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Frigate lets you train lightweight MobileNetV2 models to classify tracked objects into finer categories (like identifying specific pets or detecting safety gear). The models require consistent predictions across multiple frames before assigning labels, which prevents false positives but means you need to carefully curate training data to avoid overfitting.
- Classification requires a two-step verification: confidence score above threshold (default 0.8) plus 60% consensus across at least 3 attempts, preventing single-frame misclassifications from triggering assignments.
- Training data quality matters far more than quantity—using dozens of nearly identical images actually degrades performance through overfitting, so you should start small with visually distinct examples and add hard cases (low confidence predictions, unusual angles/lighting) over time.
- The `none` class needs the opposite training approach from named classes: it should include visual diversity (small crops, partial views, false positives) so the model can recognize when something doesn't match your known categories, otherwise borderline detections get forced into named classes by default.