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name: detection description: "Use this YOLOv5 sub-skill for object-detection training, validation, inference, PyTorch Hub loading, detection datasets, and checkpoint troubleshooting." disable-model-invocation: true metadata: disco-role: operating license: AGPL 3.0

YOLOv5 Detection

Use this route for bounding-box detection requests involving the YOLOv5 clone-run entrypoints, PyTorch Hub model loading, detection YAMLs, pretrained detection checkpoints, or detection-specific errors.

Choose the workflow

• Predict/infer: read references/workflows.md for image, directory, video, glob, webcam, screenshot, URL, and stream source planning. Use scripts/plan_detection_command.py before running a command with downloads, media, or output writes.
• Train/fine-tune: use the training section for train.py, custom data YAMLs, pretrained vs scratch training, image/batch/device choices, resume, DDP, and output directories.
• Validate/evaluate: use the validation section for val.py, data/weights pairing, confidence/IoU, task modes, JSON/plot output, and exported-format validation handoff.
• PyTorch Hub/local API: read references/api-reference.md for hubconf.custom, standard yolov5n/s/m/l/x functions, autoshape, device selection, and cache behavior.

Common decisions

• Start with yolov5n or yolov5s and a tiny local fixture when the goal is a smoke check.
• Use explicit local checkpoint and data paths for offline or reproducible work; names such as yolov5s.pt may download.
• Match the detection checkpoint and detection YAML to the class count. Do not use *-seg.pt or *-cls.pt here.
• Set an explicit --project and --name when output placement matters. --exist-ok is an overwrite policy, not a harmless default.
• Use --device cpu for deterministic parser/import checks. Use CUDA for realistic training or half precision.
• Treat webcam, RTSP/HTTP, YouTube, screen capture, and remote URLs as network/device workflows, not safe defaults.

Handoffs

• Read root references/datasets-and-weights.md before downloads or custom data.
• Route segmentation to ../segmentation/SKILL.md and classification to ../classification/SKILL.md.
• Route model conversion or backend runtime selection to ../export/SKILL.md after a checkpoint is selected.
• Route HTTP serving to ../serving/SKILL.md; this route owns model behavior, not the Flask service contract.