Use eval-reid to score a trained ReID checkpoint on a dataset's query/gallery split.
!!! example
=== "CLI"
Evaluate a checkpoint on Market1501:
```bash
boxmot eval-reid \
--weights runs/reid_train/osnet_market/best.pt \
--dataset market1501 \
--data-dir /data/reid \
--device 0
```
Save results to a custom directory:
```bash
boxmot eval-reid \
--weights runs/reid_train/lmbn_joint/best.pt \
--dataset msmt17 \
--data-dir /data/reid \
--batch-size 128 \
--output runs/reid_eval
```
eval-reid loads a trained checkpoint, rebuilds the matching backbone, extracts query and gallery embeddings, computes the distance matrix, and reports ranking metrics.
If the checkpoint stores the model architecture, you can omit --model. Otherwise pass the architecture explicitly.
The command reports and saves:
mAPrank1rank5rank10
By default the JSON summary is written next to the checkpoint as eval_<dataset>.json. Use --output to place it elsewhere.
eval-reid uses the same registered ReID datasets as training. The selected dataset must expose query and gallery splits under the dataset root passed to --data-dir.
eval-reid is available from both the CLI and Python facade via BoxMOT.eval_reid(...).
::: mkdocs-click :module: boxmot.engine.cli :command: boxmot :depth: 1 :command: eval_reid :style: table :prog_name: boxmot eval-reid