Routes 3D ResNets PyTorch video action-recognition workflows across training, inference, and data preparation.
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Operate doccano for text annotation, dataset import/export, auto-labeling, deployment, and repo maintenance.
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설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
doccano is a text annotation platform with a Django backend, a Nuxt frontend, and CLI/deployment entry points for local, Docker, and cloud use.
Use this skill when a task mentions doccano itself, project/label/member workflows, dataset import or export, auto-labeling, deployment, or repository maintenance for this checkout.
| Route | Read when |
|---|---|
sub-skills/setup-and-deploy/ | You need install, initialize, run, package, Docker, or cloud deployment guidance. |
sub-skills/project-annotation/ | You need project creation, label setup, members, comments, annotation, cloning, or metrics. |
sub-skills/data-transfer/ | You need dataset import/export formats, validation, encodings, or format-specific troubleshooting. |
sub-skills/auto-labeling/ | You need template selection, request testing, response mapping, label mapping, or auto-labeling activation. |
sub-skills/setup-and-deploy/SKILL.md.python-repository-maintenance in mind and start with sub-skills/setup-and-deploy/SKILL.md for the build/runtime entry points.Use the installed package and CLI before deeper work:
pip install doccano
python -I -c "import backend; import backend.cli; print(backend.__file__)"
doccano --help
If the package was already installed, python -m pip check is a useful follow-up health check.
references/overview.md for the repo map and major surfaces.references/task-types.md when you need the supported project types or their annotation shapes.references/cli-reference.md for the command list and environment variables.references/troubleshooting.md for cross-cutting install, runtime, and validation failures.references/repo-provenance.md before deciding whether the skill is stale.references/repo-routing-metadata.json when you need the routing contract consumed by repo-skills-router.scripts/cli-smoke.sh for a fast install-and-CLI sanity check.아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.
로그인 없이 쓸 수 있습니다. 사용 후기, 질문, 작동하지 않은 환경 등을 편하게 남겨 주세요.
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Routes 3D ResNets PyTorch video action-recognition workflows across training, inference, and data preparation.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Guide 3DDFA Python inference, geometry rendering, training/evaluation, and optional C++ ONNX workflows for 3D dense face alignment.
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Routes 3DDFA_V2 face-alignment setup, still-image demos, video tracking, and ONNX benchmarking workflows.
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Operate AB3DMOT 3D multi-object tracking workflows for KITTI and nuScenes data, tracking, evaluation, and visualization.
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Use Hugging Face Accelerate for PyTorch training-loop migration, distributed launch/configuration, DeepSpeed/FSDP/TPU backend setup, big-model inference/offload, checkpointing, tracking, and troubleshooting.
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Route Acme reinforcement-learning framework tasks across core loops, replay/data, JAX agents, and TensorFlow/Sonnet agents.
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