Menu

Individual skill

tao-analyze-gaps-visual-changenet

0upvotes

Performs gap analysis on NVIDIA TAO VCN Classify (Visual Component Net) experiments by invoking the pinned TAO data-services container directly via `docker run … gap_analysis vcn_aoi …` — picks the optimal decision threshold, ranks per-sample weakness, and emits a top-K weakest parquet expanded per-lighting for downstream augmentation. Use when analyzing VCN classification failures, picking SDA augmentation targets, or auditing PASS/NO_PASS boundary cases.

skills/tao-analyze-gaps-visual-changenet/SKILL.md

Read pinned source

Source commit fce3cca4170be75e82c64920654d56ee2811b005

What was checked

Matches the observed repository HEAD
Yes
Scanner rules are current
Yes
Repository scan complete
No
Skill scan complete
No
Observed behavior
Network or broad shell
Last checked
Nov 9, 2026

These are observations at the recorded commit, not a guarantee of safety. Review the source before running it.

Install this skill

Choose an agent in your Stack or copy a command below. Commands fetch the pinned commit and copy only this skill directory. Existing destinations are refused. Copying a command does not install anything.

An install command is unavailable: current rules, complete repository and skill scans, an eligible behavior level and a permitted license are required.

Repository context

Repository stars (not skill ratings)
3,911
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.

Files and excerpt

File metadata and permitted, redacted excerpts come from the recorded scan. Source links are pinned to that commit.

Browse files
Read excerpt

# 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:

```bash
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 \
Back to top