ML Recog

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 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. Treat phrases like "turn off AutoML", "disable AutoML", "no HPO", or "plain training" as

automl_policy: off

for this run only; otherwise default to

auto

. When

automl_policy: auto

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

Per-Action Dataset Requirements

Action	Spec Key	Source	Files	List?
evaluate	dataset.val_dataset	train_datasets	reference: metric_learning_recognition/retail-product-checkout-dataset_classification_demo/unknown_classes/reference.tar.gz, query: metric_learning_recognition/retail-product-checkout-dataset_classification_demo/unknown_classes/test.tar.gz	No
gen_trt_engine	gen_trt_engine.tensorrt.calibration.cal_image_dir	calibration_dataset	metric_learning_recognition/retail-product-checkout-dataset_classification_demo/known_classes/test.tar.gz	Yes
inference	dataset.val_dataset	train_datasets	reference: metric_learning_recognition/retail-product-checkout-dataset_classification_demo/unknown_classes/reference.tar.gz, query:	No
inference	inference.input_path	train_datasets	metric_learning_recognition/retail-product-checkout-dataset_classification_demo/unknown_classes/test.tar.gz	No
train	dataset.train_dataset	train_datasets	metric_learning_recognition/retail-product-checkout-dataset_classification_demo/known_classes/train.tar.gz	No
train	dataset.val_dataset	train_datasets	reference: metric_learning_recognition/retail-product-checkout-dataset_classification_demo/known_classes/reference.tar.gz, query: metric_learning_recognition/retail-product-checkout-dataset_classification_demo/known_classes/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

python

S3_TRAIN = "s3://bucket/data/train"

train (mandatory data sources):

python

{
    "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"},
}

gen_trt_engine (mandatory data sources):

python

{
    "gen_trt_engine.tensorrt.data_type": "INT8",
    "gen_trt_engine.tensorrt.calibration.cal_image_dir": [f"{S3_TRAIN}/metric_learning_recognition/retail-product-checkout-dataset_classification_demo/known_classes/test.tar.gz"],
}

evaluate (mandatory data sources):

python

{
    "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):

python

{
    "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.
dataset.num_instance: Instances per identity in a batch (P/K sampling). Default 4. Controls how many images of the same class appear together.
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).

Spec Key	Description	Default
`train.num_gpus`	Number of GPUs	1
`train.gpu_ids`	GPU device indices	[0]

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.

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

Action	Spec Field	Inference Function	Meaning
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	`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	`gen_trt_engine.onnx_file`	`parent_model`	model file inferred from the parent job results folder
gen_trt_engine	`gen_trt_engine.tensorrt.calibration.cal_cache_file`	`create_cal_cache`	calibration cache path
gen_trt_engine	`gen_trt_engine.trt_engine`	`create_engine_file`	output TensorRT engine path
gen_trt_engine	`results_dir`	`output_dir`	current job results directory
inference	`inference.checkpoint`	`parent_model`	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	`model.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

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 — MLRecog deploy workflow for TensorRT engine generation, TensorRT evaluation, and TensorRT inference using TAO Deploy.

tao-train-metric-learning-recognition

NPX Install

Tags

SKILL.md Content

ML Recog

Dataclass Schemas

Train Action Policy

Training Requirements

Per-Action Dataset Requirements

Typical Spec Overrides

Eval Dataset

Important Parameters

Multi-GPU / Multi-Node

Hardware

Error Patterns

Spec Param / Parent Model Inference

Deployment