---

name: detrex description: "Use detrex for detection-transformer configs, training/evaluation, demos, model zoo conversion, and package API debugging." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

detrex repo skill

detrex is a Detectron2-based toolbox for Transformer object detection, segmentation, and related DETR-family research workflows. Use this skill when a task names detrex or asks for DETR/DINO/Deformable-DETR/MaskDINO/CO-MOT configs, training, evaluation, demos, checkpoint conversion, or package API debugging.

Before acting

1. Read references/repo-provenance.md when checking whether this skill matches a checkout or when deciding whether to refresh it.
2. Read references/environment-and-installation.md before install/build/import work.
3. Run scripts/check_environment.py for safe import, CUDA-extension, config, and tool-help checks.
4. Use references/troubleshooting.md for cross-cutting install/import/backend issues, then route to the closest sub-skill troubleshooting page.

Route by task

User need	Read
Build or debug train/eval commands, LazyConfig overrides, datasets, fast debug, AMP, EMA, WandB, DDP, Hydra, or Slurm command shape	training-and-configs
Choose a project/model family, interpret model zoo rows, use DINO/MaskDINO/CO-MOT project notes, inspect/convert DETR-family checkpoints, or change pretrained backbones	model-zoo-and-converters
Run or construct image/video demos, visualize JSON predictions or datasets, inspect model structure/FLOPs, or build benchmark commands	tools-and-demos
Import/inspect Python APIs for layers, losses, matchers, backbones, data mappers, config helpers, checkpointing, EMA, distributed utilities, WandB writer, or compiled ops	package-apis

Minimal install/import orientation

Public source installations normally require Linux, Python 3.7+, matching PyTorch/torchvision, Detectron2, compiler tools, and CUDA toolkit when the custom CUDA extension is needed. A minimal validation sequence is:

python -c "import torch, torchvision; print(torch.__version__, torchvision.__version__)"
python -c "import detectron2; import detrex; print('detrex import ok')"
python scripts/check_environment.py --strict --check-config common/train.py
python scripts/check_environment.py --strict --check-cuda-extension

Only require the CUDA-extension check when the task needs MultiScaleDeformableAttention, Deformable-DETR, DINO, or other compiled-op paths. CPU-safe API/config work can still be valid without running full GPU demos or COCO training.

Operating guardrails

• Do not assume the original source checkout is available. Use installed package modules or bundled scripts/references in this skill.
• Do not download model weights, demo inputs, datasets, or benchmark assets unless the user explicitly asks and provides sources or approval.
• Treat full training, full COCO evaluation, real demos, benchmarks, and tracking evaluation as expensive/native workflows that need user-provided artifacts and runtime approval.
• Keep config, checkpoint, dataset, and backbone families aligned; DETR-family checkpoints are not interchangeable solely because they share a backbone.
• Use project-specific trainers when the model family documents a hacked or specialized trainer route.
• For custom datasets, validate Detectron2 registration, metadata, file paths, and mapper choice before debugging model code.