Skip to main content

State Classification

State classification allows you to train a custom MobileNetV2 classification model on a fixed region of your camera frame(s) to determine a current state. The model can be configured to run on a schedule and/or when motion is detected in that region.

Minimum System Requirements​

State classification models are lightweight and run very fast on CPU. Inference should be usable on virtually any machine that can run Frigate.

Training the model does briefly use a high amount of system resources for about 1–3 minutes per training run. On lower-power devices, training may take longer. When running the -tensorrt image, Nvidia GPUs will automatically be used to accelerate training.

Example use cases​

  • Door state: Detect if a garage or front door is open vs closed.
  • Gate state: Track if a driveway gate is open or closed.
  • Trash day: Bins at curb vs no bins present.
  • Pool cover: Cover on vs off.

Configuration​

State classification is configured as a custom classification model. Each model has its own name and settings. You must provide at least one camera crop under state_config.cameras.

classification:
  custom:
    front_door:
      threshold: 0.8
      state_config:
        motion: true # run when motion overlaps the crop
        interval: 10 # also run every N seconds (optional)
        cameras:
          front:
            crop: [0, 180, 220, 400]

Training the model​

Creating and training the model is done within the Frigate UI using the Classification page.

Getting Started​

When choosing a portion of the camera frame for state classification, it is important to make the crop tight around the area of interest to avoid extra signals unrelated to what is being classified.

// TODO add this section once UI is implemented. Explain process of selecting a crop.

Improving the Model​

  • Problem framing: Keep classes visually distinct and state-focused (e.g., open, closed, unknown). Avoid combining object identity with state in a single model unless necessary.
  • Data collection: Use the model’s Train tab to gather balanced examples across times of day and weather.