Pose Classification
Pose classification using ST-GCN (Spatial Temporal Graph Convolutional Network). Classifies skeleton sequences into action categories from pose keypoint data.
Typically trained from scratch on skeleton data.
The packaged PyTorch Pose Classification CLI supports
dataset_convert
,
train
,
evaluate
,
export
, and
inference
.
dataset_convert
is conditional: run it only when the input is raw DeepStream BodyPose JSON. If the dataset is already converted to TAO-ready
.npy
/
.pkl
files, start directly with
train
on those files and mark dataset conversion as
not run: preconverted dataset provided
in validation reports. This model does not expose deploy, prune, quantize, or standalone retrain actions. Resume/retrain behavior uses
pose_classification train -e ...
with
train.resume_training_checkpoint_path
populated.
Dataclass Schemas
Generated TAO Core schemas are packaged in
schemas/.schema.json
, with
schemas/manifest.json
listing available actions. Each generated schema also emits
references/spec_template_.yaml
from the schema top-level
default
field. AutoML enablement is declared at the model layer in
references/skill_info.yaml
via
automl_enabled
. Runnable AutoML still requires
schemas/train.schema.json
and
references/spec_template_train.yaml
to exist and parse. Use the packaged train schema for
automl_default_parameters
,
automl_disabled_parameters
, defaults, min/max bounds, enums, option weights, math conditions, dependencies, and popular parameters. Do not expect
~/tao-core
at runtime; maintainers regenerate schemas/templates before packaging the skill bank.
Train Action Policy
This model is AutoML-enabled at the model layer. Before handling any train-stage request, read
references/skill_info.yaml
and resolve the run override from either an explicit
automl_policy
value or the user's workflow request. Use
automl_policy: on
by default and only expose
on
/
off
in new launch prompts. Treat phrases like "turn off AutoML", "disable AutoML", "no HPO", or "plain training" as
automl_policy: off
for this run only. When
automl_policy: on
,
automl_enabled: true
, and both
schemas/train.schema.json
and
references/spec_template_train.yaml
are packaged, route the train action through
tao-skill-bank:tao-run-automl
by default with this model's
skill_dir
. Preserve workflow/application overrides for datasets, specs, output directories, GPU/platform settings, parent checkpoints, and
automl_policy
. Use direct model training only when
automl_policy: off
or the packaged train schema/template is missing; in the missing-schema case, report that AutoML is enabled but not runnable for this model until schemas are generated.
Non-train actions such as
evaluate
,
inference
,
export
, and deploy flows stay in this model skill. The per-run
automl_policy
override does not change model metadata.
Training Requirements
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Installs
585
Repository
nvidia/skills
GitHub Stars
1.9K
First Seen
Jun 8, 2026
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