技能创建器
skill-creator
创建、改进和评估自定义技能,把专业流程转化为可重复使用的 AI 能力。
evaluation-toolkit【数据与研究】Evaluate PySOT benchmark result layouts, metric families, eval commands, hp_search, and dataset adapter troubleshooting.
独立 GitHub 快照 · 自动分类 · 以原作者文档为准
Use this sub-skill when the task is about evaluating already-generated PySOT tracker results, checking benchmark result layouts, selecting the metric family, running the PySOT evaluation CLI, interpreting OPE/AR/EAO/F1 output, using hyperparameter-search result conventions, or debugging dataset/result adapter failures.
baseline/ or longterm/ folders, GOT-10k server-style files, or --tracker_prefix matching.../tracking-inference/.../training-data/.../configuration-models/.Identify the benchmark family and result root.
<tracker_path>/<dataset>/<tracker_name>/....results/.testing_dataset/<dataset>/; the bundled validator below only checks result-tree shape.Read references/datasets-and-results.md for supported dataset names, JSON sidecars, and expected tracker result files.
Run the safe validator before full evaluation:
python scripts/validate_results_layout.py \
--tracker-path results \
--dataset OTB100 \
--tracker-prefix siamrpn
This does not import PySOT, open images, require CUDA, or compute metrics. It fails clearly when the dataset result directory or prefix-selected tracker directories are missing, and it prints an evaluation command skeleton when PySOT’s Python evaluator supports the dataset family.
Read references/metrics.md to map the dataset family to OPEBenchmark, AccuracyRobustnessBenchmark, EAOBenchmark, or F1Benchmark and to interpret printed columns.
Read references/workflows.md for concrete eval and hp_search command templates, flag semantics, and VOT/server benchmark notes.
If validation or evaluation fails, use references/troubleshooting.md before changing code. Common fixes are prefix correction, moving result directories under the expected dataset root, installing the legacy toolkit.utils.region extension with Cython<3, and reducing multiprocessing to --num 1.
Safe checks in this sub-skill:
Full metric evaluation is not a safe default check. It requires user-supplied benchmark datasets, JSON sidecars, tracker result files, and a compatible Python environment. Hyperparameter search additionally requires a model snapshot, PySOT config, benchmark images, and CUDA because the source workflow loads the model with .cuda().
Inputs usually needed:
tracker_path: root containing dataset result directories, for example results.dataset: one of the supported dataset family names described in the references.tracker_prefix: optional prefix used to select tracker directories under <tracker_path>/<dataset>/.testing_dataset/<dataset>/ with <dataset>.json sidecar and image/ground-truth files referenced by the sidecar.Outputs: