Action recognition from video sequences. Supports RGB, optical flow, and joint (multi-stream) input types for classifying temporal actions in video clips. Use when training, evaluating, exporting, or running inference on a TAO action-recognition model. Trigger phrases include "train action recognition", "video action classification", "RGB + optical flow action model", "TAO ActionRecognition".
Action recognition from video sequences. Supports RGB, optical flow, and joint (multi-stream) input types for classifying temporal actions in video clips. Use when training, evaluating, exporting, or running inference on a TAO action-recognition model. Trigger phrases include "train action recognition", "video action classification", "RGB + optical flow action model", "TAO ActionRecognition".
license
Apache-2.0
compatibility
Requires docker + nvidia-container-toolkit.
metadata
{"version":"0.1.0","author":"NVIDIA Corporation"}
allowed-tools
Read Bash
tags
["action","recognition"]
Action Recognition
Standalone install? If this session was not initialized by the TAO skill bank plugin, run the skill first (host preflight, credentials, cross-skill discovery).
tao-setup
Action recognition from video sequences. Supports RGB, optical flow, and joint (multi-stream) input types for classifying temporal actions in video clips.
Set model.pretrained_model_path for pretrained backbone weights.
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).
docker run "${DOCKER_COMMON[@]}""$TAO_PYT_IMAGE" \
action_recognition train -e /specs/train.yaml
Evaluate:
docker run "${DOCKER_COMMON[@]}""$TAO_PYT_IMAGE" \
action_recognition evaluate -e /specs/evaluate.yaml
Inference:
docker run "${DOCKER_COMMON[@]}""$TAO_PYT_IMAGE" \
action_recognition inference -e /specs/inference.yaml
Export:
docker run "${DOCKER_COMMON[@]}""$TAO_PYT_IMAGE" \
action_recognition export -e /specs/export.yaml
Every action takes its spec with -e; results_dir is set in the spec or
overridden on the command line. Mount any pretrained-weights directory the spec
references, and keep every in-container path consistent across actions.
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: action_recognition
Formats: default
Training monitoring metrics:val_loss, val_acc
Evaluate task metrics:accuracy, m_accuracy
AutoML metric contract: for the required evaluation-backed baseline and
final comparison, use accuracy with maximize direction and run evaluate
through eval_fn for every recommendation. val_loss is suitable only for
an explicitly accepted training-proxy run because evaluate does not emit
it. Scratch runs with no starting checkpoint require a minimal default train
job followed by evaluation of its exact epoch/step checkpoint for the
baseline.
Per-Action Dataset Requirements
Action
Spec Key
Source
Files
List?
evaluate
evaluate.test_dataset_dir
train_datasets
test/ extracted from test.tar.gz
No
inference
inference.inference_dataset_dir
train_datasets
test/smile/ extracted from test/smile.tar.gz
No
train
dataset.train_dataset_dir
train_datasets
train/ extracted from train.tar.gz
No
train
dataset.val_dataset_dir
train_datasets
test/ extracted from test.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.
LOCAL_DATA = "/workspace/data/extracted"
If the source dataset is provided as the TAO sample archives
train.tar.gz, test.tar.gz, or test/smile.tar.gz, download and extract
them before launching the TAO container. The action-recognition entrypoints
expect directory paths and fail with NotADirectoryError when these spec keys
point at .tar.gz files.
For direct local-docker chaining without the SDK resolver, select the concrete
checkpoint produced by training, for example
model_epoch_000_step_00005.pth, and pass that exact file to evaluate,
inference, and export. Do not use the ar_model_latest.pth symlink unless
the user explicitly requests latest-checkpoint behavior. For resume training,
set train.resume_training_checkpoint_path to the exact epoch/step checkpoint
being resumed.
Eval Dataset
Optional. Test dataset may be distributed as test.tar.gz separate from
training; extract it and point the spec to the extracted test/ directory.
TAO training emits val_loss and val_acc for the packaged sample data, while
the evaluate action emits accuracy and m_accuracy. Use accuracy with
maximize direction for the normal evaluation-backed AutoML workflow. Use
val_loss with minimize direction only when the user explicitly accepts a
training-only proxy without the required impact baseline.
Important Parameters
model.model_type: Input type: rgb, of (optical flow), or joint (multi-stream).
model.backbone: Default resnet_18. Used as the spatial feature extractor.
dataset.label_map: Dictionary mapping class names to indices.
model.rgb_seq_length: Number of frames per clip for RGB input.
model.of_seq_length: Number of frames for optical flow input.
Strategy: auto (Lightning picks best strategy automatically)
No explicit num_nodes or distributed_strategy config — single-node oriented
Hardware
Minimum 1 GPU(s), recommended 2 GPU(s). 16GB+ VRAM per GPU. Memory depends on sequence length and input resolution. batch_size=2 is conservative for video data.
Error Patterns
Sequence length mismatch: Ensure video clips have enough frames for the configured rgb_seq_length or of_seq_length.
Evaluate/inference missing label map: Downstream actions rebuild the
ActionRecognitionModel before loading the checkpoint, so they need the same
dataset.label_map used during training. Include it with every evaluate or
inference spec; otherwise model construction fails before the checkpoint can be
validated.
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 action_recognition.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
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
inference
encryption_key
key
encryption key
inference
inference.checkpoint
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.of_pretrained_model_path
ptm_if_no_resume_model
PTM when no resume checkpoint exists
train
model.rgb_pretrained_model_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.
