Classification PyT

PyTorch image classification. Supports a wide range of backbones (FAN, EfficientNet, ResNet, etc.) with distillation and quantization for deployment.

Set model.backbone.pretrained_backbone_path for backbone weights or train.pretrained_model_path for full model.

For TAO Deploy TensorRT actions (

gen_trt_engine

, TensorRT

evaluate

, and TensorRT

inference

), read

references/tao-deploy-image-classification.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: image_classification
Formats: classification_pyt
Monitoring metric: val_acc_1

Per-Action Dataset Requirements

Action	Spec Key	Source	Files	List?
distill	dataset.train_dataset.images_dir	train_datasets	images_train.tar.gz	No
distill	dataset.classes_file	train_datasets	classes.txt	No
distill	dataset.val_dataset.images_dir	eval_dataset	images_val.tar.gz	No
evaluate	dataset.val_dataset.images_dir	eval_dataset	images_val.tar.gz	No
evaluate	dataset.classes_file	eval_dataset	classes.txt	No
evaluate	dataset.test_dataset.images_dir	inference_dataset	images_test.tar.gz	No
export	dataset.root_dir	train_datasets		No
inference	dataset.val_dataset.images_dir	eval_dataset	images_val.tar.gz	No
inference	dataset.classes_file	eval_dataset	classes.txt	No
inference	dataset.test_dataset.images_dir	inference_dataset	images_test.tar.gz	No
quantize	dataset.train_dataset.images_dir	train_datasets	images_train.tar.gz	No
quantize	dataset.classes_file	train_datasets	classes.txt	No
quantize	dataset.val_dataset.images_dir	eval_dataset	images_val.tar.gz	No
quantize	dataset.quant_calibration_dataset.images_dir	calibration_dataset	images_train.tar.gz	No
train	dataset.train_dataset.images_dir	train_datasets	images_train.tar.gz	No
train	dataset.classes_file	train_datasets	classes.txt	No
train	dataset.val_dataset.images_dir	eval_dataset	images_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"
S3_EVAL = "s3://bucket/data/eval"

train (mandatory data sources):

python

{
    "train.num_epochs": 2,
    "train.validation_interval": 2,
    "train.checkpoint_interval": 2,
    "train.num_gpus": 1,
    "dataset.train_dataset.images_dir": f"{S3_TRAIN}/images_train.tar.gz",
    "dataset.classes_file": f"{S3_TRAIN}/classes.txt",
    "dataset.val_dataset.images_dir": f"{S3_EVAL}/images_val.tar.gz",
}

export (mandatory data sources):

python

{
    "export.input_height": 224,
    "export.input_width": 224,
    "dataset.root_dir": f"{S3_TRAIN}",
}

gen_trt_engine:

python

{
    "gen_trt_engine.tensorrt.data_type": "fp16",
}

inference (mandatory data sources):

python

{
    "dataset.batch_size": 1,
    "dataset.val_dataset.images_dir": f"{S3_EVAL}/images_val.tar.gz",
    "dataset.classes_file": f"{S3_EVAL}/classes.txt",
    "dataset.test_dataset.images_dir": f"{S3_EVAL}/images_test.tar.gz",
}

distill (mandatory data sources):

python

{
    "dataset.train_dataset.images_dir": f"{S3_TRAIN}/images_train.tar.gz",
    "dataset.classes_file": f"{S3_TRAIN}/classes.txt",
    "dataset.val_dataset.images_dir": f"{S3_EVAL}/images_val.tar.gz",
}

evaluate (mandatory data sources):

python

{
    "dataset.val_dataset.images_dir": f"{S3_EVAL}/images_val.tar.gz",
    "dataset.classes_file": f"{S3_EVAL}/classes.txt",
    "dataset.test_dataset.images_dir": f"{S3_EVAL}/images_test.tar.gz",
}

quantize (mandatory data sources):

python

{
    "dataset.train_dataset.images_dir": f"{S3_TRAIN}/images_train.tar.gz",
    "dataset.classes_file": f"{S3_TRAIN}/classes.txt",
    "dataset.val_dataset.images_dir": f"{S3_EVAL}/images_val.tar.gz",
    "dataset.quant_calibration_dataset.images_dir": f"{S3_TRAIN}/images_train.tar.gz",
}

Eval Dataset

Optional. Validation images are provided as a separate tar alongside training images.

Important Parameters

dataset.num_classes: Number of classes. Default 20. Must match the number of subdirectories in your image tarballs.
model.backbone.type: Default fan_small_12_p4_hybrid. Supported backbones and their head in_channels (from model_params_mapping.py): FAN: fan_tiny, fan_small_12_p4_hybrid, fan_base_16_p4_hybrid, fan_large_16_p4_hybrid. GCViT: gcvit_tiny through gcvit_large. FasterViT: fastervit_0 through fastervit_6. ViT/EVA/DINO: vit_large_patch14_dinov2, eva02_large_patch14, etc. SigLIP-CLIPA: ViT-H-14-SigLIP-CLIPA-224, etc. Some backbones require non-default input resolution (384, 512, 768).
dataset.classes_file: Path to classes.txt listing class names.
train.optim.lr: Learning rate. Default 6e-5.
dataset.img_size: Input image size. Default 224.
dataset.batch_size: Per-GPU batch size. Default 8.

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]
`train.num_nodes`	Number of nodes	1

Multi-GPU strategy:
```
ddp_find_unused_parameters_true
```
No fsdp support

Multi-node env vars (set by orchestrator):

WORLD_SIZE

NODE_RANK

MASTER_ADDR

MASTER_PORT

NUM_GPU_PER_NODE

Hardware

Minimum 1 GPU(s), recommended 2 GPU(s). 16GB+ (V100 or A100) VRAM per GPU. Classification is generally lightweight. Most backbones at 224x224 fit well on 16GB GPUs with batch_size=8.

Error Patterns

CUDA out of memory: Reduce batch_size or use a smaller backbone.

num_classes mismatch: Ensure dataset.num_classes matches the actual class directories in your image tarballs and classes.txt.

Empty class directory: Every class in classes.txt must have at least one image in the corresponding subdirectory.

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

classification_pyt.config.json

Action	Spec Field	Inference Function	Meaning
distill	`distill.pretrained_teacher_model_path`	`parent_model`	model file inferred from the parent job results folder
distill	`results_dir`	`output_dir`	current job results directory
evaluate	`evaluate.checkpoint`	`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.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
quantize	`quantize.model_path`	`parent_model`	model file inferred from the parent job results folder
quantize	`results_dir`	`output_dir`	current job results directory
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.pretrained_model_path`	`ptm_if_no_resume_model`	PTM when no resume checkpoint exists
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-image-classification — Classification PyT deploy workflow for TensorRT engine generation, TensorRT evaluation, and TensorRT inference using TAO Deploy.

tao-train-image-classification

NPX Install

Tags

SKILL.md Content

Classification PyT

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