CenterPose for keypoint / pose estimation. Detects object centers and regresses keypoint locations for 6-DoF object pose estimation. Use when training, evaluating, exporting, or running inference for a TAO CenterPose model. Trigger phrases include "train CenterPose", "6-DoF object pose", "keypoint estimation", "object pose regression".
CenterPose for keypoint / pose estimation. Detects object centers and regresses keypoint locations for 6-DoF object pose estimation. Use when training, evaluating, exporting, or running inference for a TAO CenterPose model. Trigger phrases include "train CenterPose", "6-DoF object pose", "keypoint estimation", "object pose regression".
license
Apache-2.0
compatibility
Requires docker + nvidia-container-toolkit.
metadata
{"version":"0.1.0","author":"NVIDIA Corporation"}
allowed-tools
Read Bash
tags
["pose","estimation"]
CenterPose
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).
CenterPose for keypoint / pose estimation. Detects object centers and regresses keypoint locations. Used for 6-DoF object pose estimation.
Set model.backbone.pretrained_backbone_path.
For TAO Deploy TensorRT actions (gen_trt_engine, TensorRT evaluate, and TensorRT inference), use the deploy spec templates packaged in this skill's references/ folder with the spec_template_deploy_*.yaml prefix.
Dataclass Schemas
Generated TAO Core schemas are packaged in schemas/<action>.schema.json, with schemas/manifest.json listing available actions. Each generated schema also emits references/spec_template_<action>.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 for an action requires schemas/<action>.schema.json and references/spec_template_<action>.yaml to exist and parse. Use the packaged selected-action 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
Dataset type: centerpose
Formats: default
Training monitoring metrics:val_3DIoU, val_2DMPE
Evaluate task metrics:test_3DIoU, test_2DMPE
AutoML metric contract: use test_3DIoU with maximize direction for
the required evaluation-backed baseline, every recommendation through
eval_fn, best-model selection, and final evaluation. val_3DIoU is emitted
by training but not by the evaluate action, so it is only suitable for an
explicitly accepted training-proxy run without an impact baseline.
Per-Action Dataset Requirements
Action
Spec Key
Source
Files
List?
evaluate
dataset.test_data
eval_dataset
test.tar.gz
No
gen_trt_engine
gen_trt_engine.tensorrt.calibration.cal_image_dir
calibration_dataset
train.tar.gz
Yes
inference
dataset.inference_data
inference_dataset
val.tar.gz
No
train
dataset.train_data
train_datasets
train.tar.gz
No
train
dataset.val_data
eval_dataset
val.tar.gz
No
Typical Spec Overrides
Data source overrides are mandatory for every action — the agent MUST construct data source paths from the Per-Action Dataset Requirements table above and include them in spec_overrides.
Optional. Val and test datasets are provided as separate tarballs. Training
writes val_3DIoU/val_2DMPE KPIs, while evaluate writes
test_3DIoU/test_2DMPE; do not configure an evaluate-backed AutoML callback
to extract the training-prefixed names.
Important Parameters
dataset.num_classes: Number of object categories. Default 1.
dataset.num_joints: Number of keypoints per object. Fixed at 8 (bbox keypoints). Valid range: exactly 8.
dataset.input_res: Input resolution. Fixed at 512. Output resolution fixed at 128.
Strategy: auto (Lightning picks the best strategy automatically)
No explicit num_nodes or distributed_strategy config — single-node only
No sync_batchnorm
Export / TRT Defaults
Export input: 512x512 (fixed), opset 16
TRT data types: FP32, FP16, INT8
TRT opt_batch_size: 4, max_batch_size: 8
Hardware
Minimum 1 GPU(s), recommended 2 GPU(s). 16GB+ VRAM per GPU. CenterPose is moderately memory-intensive depending on input resolution and number of keypoints.
Error Patterns
num_joints mismatch: Ensure dataset.num_joints matches the keypoint count in your annotations.
Extract S3 tarballs for local Docker: The starter-kit S3 data is packaged as
train.tar.gz, val.tar.gz, and test.tar.gz, but the CenterPose TAO actions
consume extracted folders. Extract each archive and set dataset.train_data,
dataset.val_data, dataset.test_data, and dataset.inference_data to the
extracted split directories.
Checkpoint handoff: CenterPose training writes concrete checkpoints such as
model_epoch_000_step_00008.pth and a centerpose_model_latest.pth symlink.
Use the SDK/model checkpoint resolver or the exact epoch/step checkpoint for
evaluate, inference, export, and resume. Use the symlink only when the user
explicitly asks for latest.
TAO Deploy postprocessor compatibility: Use the deploy image resolved from
the skill's pinned deploy image or the selected platform. A successful gen_trt_engine run does
not prove deploy evaluate or inference works; inspect those action exit codes
and logs separately, especially for CenterPose postprocessor errors such as
TypeError: only 0-dimensional arrays can be converted to Python scalars.
Spec Param / Parent Model Inference
Model-specific inference mappings belong in this MD file, not in config.json. Generated runners should read this section and apply the mappings with SDK helpers before create_job(). This mirrors the old microservices infer_params.py flow.
Inference mappings from TAO Core centerpose.config.json:
Action
Spec Field
Inference Function
Meaning
evaluate
encryption_key
key
encryption key
evaluate
evaluate.checkpoint
parent_model
model file inferred from the parent job results folder
evaluate
evaluate.trt_engine
parent_model
model file inferred from the parent job results folder
evaluate
results_dir
output_dir
current job results directory
export
encryption_key
key
encryption key
export
export.checkpoint
parent_model
model file inferred from the parent job results folder
export
export.onnx_file
create_onnx_file
output ONNX path
export
results_dir
output_dir
current job results directory
gen_trt_engine
encryption_key
key
encryption key
gen_trt_engine
gen_trt_engine.onnx_file
parent_model
model file inferred from the parent job results folder
model file inferred from the parent job results folder
inference
inference.trt_engine
parent_model
model file inferred from the parent job results folder
inference
results_dir
output_dir
current job results directory
train
encryption_key
key
encryption key
train
model.backbone.pretrained_backbone_path
ptm_if_no_resume_model
PTM when no resume checkpoint exists
train
results_dir
output_dir
current job results directory
train
train.resume_training_checkpoint_path
resume_model
model file inferred from the current job results folder
For parent_model or parent_model_folder, pass the upstream train/export/AutoML child job id as parent_job_id. The SDK lists the parent result folder, filters checkpoint artifacts, and returns the selected model file or folder. Do not add these mappings back to config.json and do not patch generated runner scripts to guess checkpoint paths.
