---

name: training-data description: "Guide PySOT training setup, dataset preparation, training JSON and crop layouts, config preflight, distributed launch, and training failure recovery." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

PySOT training-data operating sub-skill

Use this sub-skill when the user asks to train PySOT, prepare VID/YouTube-BB/DET/COCO training data, generate train.json, build a distributed launch command, debug TrkDataset data failures, or validate training config/data paths.

Do not treat this as proof that training can run on the current machine. Full PySOT training is a GPU, dataset, and pretrained-backbone workflow; safe checks here are parser/config/data-layout preflights only.

Fast route

1. Identify the task type:

   • data preparation: read references/data-preparation.md;
   • JSON/crop-layout debugging: read references/data-formats.md;
   • training command construction or resume/pretrain setup: read references/training-workflow.md;
   • failures: read references/troubleshooting.md.

2. Collect concrete inputs before giving a runnable command: PySOT checkout root, experiment config path, requested dataset names, prepared crop roots, annotation JSON files, GPU count, pretrained backbone path, resume checkpoint if any, and intended launch directory.

3. Run the bundled safe validator before training:

   python scripts/validate_training_config.py \
     --repo-root <pysot-checkout> \
     --config <experiment-config.yaml> \
     --check-files
   

   Omit --check-files when only reviewing a config that intentionally points to unavailable external datasets.

4. Only after the validator passes and the user confirms external data/GPU availability, construct the tools/train.py distributed command from references/training-workflow.md.

5. If the user asks for architecture/model-family explanations, route to the sibling configuration-models sub-skill. If the user asks for post-training demo/test, route to tracking-inference. If the user asks for metrics or result layouts, route to evaluation-toolkit.

Safety rules

• Do not run dataset crop scripts unless the user explicitly authorizes large downloads and destructive/large side effects.
• Do not run full tools/train.py as a default check; it calls CUDA paths and expects distributed environment variables, datasets, and pretrained weights.
• Mention that PySOT's package setup installs the toolkit distribution; importing pysot normally depends on running from the checkout with PYTHONPATH/editable-development style setup.
• Keep user commands parameterized. Avoid assuming dataset paths exist just because defaults appear in pysot.core.config.
• If a legacy extension build fails, include the Cython <3 guidance from references/troubleshooting.md.