---

name: fast-reid description: "Operate FastReID setup, configuration, datasets, model/inference APIs, training/evaluation workflows, deployment/export paths, and extension projects for person and vehicle re-identification." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

FastReID repo skill

Use this skill when a task involves FastReID (fastreid), person or vehicle re-identification, Market1501/DukeMTMC/MSMT17/VeRi/VehicleID style datasets, FastReID YAML configs, ReID feature extraction, rank/mAP/mINP evaluation, training/eval command construction, ONNX/Caffe/TensorRT export, or FastReID extension projects.

First checks

1. Confirm FastReID is importable. This inspected commit is source-only, so use a local checkout import path or a bundled script's --repo-root option when no installed distribution exists.
2. Confirm the intended backend. MODEL.DEVICE defaults to cuda; CPU smoke checks must override it explicitly.
3. Confirm required data/checkpoints. Real train/eval/inference requires local datasets and often local checkpoints or pretrain weights.
4. Run the safe root helper when setup or optional dependencies are unclear:

python scripts/check_fastreid_environment.py \
  --repo-root <FASTREID_REPO> \
  --config-file <CONFIG_YAML> \
  --device cpu

The helper imports modules and merges config only; it does not train, evaluate, download, or write checkpoints.

Route by task

Task signal	Read
Source-only import, dependencies, Python version, get_cfg, _BASE_, YAML merge, opts, model-zoo recipe/config selection	sub-skills/setup-and-configuration/
FASTREID_DATASETS, built-in dataset layouts, custom dataset registration, dataloaders, transforms, samplers, data preflight	sub-skills/data-and-datasets/
build_model, backbones/heads/losses/meta-architectures, DefaultPredictor, feature tensors, CPU model smoke, rank import quirks	sub-skills/modeling-and-inference/
Training/eval command building, DefaultTrainer, distributed flags, checkpoints/resume, solvers, metrics, logs, custom loops	sub-skills/training-and-evaluation/
ONNX/Caffe/TensorRT export/inference, optional deployment deps, FastRT, project-extension imports/config hooks	sub-skills/deployment-and-projects/

Shared references

• references/package-map.md — compact module, registry, signature, dependency, and route map for this FastReID version.
• references/troubleshooting.md — cross-cutting install/import, config, backend, dataset, checkpoint, inference, deployment, and project-extension failures.
• references/repo-provenance.md — source commit, package version, evidence paths, and refresh baseline.
• references/repo-routing-metadata.json — structured metadata for managed repo-skills-router import.

Shared script

• scripts/check_fastreid_environment.py — safe import/config/optional-backend probe. Use it before deeper workflow debugging or before deciding whether CUDA/ONNX/Caffe/TensorRT dependencies are available.

Operating boundaries

• This skill teaches FastReID operation; it does not reproduce model-zoo benchmarks by itself.
• Do not run long training/evaluation, network downloads, dataset acquisition, Caffe/TensorRT builds, or multi-node jobs without explicit user data, hardware, runtime, and budget approval.
• CPU checks can validate imports/config/model plumbing, but they do not prove CUDA training performance or TensorRT/Caffe runtime equivalence.
• Extension projects add registry entries after importing their packages or config hooks. If a project config fails with unknown keys or missing registry entries, route to deployment-and-projects before editing configs.