La interfaz está localizada; las descripciones y documentos de las Skills se traducen automáticamente con IA a su idioma. El código, los comandos, rutas y términos técnicos se mantienen en el original, y prevalece el documento del autor.
Explorar Skills
Productividad · SKILL.md

model-zoo-and-configuration

model-zoo-and-configuration

Handles PaddleDetection model-family selection, YAML inheritance and overrides, model-zoo lookup, registry APIs, and configuration preflight.

Independent GitHub snapshot · Automated classification · Original documentation governs

Documentación de la Skill

GitHub

name: model-zoo-and-configuration description: "Handles PaddleDetection model-family selection, YAML inheritance and overrides, model-zoo lookup, registry APIs, and configuration preflight." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Model Zoo and Configuration

Use this route when the task is to choose a PaddleDetection model/config, inspect _BASE_ inheritance, override a YAML value, list model families, load a config, or add a registry-backed component.

Workflow

  1. Identify the task family: detection, instance segmentation, keypoint, MOT, rotated/small-object detection, semi-supervised detection, slimming, or PP-Human/PP-Vehicle.
  2. Select a representative config from the target checkout's configs/ tree. Prefer a documented model family and verify the dataset block, metric, input shape, num_classes, and save_dir.
  3. Load the YAML with ppdet.core.workspace.load_config. It recursively resolves _BASE_ files and updates the global registry/config state.
  4. Apply only deliberate overrides using -o key=value or merge_config; use dotted keys for nested values and validate the resulting object.
  5. For model-zoo lookup, use ppdet.model_zoo.list_model(filters) and get_weights_url(name). Treat get_config_file(name) and get_model(name) as network/cache operations; a source build reporting version 0.0.0 may request a nonexistent configs/0.0.0/configs.tar.
  6. Run scripts/summarize_config.py before training or export. Read references/troubleshooting.md for stale package/version and config errors.

Registry customization

ppdet.core.workspace.register adds a class to the registry and create(name, **kwargs) constructs registered modules. Keep custom modules in the target project/package, register them before loading a config that names them, and test with Trainer(cfg, mode="test") before starting a training loop. Use the API details in references/api-reference.md; do not edit generated model-zoo metadata by hand.

References and helper

Skills guardadas