ML Recog

ML Recog

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).

Metric learning recognition for fine-grained visual recognition. Learns embeddings for retrieval-based matching (e.g., retail product recognition). Uses triplet/​contrastive losses.

Set model.pretrained_model_path for pretrained backbone.

For TAO Deploy TensorRT actions (gen_trt_engine, TensorRT evaluate, and TensorRT inference), read references/​tao-deploy-metric-learning-recognition.md first. Deploy spec templates live 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: ml_recog
• Formats: default
• Monitoring metric: val Precision at Rank 1
• AutoML metric contract: Use val Precision at Rank 1 emitted during training and maximize it. Use test Precision at Rank 1 only for standalone selected-checkpoint validation.
• Standalone evaluation metric: test Precision at Rank 1. Use the training val Precision at Rank 1 KPI for AutoML recommendation ranking and the test KPI only to verify the selected checkpoint on the evaluation reference/​query split.

Per-Action Dataset Requirements

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.

S3_TRAIN = "s3://bucket/​data/​train"

train (mandatory data sources):

{
    "train.num_epochs": 30,
    "train.checkpoint_interval": 10,
    "train.validation_interval": 10,
    "train.num_gpus": 1,
    "dataset.train_dataset": f"{S3_TRAIN}/​metric_learning_recognition/​retail-product-checkout-dataset_classification_demo/​known_classes/​train.tar.gz",
    "dataset.val_dataset": {"reference": f"{S3_TRAIN}/​metric_learning_recognition/​retail-product-checkout-dataset_classification_demo/​known_classes/​reference.tar.gz", "query": f"{S3_TRAIN}/​metric_learning_recognition/​retail-product-checkout-dataset_classification_demo/​known_classes/​val.tar.gz"},
}

evaluate (mandatory data sources):

{
    "evaluate.checkpoint": "<selected train/​AutoML checkpoint>",
    "dataset.val_dataset": {"reference": f"{S3_TRAIN}/​metric_learning_recognition/​retail-product-checkout-dataset_classification_demo/​unknown_classes/​reference.tar.gz", "query": f"{S3_TRAIN}/​metric_learning_recognition/​retail-product-checkout-dataset_classification_demo/​unknown_classes/​test.tar.gz"},
}

inference (mandatory data sources):

{
    "inference.checkpoint": "<selected train/​AutoML checkpoint>",
    "dataset.val_dataset": {"reference": f"{S3_TRAIN}/​metric_learning_recognition/​retail-product-checkout-dataset_classification_demo/​unknown_classes/​reference.tar.gz"},
    "inference.input_path": f"{S3_TRAIN}/​metric_learning_recognition/​retail-product-checkout-dataset_classification_demo/​unknown_classes/​test.tar.gz",
}

Eval Dataset

Required. Evaluation requires reference and query datasets for retrieval metrics.

Important Parameters

• model.backbone: Default resnet_50. Options: resnet_50, resnet_101, fan_small, fan_base, fan_large, fan_tiny, nvdinov2_vit_large_legacy.
• model.feat_dim: Embedding dimension. Default 256. Output feature vector size for similarity matching.
• train.batch_size: Per-GPU batch size. Default 4. val_batch_size also 4. For training and AutoML search, train.batch_size must be divisible by dataset.num_instance.
• dataset.num_instance: Instances per identity in a batch (P/​K sampling). Default 4. Controls how many images of the same class appear together. If using a custom AutoML range for train.batch_size, use explicit options that are multiples of this value.
• train.optim.trunk.base_lr: Learning rate for the trunk (backbone). Default 3.5e-4 (Adam).
• train.optim.embedder.base_lr: Learning rate for the embedding head. Default 3.5e-4.
• train.optim.triplet_loss_margin: Margin for triplet loss. Default 0.3. smooth_loss=True by default.
• train.optim.miner_function_margin: Hard mining margin. Default 0.1. Controls pair mining difficulty.
• train.optim.steps: LR decay steps. Default [40, 70] with gamma=0.1.
• dataset.train_dataset: Path to training images organized in class folders.
• dataset.val_dataset: Dict with 'reference' and 'query' keys pointing to ImageNet-format directories for retrieval evaluation.

Multi-GPU / Multi-Node

Launch method: Lightning-managed (single python process, Lightning spawns workers).

• 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. Metric learning benefits from larger batch sizes for better triplet sampling but is otherwise moderate on memory.

Error Patterns

Reference/​query mismatch: Ensure reference and query datasets share compatible class namespaces for evaluation.

PyTorch 2.6 checkpoint load failure on checkpoint actions: Current TAO ML-Recog checkpoints may contain OmegaConf objects. For checkpoints produced by the same trusted TAO train/​AutoML workflow, set TORCH_FORCE_NO_WEIGHTS_ONLY_LOAD=1 in downstream evaluate, inference, export, or resume/​retrain job env vars so Lightning can load the full checkpoint. Do not use this env var for untrusted checkpoints.

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 ml_recog.config.json:

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.

Deployment

• tao-deploy-metric-learning-recognition