FreePublished on GitHub

fast-reid

Operate FastReID setup, configuration, datasets, model/inference APIs, training/evaluation workflows, deployment/export paths, and extension projects for person and vehicle re-identification.

The English summary is being prepared. Showing the original description.

VectorSpaceLab/AREX-Skill

See how to install

Files included (36)

SKILL.md4.1 KB
references/package-map.md5.5 KB
references/repo-provenance.md1.8 KB
references/repo-routing-metadata.json514 B
references/troubleshooting.md6.0 KB
scripts/check_fastreid_environment.py6.4 KB
sub-skills/data-and-datasets/references/data-api.md8.8 KB
sub-skills/data-and-datasets/references/dataset-formats.md6.8 KB
sub-skills/data-and-datasets/references/troubleshooting.md7.0 KB
sub-skills/data-and-datasets/scripts/validate_dataset_layout.py18.6 KB
sub-skills/data-and-datasets/SKILL.md3.9 KB
sub-skills/deployment-and-projects/references/deployment-workflows.md10.9 KB
sub-skills/deployment-and-projects/references/project-extensions.md8.6 KB
sub-skills/deployment-and-projects/references/troubleshooting.md8.5 KB
sub-skills/deployment-and-projects/scripts/check_deployment_dependencies.py7.0 KB
sub-skills/deployment-and-projects/scripts/project_import_probe.py11.9 KB
sub-skills/deployment-and-projects/SKILL.md3.3 KB
sub-skills/modeling-and-inference/references/inference-workflows.md5.5 KB
sub-skills/modeling-and-inference/references/model-api.md6.2 KB
sub-skills/modeling-and-inference/references/troubleshooting.md5.8 KB
sub-skills/modeling-and-inference/scripts/feature_extraction_smoke.py10.5 KB
sub-skills/modeling-and-inference/scripts/model_forward_smoke.py6.0 KB
sub-skills/modeling-and-inference/SKILL.md2.8 KB
sub-skills/setup-and-configuration/references/configuration.md5.0 KB
sub-skills/setup-and-configuration/references/model-zoo-and-recipes.md4.6 KB
sub-skills/setup-and-configuration/references/troubleshooting.md4.4 KB
sub-skills/setup-and-configuration/scripts/config_merge_check.py6.6 KB
sub-skills/setup-and-configuration/SKILL.md2.5 KB
sub-skills/training-and-evaluation/references/evaluation.md5.2 KB
sub-skills/training-and-evaluation/references/trainer-api.md8.7 KB
sub-skills/training-and-evaluation/references/training-cli.md10.3 KB
sub-skills/training-and-evaluation/references/troubleshooting.md7.2 KB
sub-skills/training-and-evaluation/scripts/run_training_entrypoint.py7.6 KB
sub-skills/training-and-evaluation/scripts/train_command_builder.py11.2 KB
sub-skills/training-and-evaluation/scripts/training_cli_help_check.py3.9 KB
sub-skills/training-and-evaluation/SKILL.md3.8 KB

SKILL.md (original)

Review the instructions the agent will receive before you install.

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

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.
Confirm the intended backend. MODEL.DEVICE defaults to cuda; CPU smoke checks must override it explicitly.
Confirm required data/checkpoints. Real train/eval/inference requires local datasets and often local checkpoints or pretrain weights.
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.

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Install

Install with a command

npx skills add https://github.com/VectorSpaceLab/AREX-Skill --skill fast-reid

Paste it into your terminal and run it (requires Node.js). You can choose which agents to install it for, such as Claude Code or Codex.

Ask your agent

Please install the following Agent Skill.
https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/fast-reid

First review SKILL.md and the bundled files and confirm there is nothing suspicious (such as sending data externally or reading credentials). Then place it in ~/.claude/skills/fast-reid/ for Claude Code, or ~/.agents/skills/fast-reid/ for Codex.

Send this text to Claude Code or Codex as is. It checks the contents before installing.

View source on GitHub

Repository: VectorSpaceLab/AREX-Skill
Stars: 340
Forks: 26
Category: DevOps
License: Apache-2.0
Original language: English
Last updated: Sep 3, 2026

A skill is a set of instructions and scripts for an agent. cccskills does not guarantee that skills are safe. Review the contents before you install.