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name: data-preparation description: "Guides SiamMask benchmark and training data acquisition, layout validation, crop/index preprocessing, VOT JSON generation, and data-path troubleshooting." disable-model-invocation: true metadata: disco-role: operating license: MIT

SiamMask Data Preparation

Use this sub-skill when the task is about VOT/DAVIS/YouTube-VOS benchmark data, COCO/DET/VID/YouTube-VOS training data, crop511 preprocessing, dataset JSON files, or missing data paths.

Safety First

Most original preprocessing steps involve large downloads, unzips, symlinks, multiprocessing crops, or generated files. Keep commands in dry-run/read-only mode until the user explicitly approves network, disk, and runtime costs.

Main Routes

User intent	What to do
"Check whether data is prepared"	Run scripts/check_dataset_layout.py and read references/data-layouts.md.
"Generate VOT metadata JSON"	Use scripts/generate_vot_json.py on an existing VOT directory; it adapts the repo metadata generator without downloading data.
"Run original preprocessing/crop scripts"	Use scripts/run_data_prep.py in dry-run mode, then add --run only after approving side effects.
"Prepare training data"	Read references/workflows.md and validate COCO/DET/VID/YouTube-VOS generated outputs before training.
"Dataset errors during tracking/training"	Read references/troubleshooting.md and root troubleshooting.

Bundled Helpers

• scripts/check_dataset_layout.py: read-only JSON report for VOT, DAVIS, YouTube-VOS, COCO, DET, VID, or the full training data mix.
• scripts/generate_vot_json.py: self-contained VOT metadata generator for existing local VOT datasets.
• scripts/run_data_prep.py: dry-run-first launcher for checkout-local data-preparation scripts; useful for auditing cwd, PYTHONPATH, and arguments before heavy preprocessing.

Example read-only check:

python scripts/check_dataset_layout.py --data-root <siammask-checkout>/data --dataset training

Example VOT metadata generation:

python scripts/generate_vot_json.py \
  --dataset-root <siammask-checkout>/data/VOT2019 \
  --dataset-name VOT2019 \
  --output <siammask-checkout>/data/VOT2019.json

Cross-Links

• After benchmark data exists, use ../tracking/SKILL.md for test/eval/tune runs.
• After training data exists, use ../training/SKILL.md for CUDA training dry-runs.
• Use root install/setup for Cython extension and pycocotools build details.