tao-train-pose-classification

Pose Classification

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).

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.

Quick Start (docker run)

Docker-native launch — no TAO SDK and no Python on the host. Use the local Docker/platform skill instead when it gives a stricter environment-specific command (non-root UID mapping, cache redirects, remote daemons).

TAO_PYT_IMAGE_DEFAULT=nvcr.io/nvstaging/tao/tao-toolkit-pyt:7.2.0-rc-36-multiarch  # versions-key: images.tao_toolkit.pyt
TAO_PYT_IMAGE="${TAO_PYT_IMAGE:-$TAO_PYT_IMAGE_DEFAULT}"
RUN_ROOT="${RUN_ROOT:-$PWD}"
DOCKER_COMMON=(
  --rm --gpus all --ipc=host
  --shm-size=8g
  --ulimit memlock=-1
  --ulimit stack=67108864
  -v "$RUN_ROOT/data:/data:ro"
  -v "$RUN_ROOT/specs:/specs:ro"
  -v "$RUN_ROOT/results:/results"
)

Dataset convert:

docker run "${DOCKER_COMMON[@]}" "$TAO_PYT_IMAGE" \
  pose_classification dataset_convert -e /specs/dataset_convert.yaml

Train:

docker run "${DOCKER_COMMON[@]}" "$TAO_PYT_IMAGE" \
  pose_classification train -e /specs/train.yaml

Evaluate: