算法艺术创作
algorithmic-art
通过代码和可调参数创作生成艺术与交互式视觉效果。
siammask【设计与创意】Routes SiamMask visual object tracking, segmentation, benchmark evaluation, dataset preparation, and CUDA training workflows.
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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.
Read references/repo-provenance.md when checking whether this skill matches a checkout or before refreshing it.
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.
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
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>
Pick the nearest sub-skill below for the user-facing task.
| Task signal | Read next | Why |
|---|---|---|
| Run the OpenCV demo, track one sequence, generate VOT/DAVIS/YouTube-VOS benchmark outputs, evaluate VOT result folders, or tune tracking hyperparameters | sub-skills/tracking/SKILL.md | Covers 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 usage | sub-skills/training/SKILL.md | Covers 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 paths | sub-skills/data-preparation/SKILL.md | Covers raw-data sources, expected directory layouts, preprocessing order, read-only layout checks, and VOT metadata generation. |
setup.py or pyproject.toml. Its Python entry points expect the checkout root on PYTHONPATH; bundled helpers add this automatically.np.float/np.int aliases.