Routes 3D ResNets PyTorch video action-recognition workflows across training, inference, and data preparation.
日本語の概要は準備中です。原文の説明を表示しています。
Routes evo trajectory-evaluation workflows for APE/RPE, trajectory I/O, result analysis, configuration, and programmatic usage.
インストールする前に、エージェントに与えられる指示の中身を確認できます。
Use this skill for the evo Python package, which evaluates odometry and SLAM trajectories, loads and converts trajectory files, compares saved results, manages package settings, and supports programmatic plotting and notebooks.
pip install -e ..pip install evo.pip install evo[rerun] for Rerun workflows.pip install evo[gui] for the Qt plot backend.pip install evo[geo] for map tiles via contextily.python -I -c "import evo; print(evo.__version__)"python scripts/check_env.pyevo --help| User need | Go here |
|---|---|
| APE / RPE scoring, alignment, delta selection, result zips | sub-skills/metrics/SKILL.md |
| Trajectory file formats, converters, sync, align, export, bag / MCAP loading | sub-skills/trajectory-data/SKILL.md |
| Saved result comparison, merge, table export, label cleanup | sub-skills/result-analysis/SKILL.md |
evo_config, evo pkg, evo cat_log, evo_ipython | sub-skills/configuration/SKILL.md |
Custom Python scripts, notebooks, plotting, Rerun, contextily | sub-skills/python-api/SKILL.md |
references/repo-provenance.md when you need to know whether this skill still matches the current checkout.references/troubleshooting.md for cross-cutting install/import, optional dependency, backend, and settings problems.scripts/check_env.py when you want a quick import-and-CLI smoke check from an installed environment.references/repo-provenance.md and refresh the skill when needed.まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
Routes 3D ResNets PyTorch video action-recognition workflows across training, inference, and data preparation.
日本語の概要は準備中です。原文の説明を表示しています。
Guide 3DDFA Python inference, geometry rendering, training/evaluation, and optional C++ ONNX workflows for 3D dense face alignment.
日本語の概要は準備中です。原文の説明を表示しています。
Routes 3DDFA_V2 face-alignment setup, still-image demos, video tracking, and ONNX benchmarking workflows.
日本語の概要は準備中です。原文の説明を表示しています。
Operate AB3DMOT 3D multi-object tracking workflows for KITTI and nuScenes data, tracking, evaluation, and visualization.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
Route Acme reinforcement-learning framework tasks across core loops, replay/data, JAX agents, and TensorFlow/Sonnet agents.
日本語の概要は準備中です。原文の説明を表示しています。