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configuration-model-zoo

configuration-model-zoo

Choose, load, inspect, modify, and validate Detectron2 Yacs, LazyConfig, and model-zoo configurations without relying on the source checkout.

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Documentación de la Skill

GitHub

name: configuration-model-zoo description: "Choose, load, inspect, modify, and validate Detectron2 Yacs, LazyConfig, and model-zoo configurations without relying on the source checkout." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Configuration and Model Zoo

Use this sub-skill when a request mentions Detectron2 configs, _BASE_, Yacs/YAML, CfgNode, get_cfg, LazyConfig Python files, LazyCall, instantiate, model-zoo config names, checkpoint URLs, MODEL.WEIGHTS, train.init_checkpoint, or CPU/GPU config overrides.

Fast Routing

  • For Yacs/YAML workflows, read references/configuration.md and use get_cfg(), merge_from_file(), and merge_from_list().
  • For LazyConfig workflows, read references/configuration.md and use LazyConfig.load(), LazyConfig.apply_overrides(), LazyConfig.to_py(), LazyCall, and instantiate().
  • For official model-zoo paths, checkpoint URLs, and no-download inspection, read references/model-zoo.md.
  • For symptoms such as wrong paths, mixed override syntax, unresolved relative imports, serialization failures, or CPU/GPU confusion, read references/troubleshooting.md.
  • To safely inspect a config without building a model or downloading weights, run scripts/inspect_config.py with --help first.

Boundary Notes

This sub-skill owns configuration loading, config mutation, static inspection, and official model-zoo path/checkpoint selection. Route dataset registration, dataset dictionaries, COCO JSON schemas, and mappers to ../data-datasets/. Route training launch flags, evaluation commands, and distributed execution to ../training-evaluation/. Route custom component registries and project extensions to ../extension-projects/. Route TorchScript, tracing, Caffe2, and deployment format questions to ../deployment-export/.

Safe Defaults

  • Prefer model_zoo.get_config() or model_zoo.get_config_file() for config inspection; reserve model_zoo.get() for cases that intentionally build a model and may load checkpoint files.
  • Set device explicitly for CPU-only work: Yacs uses MODEL.DEVICE cpu; LazyConfig model-zoo configs typically require editing cfg.model.device = "cpu" or passing device="cpu" to model_zoo.get() when building.
  • Avoid trained=True and model_zoo.get() when validating config syntax only; use get_checkpoint_url() to print a URL without downloading.
  • Keep Yacs CLI overrides as alternating KEY VALUE tokens, and LazyConfig overrides as key=value strings.
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