label. You'll be guided through each class one at a time to select which images represent that class. Any images not assigned to a specific class will automatically be assigned to `none` when you complete the last class. Once all images are processed, training will begin automatically. diff --git a/docs/docs/configuration/custom_classification/state_classification.md b/docs/docs/configuration/custom_classification/state_classification.md index 196ec78de..1ffdf9011 100644 --- a/docs/docs/configuration/custom_classification/state_classification.md +++ b/docs/docs/configuration/custom_classification/state_classification.md @@ -48,6 +48,8 @@ classification: crop: [0, 180, 220, 400] ``` +An optional config, `save_attempts`, can be set as a key under the model name. This defines the number of classification attempts to save in the Recent Classifications tab. For state classification models, the default is 100. + ## Training the model Creating and training the model is done within the Frigate UI using the `Classification` page. The process consists of three steps: