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name: model-and-config description: "Inspect and adapt pytorch-yolo-v3 Darknet model configuration, class names, and binary weight handling." disable-model-invocation: true metadata: disco-role: operating license: NO_LICENSE

model-and-config

Use this sub-skill when a user needs to understand or safely inspect this repo's Darknet/YOLO model configuration surface: cfg parsing, model construction, supported cfg files, class-name alignment, Darknet binary weight loading/saving, or lightweight API inspection.

Route by task

• For parse_cfg, create_modules, Darknet, Darknet.forward, load_weights, or save_weights behavior, use references/api-reference.md.
• For cfg/yolov3.cfg, class-count edits, names-file expectations, resolution constraints, and unsupported cfg variants, use references/model-configs.md.
• For failures such as Something I dunno, AssertionError, class/filter mismatch, invalid input size, missing weights, or legacy PyTorch behavior, use references/troubleshooting.md.
• To inspect a user's checkout without downloading weights or running inference, run the bundled script:

python scripts/inspect_darknet_config.py --repo-root <repo-root> --cfg cfg/yolov3.cfg --names data/coco.names

Add --build-model only when the user explicitly wants to prove that Darknet(cfgfile) instantiates in the current Python environment.

Out-of-scope routing

• Image batch CLI usage, drawing, image preprocessing, and detection postprocessing belong in ../image-detection/SKILL.md.
• Video files, webcam demos, half-precision display loops, GUI/display behavior, and camera handling belong in ../video-camera-demos/SKILL.md.
• Training is out of scope for this repo skill; this repo's documented surface is detection-only.

Operating guardrails

• Do not download weights, datasets, or class files from this sub-skill. Require the user to provide local files.
• Do not run image/video inference or GUI/camera flows from this sub-skill.
• Treat cfg files with region or reorg blocks as parseable but not constructible by this repo's create_modules implementation.
• Before loading weights after class-count edits, inspect the cfg/names/filter relationship and warn that pretrained Darknet weights are tied to the original detection-head shapes.