> For the complete documentation index, see [llms.txt](https://yall.yassrobotics.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://yall.yassrobotics.com/documentation/tuning/neural-network-pipeline-tuning.md). # Tuning a Neural Network Pipeline Neural classifier and detector pipelines expose far fewer tuning knobs than retroreflective or AprilTag pipelines — most of the "tuning" work is in the model itself (see Limelight's training documentation for classifiers/detectors), not the pipeline settings. In the web UI, there are two levers worth adjusting: ## Confidence threshold Both pipeline types expose a **confidence threshold** slider. Raising it rejects lower-confidence detections/classifications outright — fewer false positives, but you'll also start missing genuine borderline detections. There's no universal value; raise it until false positives on your specific game pieces/field elements stop, then back off slightly to keep recall reasonable. `NeuralDetector.confidence` / `NeuralClassifier.confidence` (read via `LimelightResults.targets_Detector` / `targets_Classifier`) reflect whatever passed this threshold — you can still apply an additional, stricter threshold in robot code if you want different behavior per-mode (e.g. stricter during autonomous) without touching the pipeline itself. ## Crop window Both pipeline types support a **crop window** — restricting inference to a sub-region of the frame: * For a **detector**, this lets you ignore objects outside a zone you care about (e.g. only the field in front of an intake). * For a **classifier**, this reduces how much irrelevant background the model has to reason about, which usually improves classification accuracy directly. `LimelightSettings.withCropWindow(minX, maxX, minY, maxY)` sets the same crop from robot code if you need it to change dynamically (e.g. widening the zone once a game piece is expected to be closer) rather than fixed per-pipeline in the web UI. ## Hardware prerequisite Neural pipelines require a coprocessor (e.g. Google Coral) connected via the Limelight's USB-A port — if detections/classifications never populate `LimelightResults` regardless of threshold, confirm the accelerator is connected before assuming it's a tuning problem. ## See Also * [Tuning Workflow Overview](/documentation/tuning/tuning-workflow-overview.md) * [Target & Pipeline Types](/documentation/understanding/target-and-pipeline-types.md) — the `NeuralClassifier` vs. `NeuralDetector` distinction. * [Object Detection tutorial](/documentation/tutorials/object-detection.md) --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter: ``` GET https://yall.yassrobotics.com/documentation/tuning/neural-network-pipeline-tuning.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.