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name: reid-lifecycle description: "Use BoxMOT for ReID training, evaluation, comparison, export, embedding extraction, and registry inspection." metadata: disco-role: operating disable-model-invocation: true license: AGPL 3.0

ReID Lifecycle

Use this sub-skill when the task involves ReID models: training a backbone, evaluating a checkpoint, comparing checkpoints across datasets, exporting deployment formats, generating embeddings, or inspecting registries.

Covers

• boxmot train
• boxmot eval-reid
• boxmot compare-reid
• boxmot export
• BoxMOT.train(...), BoxMOT.eval_reid(...), BoxMOT.export(...), and BoxMOT.embed(...)
• boxmot.ReIDModel
• training recipes, ReID dataset names, export formats, and checkpoint metadata

Does not cover

• live tracking or tracker output schemas unless the question is about ReID features used by a tracker
• benchmark generate / eval / tune / research cache workflows
• native C++ build tooling except native ReID notes relevant to C++ trackers

Read first

• ReID lifecycle workflows
• Model and export overview
• Troubleshooting
• Registry summary script

Good prompts for this route

• "Train a ReID model on Market1501."
• "Evaluate this checkpoint and save mAP/rank metrics."
• "Compare these two checkpoints across Duke and Market1501."
• "Export an OSNet checkpoint to ONNX or TensorRT."
• "Generate embeddings for image crops and pass them to a tracker."

Typical workflow

1. Determine whether the user needs training, single-checkpoint evaluation, multi-checkpoint comparison, export, or embedding inference.
2. Verify the dataset root and expected split layout before suggesting heavy runs.
3. Check checkpoint metadata when model architecture is ambiguous.
4. Match preprocess, image size, and inference feature overrides between train/eval/export as needed.
5. Use the registry summary script for a safe inventory before recommending a model or format.

Entry points

boxmot train --model osnet_x0_25 --dataset market1501 --data-dir /data/reid --device cpu
boxmot eval-reid --weights runs/reid_train/exp/best.pt --dataset market1501 --data-dir /data/reid --device cpu
boxmot compare-reid --weights runs/reid_train/exp/best.pt --target market1501=/data/reid/Market-1501-v15.09.15
boxmot export --weights osnet_x0_25_msmt17.pt --include onnx

Python facade:

from boxmot import BoxMOT, ReIDModel

api = BoxMOT()
train_result = api.train(model="mobilenetv4", dataset="market1501", data_dir="/data/reid", epochs=5)
metrics = api.eval_reid(weights=train_result.weights_path, dataset="market1501", data_dir="/data/reid")
features = ReIDModel("osnet_x0_25_msmt17.pt", device="cpu").embed("crop.jpg")

Use scripts/reid_registry_summary.py when the user needs to know registered backbones, recipes, datasets, or export formats before launching a heavy job.