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name: inference description: "Run YOLOv3 inference through detect.py, PyTorch Hub, and DetectMultiBackend with correct sources, weights, labels, and outputs." disable-model-invocation: true metadata: disco-role: operating license: AGPL 3.0

Inference Sub-skill

Read this when the task involves detect.py, PyTorch Hub, hubconf.py, DetectMultiBackend, image/video/stream sources, custom weights, saved labels, crops, class filtering, confidence thresholds, NMS, or inference troubleshooting.

Use

• For CLI detection workflows, read references/workflows.md and use scripts/yolov3_inference_smoke.py for a deterministic local model probe before weight downloads.
• For Python/PyTorch Hub loading, read references/api-reference.md and use scripts/yolov3_hub_smoke.py with --no-pretrained for offline checks.
• For inference failures, read references/troubleshooting.md.

Important facts

• Official model names are yolov3, yolov3-spp, and yolov3-tiny; PyTorch Hub functions are yolov3, yolov3_spp, yolov3_tiny, and custom.
• detect.py --weights yolov3-tiny.pt may download release weights when absent.
• --save-txt writes normalized YOLO label text; add --save-conf for confidences and --save-crop for crops.
• Use --device cpu for portable correctness smokes; use CUDA only after verifying the target environment.