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siammask

siammask

Routes SiamMask visual object tracking, segmentation, benchmark evaluation, dataset preparation, and CUDA training workflows.

Independent GitHub snapshot · Automated classification · Original documentation governs

スキルの説明

GitHub

name: siammask description: "Routes SiamMask visual object tracking, segmentation, benchmark evaluation, dataset preparation, and CUDA training workflows." disable-model-invocation: true metadata: disco-role: operating license: MIT

SiamMask Repo Skill

Use this skill when a task involves the SiamMask CVPR/TPAMI visual object tracking and segmentation codebase: running a demo, evaluating VOT/DAVIS/YouTube-VOS results, preparing benchmark or training data, or training SiamMask/SiamRPN checkpoints.

This skill is self-contained guidance plus bundled helpers. It assumes the user has or will provide a SiamMask checkout to operate on; pass that checkout to bundled scripts with --repo-root <siammask-checkout> instead of relying on this skill's construction checkout.

Start Here

  1. Read references/repo-provenance.md when checking whether this skill matches a checkout or before refreshing it.

  2. Read references/install-and-setup.md before running any workflow; SiamMask is legacy PyTorch code and has important NumPy, CUDA, OpenCV, and Cython-extension constraints.

  3. Run the read-only environment probe when you need to verify imports and backend readiness:

    python scripts/check_environment.py --repo-root <siammask-checkout> --expect-cuda auto --check-cli
    
  4. Build checkout-local Cython extensions when tracking/evaluation imports fail on region or _mask modules:

    bash scripts/build_extensions.sh --repo-root <siammask-checkout> --python <python-in-your-env>
    
  5. Pick the nearest sub-skill below for the user-facing task.

Route Map

Task signalRead nextWhy
Run the OpenCV demo, track one sequence, generate VOT/DAVIS/YouTube-VOS benchmark outputs, evaluate VOT result folders, or tune tracking hyperparameterssub-skills/tracking/SKILL.mdCovers inference-time model/config/checkpoint selection, mask/refine behavior, benchmark result layout, and dry-run-first launch helpers.
Train SiamMask base, train the refine/sharp model, train the unofficial SiamRPN/ResNet baseline, resume checkpoints, inspect experiment configs, or plan GPU/batch-size usagesub-skills/training/SKILL.mdCovers CUDA-only training scripts, dataset JSON/crop requirements, ResNet pretraining, snapshots, TensorBoard logs, and launch command composition.
Download or validate VOT/DAVIS/YouTube-VOS/COCO/DET/VID data, build COCO pycocotools, crop crop511 training data, generate dataset JSON indexes, or debug missing data pathssub-skills/data-preparation/SKILL.mdCovers raw-data sources, expected directory layouts, preprocessing order, read-only layout checks, and VOT metadata generation.

Repo-Wide Facts to Keep in Mind

  • SiamMask is not packaged with setup.py or pyproject.toml. Its Python entry points expect the checkout root on PYTHONPATH; bundled helpers add this automatically.
  • The official README tested Ubuntu 16.04, Python 3.6, PyTorch 0.4.1, CUDA 9.2, and RTX 2080-era GPUs. Modern environments can import the code, but keep NumPy below 1.24 unless you patch legacy np.float/np.int aliases.
  • Tracking and VOT evaluation can be CPU-capable, although slow. Training scripts and the VOS tuning script call CUDA APIs unconditionally and require a CUDA-capable PyTorch environment.
  • Interactive demo mode uses OpenCV GUI ROI selection. Headless machines should use benchmark/test wrappers or non-interactive validation instead.
  • Checkpoint files and full datasets are not bundled. Treat downloads as user-authorized network/data acquisition steps and validate paths before starting long runs.
  • Read references/model-overview.md when choosing between SiamMask base, SiamMask refine/sharp, and SiamRPN experiment families.
  • Read references/troubleshooting.md for cross-cutting install/import/backend failures.

Avoid Using This Skill When

  • The task is about a different tracker family or a general OpenCV/PyTorch issue with no SiamMask-specific code, config, checkpoint, or dataset signal.
  • The user asks for paper-level distillation rather than operating this repository.
  • The user needs to import/export DisCo skills; that is handled by repo-skill management workflows, not this operating skill.
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