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name: detectron2 description: "Use Detectron2 for object detection, segmentation, configuration, datasets, training, inference, export, and extension workflows." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Detectron2 Repo Skill

Use this repo skill when a task mentions Detectron2, Detectron, object detection, instance/keypoint/panoptic/semantic segmentation, Detectron2 model zoo configs, DefaultPredictor, DefaultTrainer, DatasetCatalog, Detectron2 export, or custom Detectron2 model components.

Start Here

• Verify the package and optional runtime pieces with scripts/check_detectron2_env.py before debugging imports, CUDA, OpenCV, Caffe2, or ONNX.
• Read references/troubleshooting.md for install/build/import, optional dependency, CUDA/backend, model-zoo download, and source-demo caveats.
• Read references/repo-provenance.md before deciding whether this generated skill is current for a local checkout.
• Router metadata for managed import lives in references/repo-routing-metadata.json.

Installation Baseline

Detectron2 is a PyTorch-based library with native C++/CUDA extensions. Public installs should start from a compatible PyTorch and torchvision pair, then install Detectron2 from a release wheel or source build appropriate for the platform.

python -m pip install torch torchvision
python -m pip install 'git+https://github.com/facebookresearch/detectron2.git'
python - <<'PY'
import detectron2
print(detectron2.__version__)
PY

For source builds, ensure torch is importable before running Detectron2 setup. If editable source installation fails because build isolation cannot import torch, retry with a package/build process that disables build isolation after installing torch. OpenCV is optional for core APIs but needed for demo-style image/video visualization workflows.

Route By Task

• Configs and model zoo: Use sub-skills/configuration-model-zoo/SKILL.md for Yacs YAML configs, LazyConfig Python configs, _BASE_, overrides, LazyCall, instantiate, model-zoo config paths, and checkpoint URLs.
• Data and datasets: Use sub-skills/data-datasets/SKILL.md for DatasetCatalog, MetadataCatalog, COCO helpers, standard dataset dicts, metadata keys, mappers, augmentations, and train/test data loaders.
• Training and evaluation: Use sub-skills/training-evaluation/SKILL.md for project-local train/eval driver commands, DefaultTrainer, hooks, launch, checkpointing, evaluators, inference_on_dataset, and solver/LR/batch-size adjustments.
• Inference and visualization: Use sub-skills/inference-visualization/SKILL.md for DefaultPredictor, direct model inference, Instances, Boxes, masks, model input/output formats, Visualizer, confidence thresholds, CPU overrides, and prediction JSON sanity checks.
• Deployment and export: Use sub-skills/deployment-export/SKILL.md for TorchScript tracing/scripting, optional Caffe2/ONNX planning, TracingAdapter, scripting_with_instances, model analysis, benchmarking, and safe export command construction.
• Extensions and projects: Use sub-skills/extension-projects/SKILL.md for registries, custom backbones/ROI heads/meta-architectures, @configurable, custom trainers, and bundled or optional research projects such as PointRend, DeepLab, Panoptic-DeepLab, DensePose, TensorMask, ViTDet, and MViTv2.

Common Decisions

• Yacs vs LazyConfig: YAML configs use get_cfg(), merge_from_file(), and alternating KEY VALUE overrides. Python configs use LazyConfig.load() and key=value overrides.
• Weights vs config: Config inspection should not build a model or download weights. Use model-zoo URL helpers for checkpoint URLs, and load weights only when inference/evaluation/export is intentional.
• Dataset first: Custom training/evaluation needs dataset registration and metadata before cfg.DATASETS.TRAIN/TEST or evaluator logic can work.
• CPU-only work: Set MODEL.DEVICE cpu for Yacs configs or the matching LazyConfig model device field before constructing predictors/models.
• Long-running work: Ask before launching training, full evaluation, benchmarks, export runs that load large weights, multi-GPU/multi-machine jobs, or commands that download model/data artifacts.

Bundled Helpers

• Root diagnostic: scripts/check_detectron2_env.py checks imports, package version, torch/CUDA, OpenCV, Caffe2/ONNX availability, and key API signatures.
• Config inspector: sub-skills/configuration-model-zoo/scripts/inspect_config.py safely inspects Yacs, LazyConfig, or model-zoo configs without building models.
• Dataset validators: sub-skills/data-datasets/scripts/validate_dataset_dicts.py and sub-skills/data-datasets/scripts/validate_dataset_registration.py check dataset records/catalog behavior.
• Command builders: training, inference/demo, export, and analysis sub-skills include dry-run command builders that print commands or plans but do not run heavyweight work.

Source Caveats Captured

• The source checkout used for this skill had package version 0.6 and a clean git state at the recorded commit in provenance.
• The source demo CLI was treated as evidence rather than a runtime dependency because its demo.py imports a non-package path in this checkout; use the inference sub-skill helpers and API patterns instead.
• Caffe2 was not available in the inspection environment, so Caffe2 export guidance is optional and gated behind dependency checks.