---

name: training-evaluation description: "Train, evaluate, configure, and inspect Torchreid data workflows for person re-identification." disable-model-invocation: true metadata: disco-role: operating license: MIT

Torchreid training and evaluation

Use this sub-skill when a task asks for Torchreid image/video ReID training, checkpoint evaluation, dataset/data-manager setup, YACS-style config edits, sampler/optimizer/lr-scheduler choices, mean/std analysis, or split-log parsing. It covers Torchreid 1.4.0 (torchreid distribution/import) and the unified scripts/main.py-style workflow in a self-contained form.

Route here for

• Training or evaluating OSNet/ResNet-family ReID models on Market1501, DukeMTMC-reID, MSMT17, CUHK03, MARS, iLIDS-VID, PRID2011, or custom datasets.
• Same-domain, cross-domain, multi-source, target-training-loader, and video-ReID data manager construction.
• ImageDataManager, VideoDataManager, Engine.run, ImageSoftmaxEngine, ImageTripletEngine, VideoSoftmaxEngine, VideoTripletEngine usage.
• YACS config overrides such as model.load_weights, test.evaluate, data.save_dir, sampler.train_sampler, train.lr_scheduler, cuhk03.classic_split.
• Dataset layout checks, custom dataset registration, compute_mean_std, and parse_test_res-style log analysis.

Route elsewhere

• Embedding-only APIs, FeatureExtractor, model-key catalogs, raw distance matrices, CMC/mAP API internals, re-ranking details, model complexity, activation maps, or ranked visualization internals: use ../feature-extraction/SKILL.md.
• ONNX, OpenVINO, TFLite, or other deployment/export work: use ../model-export/SKILL.md.
• Deep Mutual Learning, OSNet-AIN NAS, or PA-100K attribute-recognition project workflows are excluded long-tail gaps in this generated skill; do not claim runnable coverage unless a future extension bundles those project sources.

Operating sequence

1. Determine whether the request is train, evaluate/test-only, data inspection, config generation, mean/std, or log parsing.
2. Check dataset type and layout in references/data-formats.md. Do not assume downloads are available; many public dataset links are fragile or gated.
3. Build or edit the config using references/configuration.md. Prefer explicit dotted opts for final commands.
4. Choose the workflow recipe from references/workflows.md. Use CUDA only as an optional performance backend unless the user has already verified it.
5. Confirm API signatures and output structures in references/api-reference.md before writing code.
6. If errors appear, diagnose with references/troubleshooting.md.

Bundled safe helpers

These scripts are safe by default: --help never imports heavy packages, and no helper runs training by default.

• scripts/torchreid_train_eval.py: prints unified Torchreid train/eval command plans, merges embedded official templates or a supplied YAML config, validates common opts, and can write a resolved config.
• scripts/compute_mean_std.py: checks dataset prerequisites and computes channel statistics only when --compute is explicitly supplied.
• scripts/parse_test_results.py: parses split directories containing test.log* files and reports missing or incomplete logs clearly.

Safe verification quick checks

From this sub-skill directory, future agents can run:

python scripts/torchreid_train_eval.py --help
python scripts/torchreid_train_eval.py --template im_osnet_x1_0_softmax_256x128_amsgrad_cosine --mode eval --root /path/to/reid-data --weights /path/to/model.pth.tar --visrank --save-dir log/eval_osnet --dry-run
python scripts/compute_mean_std.py --help
python scripts/compute_mean_std.py /path/to/reid-data market1501 --check-only
python scripts/parse_test_results.py --help

Only run actual training/evaluation/mean-std computation after the user provides local datasets, checkpoint paths when needed, and compute/backend approval.