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name: ops-and-detection description: "Use torchvision.ops for boxes, NMS, ROI pooling/alignment, detection helper concepts, and extension-dependent troubleshooting." disable-model-invocation: true metadata: disco-role: operating license: BSD 3-Clause

TorchVision Ops And Detection

Use this sub-skill when the task needs low-level torchvision.ops APIs, detection box utilities, ROI/NMS behavior, detection postprocessing concepts, or diagnosis of missing custom C++ operators.

Route first

• Use this skill for torchvision.ops.nms, batched_nms, box IoU/conversion/clipping/filtering, masks_to_boxes, roi_align, roi_pool, MultiScaleRoIAlign, FPN helpers, losses, and operator availability errors.
• Use ../models-and-weights/ for choosing or constructing high-level detection models and pretrained weights.
• Use ../training-references/ for official detection training/evaluation command patterns.
• Use ../transforms-and-tv-tensors/ for annotation-aware transform pipelines, TVTensor metadata, and bounding-box transform migration.

Fast workflow

1. Confirm coordinate convention and tensor shapes before calling ops. Most detection model internals use xyxy boxes with shape [N, 4] and coordinates ordered x1 <= x2, y1 <= y2.
2. Normalize formats explicitly with torchvision.ops.box_convert before IoU/NMS; do not mix xyxy, xywh, and center formats in the same tensor.
3. Keep boxes, scores, labels, feature maps, and ROI tensors on compatible devices and dtypes; device mismatches are common when CUDA tensors meet CPU boxes.
4. Treat NMS output order as score-sorted indices, but avoid relying on deterministic tie-breaking when multiple boxes have identical scores and IoUs.
5. For ROI ops, validate feature map rank [N, C, H, W], box format, batch indices, output_size, spatial_scale, and featmap_names before debugging model heads.
6. If an op raises operator torchvision::nms does not exist or a custom-ops loading error, diagnose install/build compatibility before rewriting model code.

References

• references/box-and-roi-ops.md: box formats, IoU variants, NMS, masks, ROI Align/Pool, FPN, and layers/losses.
• references/detection-helpers.md: detection model helper concepts, postprocessing knobs, output structures, and boundaries.
• references/troubleshooting.md: extension, version, device, coordinate, nondeterminism, and ROI-shape failures.

Bundled check

Run the tiny CPU smoke script to verify that import, boxes, IoU, NMS, and ROI Align work without downloads or large tensors:

python sub-skills/ops-and-detection/scripts/smoke_ops.py

If the script reports missing compiled ops, follow references/troubleshooting.md rather than treating it as a model-definition issue.