Labeling & Feedback

Labels are how operators tell Gaard whether a classification was right. This guide shows how to attach feedback labels from the web app and the API, and how those labels feed back into model quality.

Why feedback matters

Every classification carries a model-derived risk level, but only a human knows the ground truth. A feedback label records that ground truth against a specific result: was this alarm real, or was it a branch moving in the wind?

Labeled results give you three things:

• Measured accuracy. Confirmed true and false positives let you quantify how the model performs on your cameras and scenes, not a generic benchmark.
• Tuning signal. Systematic false positives on a camera are the input to threshold changes and exclusion zones: you cannot fix what you have not measured.
• Training data. Labeled clips are the highest-value examples for improving the model over time. Feedback closes the loop between operators and the classifier.

Labeling is quick and additive: label the results you are confident about, and skip the rest. Even a small, consistent labeling habit sharpens the accuracy picture for your tenant.

Feedback labels

The four standard feedback labels describe the relationship between what Gaard flagged and what actually happened:

Label	Meaning	Use when
TP	True positive	Gaard correctly identified a real threat.
FP	False positive	Gaard flagged the video but there was no real threat.
FN	False negative	Gaard missed a real threat.
TN	True negative	Gaard correctly classified the video as safe.

If you do not need the full confusion-matrix vocabulary, the simpler true and false labels are also accepted.

Alongside feedback labels, Gaard ships a set of descriptive standard labels (categories such as person, vehicle, animal, and plant, plus environmental factors like rain and wind) that let you record what was in the clip rather than only whether the alarm was correct. See the Labels reference for the complete list returned by the API.

Custom labels

Standard labels rarely cover every operational distinction a team cares about: a specific gate, a known vehicle, a recurring nuisance. Custom labels are free-text labels you define for your own tracking.

• Create a custom label inline the first time you need it: type a new value into the label selector and add it.
• Custom labels are scoped to your tenant. Once created, a custom label is remembered and offered as a suggestion the next time you label a result.
• Any string value is accepted and stored. Custom labels also appear in the list returned by the API, so they are available to integrations as well as the web app.

Tip

Agree on a small, shared vocabulary of custom labels before your team starts using them. A handful of consistent labels is far more useful for analysis than dozens of near-duplicates.

Label from the web app

You label results while reviewing them, so feedback is a natural part of the review workflow rather than a separate chore.

1. Open a classification in the review workspace and select the clip you want to label.

2. Open the Score tab. The label selector and comment field sit alongside the per-clip scores, so you can see the model’s confidence while you decide.

3. Select one or more labels. Choose from the standard labels or start typing to create and apply a custom label. Selections save as you make them.

4. Add a comment (optional). Use the comment field to explain the label (for example, “False alarm) wind moving a branch”. Comments are stored with the label and are visible to teammates reviewing the same result.

To browse and manage everything your team has labeled, open the Labels view. It lists labeled results with filtering, and it is also where you export labels as CSV or build a dataset: see Exporting Data.

Label from the API

Integrations can label results programmatically, which is useful when an upstream alarm platform or operator tool already knows the outcome.

Attach one or more labels and an optional comment to an existing classification with its classify_id:

curl

terminal

curl -X POST https://vision.gaard.ai/api/label/66436fc66d24ab9cf81140eb \
  -H "Authorization: Bearer $API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"labels": ["FP"], "comment": "False alarm - wind moving a branch"}'

To discover which labels are available for your tenant (standard and custom) list them with OPTIONS /api/label.

For the full request and response schema, including field types and the recommended label values, see the Labels reference.

Next steps

Reviewing Classifications Work through results in the review workspace before you label them.

Exclusion Zones Turn recurring false positives into masked regions per camera.

Exporting Data Export labels as CSV or build a dataset from labeled clips.

Labels reference The API endpoints and full schema for listing and posting labels.