Sparse4D
Sparse4D for multi-camera temporal 3D object detection and tracking. Uses sparse queries with deformable attention across camera views and time for end-to-end 3D perception. Includes instance bank for temporal tracking.
Use a pretrained ResNet-101 backbone when one is available by setting
train.pretrained_model_path
. For local smoke validation, Sparse4D training
can run with an empty
train.pretrained_model_path
, but production runs should
still use a compatible PTM.
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.
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Installs
586
Repository
nvidia/skills
GitHub Stars
1.9K
First Seen
Jun 8, 2026
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