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。
Create, polish, and troubleshoot figures4papers-style publication-ready matplotlib figures, including grouped bars, trends, radar charts, heatmaps, concept diagrams, manifolds, and high-DPI/vector export workflows.
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 repo skill when the user wants publication-quality Python/matplotlib figures in the figures4papers house style: polished paper plots, wide comparison panels, print-safe encodings, radar or heatmap layouts, conceptual manifolds, clean typography, and reproducible PNG/PDF/SVG export.
This repository is a script-and-skill collection, not an installable Python package. Future agents can use this generated skill without the original checkout by following the bundled references and scripts below.
Install the plotting packages needed by the selected route:
python -m pip install numpy matplotlib scipy seaborn python-dateutil
numpy and matplotlib cover the core templates. scipy supports KDE,
diffusion, and manifold helpers; seaborn supports seaborn-style heatmaps;
python-dateutil helps date/month timelines. System LaTeX is optional and only
needed when exact text.usetex=True rendering is required. Portable bundled
scripts keep TeX disabled by default.
Run the environment checker when import or export behavior is uncertain:
python scripts/check_figure_env.py --output figure_env_smoke --formats png
| Task | Open |
|---|---|
| Grouped bars, horizontal ablations, log-speed bars, multi-metric comparison rows, direct annotations, legend-only axes | sub-skills/bar-comparison-figures/SKILL.md |
| Trend/line panels, cumulative timelines, event arrows, radar/polar benchmark comparisons, heatmaps, colorbars, matrix panels | sub-skills/trend-radar-heatmap-figures/SKILL.md |
| Concept distributions, manifold clouds, KDE contours, Swiss-roll/diffusion matrices, shaded spheres, geodesic arrows, 3D-style geometry | sub-skills/concept-manifold-diagrams/SKILL.md |
| Shared style, palette, rcParams, export formats, and helper functions | references/style-and-export.md |
| Natural-language pattern routing across all figure families | references/project-patterns.md |
| Cross-cutting install/import, font, TeX, backend, shape, and output failures | references/troubleshooting.md |
| Repository snapshot and refresh baseline | references/repo-provenance.md |
FigureStyle, apply_publication_style,
create_subplots, finalize_figure, grouped bar, trend, heatmap, scatter,
and shaded-sphere helpers.Read references/repo-provenance.md before refreshing this skill against a changed repository. This candidate was generated for DisCo's managed repo-skill format, but this construction run intentionally does not import it into the live repo-skills library.
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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。