---
id: state_classification
title: 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.

## Classes

Classes are the different states an area on your camera can be in. Each class represents a distinct visual state that the model will learn to recognize.

For state classification:

- Define classes that represent mutually exclusive states
- Examples: `open` and `closed` for a garage door, `on` and `off` for lights
- Use at least 2 classes (typically binary states work best)
- Keep class names clear and descriptive

## 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`.

```yaml
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. The process consists of three steps:

### Step 1: Name and Define

Enter a name for your model and define at least 2 classes (states) that represent mutually exclusive states. For example, `open` and `closed` for a door, or `on` and `off` for lights.

### Step 2: Select the Crop Area

Choose one or more cameras and draw a rectangle over the area of interest for each camera. The crop should be tight around the region you want to classify to avoid extra signals unrelated to what is being classified. You can drag and resize the rectangle to adjust the crop area.

### Step 3: Assign Training Examples

The system will automatically generate example images from your camera feeds. You'll be guided through each class one at a time to select which images represent that state.

**Important**: All images must be assigned to a state before training can begin. This includes images that may not be optimal, such as when people temporarily block the view, sun glare is present, or other distractions occur. Assign these images to the state that is actually present (based on what you know the state to be), not based on the distraction. This training helps the model correctly identify the state even when such conditions occur during inference.

Once all images are assigned, training will begin automatically.

### 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 Recent Classifications tab to gather balanced examples across times of day and weather.
- **When to train**: Focus on cases where the model is entirely incorrect or flips between states when it should not. There's no need to train additional images when the model is already working consistently.
- **Selecting training images**: Images scoring below 100% due to new conditions (e.g., first snow of the year, seasonal changes) or variations (e.g., objects temporarily in view, insects at night) are good candidates for training, as they represent scenarios different from the default state. Training these lower-scoring images that differ from existing training data helps prevent overfitting. Avoid training large quantities of images that look very similar, especially if they already score 100% as this can lead to overfitting.
