Miscellaneous fixes (#23651)

This commit is contained in:
Josh Hawkins
2026-07-08 08:27:38 -05:00
committed by GitHub
parent c99d6b0dcf
commit e6cac50250
43 changed files with 448 additions and 318 deletions
@@ -103,7 +103,7 @@ Once some images are assigned, training will begin automatically.
:::tip Diversity matters far more than volume
Selecting dozens of nearly identical images is one of the fastest ways to degrade model performance. MobileNetV2 can overfit quickly when trained on homogeneous data — the model learns what _that exact moment_ looked like rather than what actually defines the state. This often leads to models that work perfectly under the original conditions but become unstable when day turns to night, weather changes, or seasonal lighting shifts. **This is why Frigate does not implement bulk training in the UI.**
Selecting dozens of nearly identical images is one of the fastest ways to degrade model performance. MobileNetV2 can overfit quickly when trained on homogeneous data. The model learns what _that exact moment_ looked like rather than what actually defines the state. This often leads to models that work perfectly under the original conditions but become unstable when day turns to night, weather changes, or seasonal lighting shifts. **This is why Frigate does not implement bulk training in the UI.**
For more detail, see [Frigate Tip: Best Practices for Training Face and Custom Classification Models](https://github.com/blakeblackshear/frigate/discussions/21374).