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name: extension-projects description: "Extend Detectron2 with registries, configurable model components, custom trainers, and optional project packages without over-installing optional stacks." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Detectron2 Extension Projects

Use this sub-skill when a task asks you to add or debug a Detectron2 extension: custom registries, custom backbones, ROI heads, meta-architectures, configurable components, trainer subclasses, or the bundled research-project packages under detectron2.projects.

Read First

• For registry and config-extension patterns, read references/extension-patterns.md.
• For custom model component interfaces, read references/model-components.md.
• For bundled and optional research projects, read references/projects-overview.md.
• For extension failures and diagnosis, read references/troubleshooting.md.
• To smoke-test registry mechanics without building or training a model, run python scripts/registry_smoke_check.py from this sub-skill directory or copy the script into a scratch workspace.

Choose This Sub-Skill For

• Registering or diagnosing BACKBONE_REGISTRY, ROI_HEADS_REGISTRY, META_ARCH_REGISTRY, SEM_SEG_HEADS_REGISTRY, PROPOSAL_GENERATOR_REGISTRY, or related model component registries.
• Wiring custom classes into Yacs configs through names such as MODEL.BACKBONE.NAME, MODEL.ROI_HEADS.NAME, or MODEL.META_ARCHITECTURE.
• Deciding whether to register a component, construct it with explicit arguments, or use @configurable plus from_config.
• Subclassing DefaultTrainer to customize build_model, build_optimizer, build_train_loader, build_evaluator, hooks, or test-time behavior.
• Loading or diagnosing installed project packages such as detectron2.projects.point_rend, detectron2.projects.deeplab, and detectron2.projects.panoptic_deeplab.

Boundaries

• For dataset registration, mappers, metadata, and dataloaders, use the data/config owning sub-skill.
• For standard training, launch, checkpoints, evaluation, and inference execution, use the training/inference owning sub-skills.
• For export/deployment details, use the deployment owning sub-skill.
• Treat DensePose, TensorMask, TridentNet, ViTDet, MViTv2, PointSup, and other research projects as optional examples unless the environment explicitly includes their dependencies and the user asks for that stack.

Working Checklist

1. Identify the extension seam: model component registry, full meta-architecture, explicit constructor override, trainer hook/classmethod, or project config adder.
2. Import the module that performs registration before asking a builder or registry to resolve a string name.
3. Add project config keys before merging a project config or setting project-specific options.
4. Match the builder's expected signature, input feature names, and ShapeSpec contracts.
5. Keep optional project dependencies opt-in; do not install broad extras unless the requested project requires them and the user approves.
6. Use the bundled smoke script to validate registry behavior before debugging heavier model/data/training code.