Negative Frames

Case: Your model predicts animals on frames that are actually empty, and you want to teach it that some frames contain no animals.

A negative frame (also called a background frame) is a video frame that you explicitly mark as containing no animals. When negative frames are included in training, the model learns to predict nothing on empty frames, which reduces false positives — spurious detections on background.

This is different from a frame that is simply empty (a labeled frame whose instances were all deleted). An empty frame carries no information and is discarded during training. A negative frame is a deliberate assertion that the frame is background, and it is used as a training example.

Why a deliberate action

Marking a frame negative is intentional by design. An accidental negative frame introduces a false negative into your training data, so SLEAP asks you to confirm before removing any existing instances, and gives you several ways to review which frames are marked (see Reviewing negative frames).

Marking a frame as negative

Navigate to a frame that contains no animals, then mark it using any of:

• Menu: Labels → Mark Frame as Negative
• Keyboard shortcut: N
• Right-click in the video → Mark Frame as Negative

The menu item and context-menu entry are checkable — the checkmark shows whether the current frame is currently marked as negative.

When the current frame already has instances on it, SLEAP shows a confirmation dialog reporting how many instances will be removed. Marking the frame negative removes those instances, because a frame with a labeled animal is not a background frame. Adding a new instance to a negative frame automatically clears the negative flag for the same reason.

To unmark a frame, use the same action again. If the frame is empty when you unmark it, it is removed from the project entirely (an empty, non-negative frame would otherwise be silently dropped during training).

Reviewing negative frames

Negative frames are surfaced in several places so accidental ones can be caught:

• On-canvas overlay: while you are viewing a negative frame, a thick blue border is drawn around the periphery of the frame with a "Negative Frame" caption in the top-left corner.
• Seekbar marker: every negative frame gets a blue tick on the seekbar. Hover over it to confirm ("negative (background) frame").
• Status bar: when the current frame is negative, the status bar shows a [NEGATIVE FRAME] tag.
• Label Quality Control: the Label Quality Control checks flag any negative frame that still has instances — an inconsistency that usually means a mislabel.

Training with negative frames

Negative frames are stored in your .slp project but are only used for training when you opt in. In the Training Pipeline dialog, on the Data tab of a model:

• Use Negative Frames — when enabled, every frame you marked as negative is added to the training set as a background example.
• Negative Loss Weight — the relative weight of the loss on negative frames. 1.0 weights them the same as labeled frames. Increase it above 1.0 if false positives persist, or decrease it to reduce their influence. It must be greater than 0, and it is only used when Use Negative Frames is enabled.

Supported model types

Negative frames are used by single-instance, centroid, bottom-up, and multi-class bottom-up models. They are ignored by the centered-instance and multi-class top-down heads, so the two options are hidden on those tabs. For a top-down pipeline, negative frames still help the centroid stage — which is the stage that produces the false-positive detections — so the options remain available there.

If you enable Use Negative Frames but have not marked any frames as negative, SLEAP warns you in the training dialog, since the option would otherwise have no effect.