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name: inference-visualization description: "Run Detectron2 inference, inspect model outputs, and visualize predictions safely without depending on the source demo script." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Detectron2 Inference & Visualization

Use this sub-skill when a task asks to run Detectron2 inference, call DefaultPredictor, call a built model directly, inspect Instances/Boxes/masks, visualize predictions, plan demo-style image/video/webcam commands, or validate COCO-style prediction JSON before drawing it.

Fast Routing

• For simple single-image inference, read references/inference-workflows.md and use DefaultPredictor(cfg) only when weights are local or downloads are intentional.
• For batch/direct model calls, read references/inference-workflows.md and use build_model(cfg), DetectionCheckpointer(model).load(...), model.eval(), and model([input_dict])[0].
• For output parsing, field checks, JSON conversion, and visualization requirements, read references/structures-and-outputs.md.
• For broken demo imports, OpenCV issues, CPU/CUDA mismatches, empty predictions, metadata labels, or output side effects, read references/troubleshooting.md.
• Build a safe demo-style command without executing it through scripts/demo_command_builder.py.
• Validate COCO-style prediction JSON without drawing or importing Detectron2 through scripts/visualize_json_schema_check.py.

Safe Defaults

• Set inference device before model or predictor construction: Yacs configs use cfg.MODEL.DEVICE = "cpu" or the CLI override MODEL.DEVICE cpu.
• Do not instantiate DefaultPredictor during no-download validation if cfg.MODEL.WEIGHTS points to a URL or detectron2:// URI; it loads weights during construction.
• Treat OpenCV images as BGR, but pass RGB images to Visualizer; convert with image_rgb = image_bgr[:, :, ::-1].
• Move predictions to CPU before visualization: instances = outputs["instances"].to("cpu").
• Check instances.has("pred_boxes"), instances.has("scores"), and metadata class names before assuming labels or boxes exist.

Boundaries

This sub-skill owns inference calls, model input/output formats, Instances, Boxes, BoxMode, BitMasks, PolygonMasks, ImageList, Visualizer, ColorMode, demo-style flags, confidence thresholds, CPU overrides, and prediction JSON sanity checks.

Route model-zoo config/checkpoint selection to ../configuration-model-zoo/. Route dataset registration, catalog metadata, and dataset dict validation to ../data-datasets/. Route training, evaluation loops, evaluators, and metrics to ../training-evaluation/. Route TorchScript, tracing, Caffe2, and deployment packaging to ../deployment-export/.