name: visualization-and-analysis
description: "Route Det3D LiDAR, BEV, KITTI, prediction, training-log, FLOPs,
and headless-visualization analysis tasks."
metadata:
disco-role: operating
disable-model-invocation: true
license: Apache 2.0
Visualization and Analysis
Use this route for prediction/result rendering, BEV/LiDAR/KITTI visualization,
training-log summaries, learning curves, and model-complexity interpretation.
Use scripts/summarize_log.py for deterministic headless log summaries.
Workflow
- Identify artifact type: JSON log, pickle/result objects, KITTI text,
point-cloud/boxes, image/calibration, or model/config.
- Validate coordinate frames, box convention, classes, and score threshold
before rendering.
- Prefer saved images/text summaries on remote or headless hosts.
- Read visualization-workflows.md for
optional VTK/Open3D/Matplotlib paths and log-analysis.md
for scalar aggregation.
- Follow troubleshooting.md for display,
dependency, malformed-log, or empty-result failures.
FLOPs and parameter counts depend on the exact model/config/input shape and may
require the same compiled dependencies as model construction. A plot or summary
does not prove detector correctness; route metric/evaluator concerns to
training-and-evaluation.