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 and troubleshoot Folium maps, GeoJSON layers, choropleths, and plugins for browser-rendered Leaflet output.
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 a task asks for Folium maps, layers, choropleths, plugins, or browser-rendered HTML output from Python. It is organized by user workflow, not by source file.
python -m pip install folium.python -c "import folium; print(folium.__version__)".pandas, geopandas, pillow, flask, jenkspy, or selenium.sub-skills/map-and-layers/ — base maps, tiles, markers, popups, layer controls, vector/raster overlays, custom panes, browser embedding, PNG export, and JS/CSS resource handling.sub-skills/geojson-and-data/ — GeoJson, TopoJson, Choropleth, GeoDataFrame and __geo_interface__ inputs, styling, tooltips, popups, and coordinate-order troubleshooting.sub-skills/plugins/ — clustering, drawing, search, time sliders, realtime data, layer comparison, vector tiles, and WebGLEarth.references/package-overview.md — high-level package summary, output model, and optional dependency map.references/troubleshooting.md — cross-cutting install/import, optional dependency, browser/CDN, and staleness issues.references/repo-provenance.md — source snapshot and refresh baseline.scripts/smoke_render_map.py — deterministic HTML render smoke check.import folium
m = folium.Map(location=[45.5236, -122.6750])
m.save("map.html")
For notebook work, m._repr_html_() renders the map inline, while m._repr_png_() only works when PNG export is enabled and the browser export dependencies are available.
map-and-layers when the user asks to build a map, add tiles or markers, compose layers, embed Folium in Flask or HTML, or diagnose PNG/export issues.geojson-and-data when the task involves GeoJSON/TopoJSON, GeoDataFrame input, choropleths, key_on, feature styling, or coordinate-order issues.plugins when the task involves clustering, draw/search controls, time-based data, live feeds, vector tiles, or WebGLEarth.references/repo-provenance.mdreferences/package-overview.mdSKILL.mdIf the current checkout commit, working tree state, or package version no longer matches references/repo-provenance.md, refresh the skill before trusting it for new work.
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。