TAO VCN Classify Gap Analysis Skill
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
tao_toolkit.data_services
image declared in
versions.yaml
(resolved at runtime — see Setup). The container's entrypoint takes
[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; introspect with
docker run ... gap_analysis vcn_aoi --cfg=job
). (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
docker run --rm "$DS_IMAGE" gap_analysis vcn_aoi --cfg=job
and 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
.
Inputs
Experiment result directory
— contains
inference/inference.csv
from TAO VCN Classify inference. Required columns:
input_path
,
object_name
,
label
,
siamese_score
. Pass the
directory
(e.g.
inference/latest/
), not the CSV file — the container reads
inference_results_dir/inference.csv
.
Training code/config directory
— contains the VCN train YAML. The container reads
dataset.classify.input_map
(lighting condition list) and
dataset.classify.image_ext
from it to expand each weak sample into one row per lighting.
Dataset directory
— image root prepended to the relative
input_path
from each row (
kpi_media_path
).
Schema overrides
—
min_recall
,
top_k_per_label
, and optionally a hard-pinned
threshold
are passed as Hydra overrides (defaults:
min_recall=1.0
,
top_k_per_label=50
,
threshold=-1.0
meaning sweep).
top_k_per_label
must be a positive integer
— omitting it flips the container into "below-threshold filter" mode, which at
min_recall=1.0
returns only PASS misclassifications and zero NO_PASS rows. See Common pitfalls.
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Installs
581
Repository
nvidia/skills
GitHub Stars
1.9K
First Seen
Jun 8, 2026
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