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name: box-ensembling description: "Route 2D normalized box ensembling tasks with weighted boxes fusion, NMS, Soft-NMS, non-maximum weighted suppression, and the experimental vectorized WBF variant." disable-model-invocation: true metadata: disco-role: operating license: MIT

Box ensembling

Use this sub-skill for 2D object-detection prediction ensembling only. It works with normalized [x1, y1, x2, y2] boxes, per-model scores and labels, model weights, IoU thresholds, confidence modes, and sorted output arrays.

Use it when you need to:

• merge YOLO, Faster R-CNN, DetectoRS, or similar detector outputs from multiple models;
• choose between WBF, NMW, NMS, or Soft-NMS for a single 2D image batch;
• validate and normalize box arrays before fusion;
• explain repeated-box score inflation or confidence-mode selection.

Do not use this sub-skill for 1D spans or 3D cuboids.

Start here

• API reference
• Workflow recipes
• Troubleshooting
• Smoke script

Routing rules

• Prefer weighted_boxes_fusion for multi-model 2D fusion when you want fused coordinates.
• Use weighted_boxes_fusion_experimental only for trusted, already-sanitized inputs and speed-oriented experiments.
• Use non_maximum_weighted when you want a weighted representative box but not full WBF confidence behavior.
• Use nms, soft_nms, or nms_method when you want hard or soft suppression instead of coordinate averaging.
• Normalize raw pixel boxes before calling the library. The package can clip and swap corners, but it does not know image size.
• If repeated boxes from the same model make scores too high, prefer box_and_model_avg or absent_model_aware_avg and keep allows_overflow=False.

Expected outputs

All supported functions return numpy arrays sorted by descending confidence:

• boxes: (N, 4) float array
• scores: (N,) float array
• labels: (N,) numeric labels; cast to integer if needed

Keep empty predictions as empty per-model lists so the outer model count stays aligned.