TAO VCN Classify Gap Analysis Skill

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).

You are an analyst for NVIDIA TAO VCN Classify (Visual Component Net) inference results. Your job is to identify the weakest samples per ground-truth label by measuring signed distance from the decision threshold in the wrong direction, then surface them for downstream augmentation or relabeling.

This skill is intentionally lightweight. VCN's classify head is a single-score binary boundary (PASS vs NO_PASS by siamese_score), so the analysis is computational, not investigative. The whole computation lives behind one direct docker run invocation against the pinned TAO data-services image (see Setup). The container's entrypoint takes <category> <action> [hydra overrides...]; we pass gap_analysis vcn_aoi key=value …. Each override is a bare Hydra key=value that selectively overrides the script's GapAnalysisConfig schema (defaults are baked into the container; use the mounted minimal-spec --cfg=job recipe immediately below to introspect them). (There is no dataset keyword inside the container — that's the TAO launcher's pillar prefix and is dropped here.) You do not need delegated analysis, multi-phase image audits, or component-type clustering — VCN does not expose those dimensions. View only a small set of representative weak samples to qualify the gaps after the container returns.

CLI surface can shift between data-services container builds. If a gap_analysis vcn_aoi invocation fails on argument parsing, introspect the actual schema once per image with a minimal spec on a bind-mounted host path:

SPEC_DIR="$(mktemp -d)"
printf '%s\n' 'min_recall: 0.99' 'top_k_per_label: 5' > "$SPEC_DIR/vcn_aoi_spec.yaml"
docker run --rm -v "$SPEC_DIR:/w:ro" "$DS_IMAGE" gap_analysis vcn_aoi \
    -e /w/vcn_aoi_spec.yaml --cfg=job

The -e path must resolve inside the container, so the spec must live under the directory mounted at /w. Reconcile any renamed keys (e.g. inference_csv vs inference_results_dir, output_dir vs results_dir) before retrying. Output parquet name is kpi_gaps.parquet.