Routes 3D ResNets PyTorch video action-recognition workflows across training, inference, and data preparation.
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Use the GeoParquet specification and bundled inspection helpers to author, validate, distribute, or safely review geospatial Parquet data, including GeoParquet 2.0 vector metadata, external writer choices, and the alpha Parquet Raster proposal.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
Use this skill when a task involves GeoParquet, Parquet GEOMETRY/GEOGRAPHY
logical types, WKB geometry columns, the Parquet geo metadata key, CRS/PROJJSON,
spatial row-group statistics, GeoParquet distribution, STAC publication, or the
alpha Parquet Raster proposal.
This repository is a specification and example suite, not an installable Python
writer package. Do not tell a user to install geoparquet as if it were a
library. For local inspection helpers, install the dependencies appropriate to
the task, then run a neutral import check:
python -m pip install "pyarrow>=22" "jsonschema>=4" "geoarrow-pyarrow>=0.2"
python -c "import pyarrow, jsonschema; print(pyarrow.__version__)"
The helper scripts are self-contained and offline by default. They do not fetch PROJJSON schemas, resolve authority registries, install external writers, or contact out-of-database raster URIs. Keep the exact writer/reader versions and raw inspection output with any conformance or publication decision.
sub-skills/validate-geoparquet/SKILL.md.
Use it for geo JSON, native logical types, WKB layout, CRS, geometry types,
bbox, and row-group geospatial statistics. It distinguishes a full 2.0 file
from metadata-only or native-type-only Parquet.sub-skills/distribute-geoparquet/SKILL.md.
Use it for GDAL/OGR, DuckDB, gpio/geoparquet-io, Sedona, compression,
ordering, row groups, spatial partitioning, STAC, and old-reader tradeoffs.sub-skills/parquet-raster/SKILL.md.
Treat it as an alpha proposal only; do not transfer vector conformance rules
to raster columns.BYTE_ARRAY geometry columns annotated GEOMETRY or GEOGRAPHY, plus a
JSON geo metadata block.Read references/troubleshooting.md for
cross-cutting recovery guidance, and read
references/repo-provenance.md before deciding
whether this graph is stale relative to a repository checkout.
This graph does not implement a general GeoParquet writer, run production-scale conversion or benchmarks, install external tools, resolve every CRS authority, or certify the alpha raster proposal. It does include safe bundled vector metadata/footer helpers; their ownership and usage are described by the validation sub-skill.
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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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