Individual skill

tao-train-bevfusion

BEVFusion for multi-sensor 3D object detection. Fuses LiDAR point clouds and camera images in bird's-eye-view (BEV) space, used in autonomous driving for robust 3D perception. Use when training, evaluating, or running inference for a TAO BEVFusion model. Trigger phrases include "train BEVFusion", "LiDAR + camera fusion", "BEV 3D detection", "multi-sensor 3D perception".

skills/tao-train-bevfusion/SKILL.md

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Repository stars (not skill ratings): 3,900
Repository last updated: Nov 6, 2026
License: Apache-2.0

Observed agent integration: No specific integration evidence. Standard SKILL.md installation does not imply verified compatibility.

agent-skills Repository topic
agentic-ai Repository topic
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claude-code Repository topic
codex Repository topic
coding-agents Repository topic
cuda Repository topic
developer-tools Repository topic
llm Repository topic
omniverse Repository topic
multi Declared skill tag
sensor Declared skill tag
3d Declared skill tag
detection Declared skill tag
engineering Derived category

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BENCHMARK.mddoc · 7,115 bytes
SKILL.mdcore · 13,477 bytes
SKILL.mdcore · 13,477 bytes
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SKILL.mdcore · 13,477 bytes
SKILL.mdcore · 13,477 bytes
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config/skillspector-baseline.yamlasset · 834 bytes
evals/evals.jsonasset · 787 bytes
references/skill_info.yamlasset · 2,576 bytes
references/spec_template_dataset_convert.yamlasset · 156 bytes
references/spec_template_evaluate.yamlasset · 7,002 bytes
references/spec_template_inference.yamlasset · 7,002 bytes
references/spec_template_train.yamlasset · 7,001 bytes
schemas/dataset_convert.schema.jsonasset · 2,674 bytes
schemas/evaluate.schema.jsonasset · 96,908 bytes
schemas/inference.schema.jsonasset · 96,910 bytes
schemas/manifest.jsonasset · 12,750 bytes
schemas/train.schema.jsonasset · 96,896 bytes
skill-card.mddoc · 4,771 bytes
skill.oms.sigasset · 7,309 bytes

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# BEVFusion

> **Standalone install?** If this session was not initialized by the TAO skill bank plugin, run the `tao-setup` skill first (host preflight, credentials, cross-skill discovery).

BEVFusion for multi-sensor 3D object detection. Fuses LiDAR point clouds and camera images in bird's-eye-view (BEV) space. Used in autonomous driving for robust 3D perception.

Set pretrained backbone paths for Swin image backbone.

BEVFusion requires the BEVFusion-specific TAO container
`nvcr.io/nvidia/tao/tao-toolkit:5.5.0-pyt`. <!-- unpinned: BEVFusion-only container --> Shared TAO PyTorch 7.x images do
not package `mmdet3d` and fail before any BEVFusion action can parse its
spec. The model-skill action is named `dataset_convert`, but the 5.5 container
CLI subtask is `bevfusion convert -e <spec>`.