OCRNet

OCRNet for scene text recognition. Recognizes text content from cropped text region images. Supports CTC and attention-based decoders.

Set train.pretrained_model_path for pretrained OCR weights.

For TAO Deploy TensorRT actions (

gen_trt_engine

, TensorRT

evaluate

, and TensorRT

inference

), read

references/tao-deploy-ocrnet.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: ocrnet
Formats: default
Monitoring metric: val_acc

Per-Action Dataset Requirements

Action	Spec Key	Source	Files	List?
dataset_convert	dataset_convert.input_img_dir	id		No
dataset_convert	dataset_convert.gt_file	id		No
evaluate	dataset.character_list_file	eval_dataset	character_list	No
evaluate	evaluate.test_dataset_dir	eval_dataset	results/{dataset_convert_job_id}/dataset_convert/lmdb	No
export	dataset.character_list_file	eval_dataset	character_list	No
gen_trt_engine	gen_trt_engine.tensorrt.calibration.cal_image_dir	calibration_dataset		Yes
inference	dataset.character_list_file	eval_dataset	character_list	No
inference	inference.inference_dataset_dir	eval_dataset	test.tar.gz	No
prune	dataset.character_list_file	eval_dataset	character_list	No
quantize	dataset.train_dataset_dir	train_datasets	results/{dataset_convert_job_id}/dataset_convert/lmdb	Yes
quantize	dataset.val_dataset_dir	eval_dataset	results/{dataset_convert_job_id}/dataset_convert/lmdb	No
quantize	dataset.character_list_file	eval_dataset	character_list	No
quantize	dataset.quant_calibration_dataset.images_dir	train_datasets	train.tar.gz	No
retrain	dataset.train_dataset_dir	train_datasets	results/{dataset_convert_job_id}/dataset_convert/lmdb	Yes
retrain	dataset.val_dataset_dir	eval_dataset	results/{dataset_convert_job_id}/dataset_convert/lmdb	No
retrain	dataset.character_list_file	eval_dataset	character_list	No
train	dataset.train_dataset_dir	train_datasets	results/{dataset_convert_job_id}/dataset_convert/lmdb	Yes
train	dataset.val_dataset_dir	eval_dataset	results/{dataset_convert_job_id}/dataset_convert/lmdb	No
train	dataset.character_list_file	eval_dataset	character_list	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": 30,
    "train.checkpoint_interval": 10,
    "train.validation_interval": 10,
    "train.num_gpus": 1,
    "dataset.batch_size": 16,
    "dataset.train_dataset_dir": [f"{S3_TRAIN}/results/{dataset_convert_job_id}/dataset_convert/lmdb"],
    "dataset.val_dataset_dir": f"{S3_EVAL}/results/{dataset_convert_job_id}/dataset_convert/lmdb",
    "dataset.character_list_file": f"{S3_EVAL}/character_list",
}

gen_trt_engine (mandatory data sources):

python

{
    "gen_trt_engine.tensorrt.data_type": "fp16",
    "gen_trt_engine.tensorrt.calibration.cal_image_dir": [f"{S3_TRAIN}"],
}

evaluate (mandatory data sources):

python

{
    "dataset.character_list_file": f"{S3_EVAL}/character_list",
    "evaluate.test_dataset_dir": f"{S3_EVAL}/results/{dataset_convert_job_id}/dataset_convert/lmdb",
}

export (mandatory data sources):

python

{
    "dataset.character_list_file": f"{S3_EVAL}/character_list",
}

inference (mandatory data sources):

python

{
    "dataset.character_list_file": f"{S3_EVAL}/character_list",
    "inference.inference_dataset_dir": f"{S3_EVAL}/test.tar.gz",
}

prune (mandatory data sources):

python

{
    "dataset.character_list_file": f"{S3_EVAL}/character_list",
}

quantize (mandatory data sources):

python

{
    "dataset.train_dataset_dir": [f"{S3_TRAIN}/results/{dataset_convert_job_id}/dataset_convert/lmdb"],
    "dataset.val_dataset_dir": f"{S3_EVAL}/results/{dataset_convert_job_id}/dataset_convert/lmdb",
    "dataset.character_list_file": f"{S3_EVAL}/character_list",
    "dataset.quant_calibration_dataset.images_dir": f"{S3_TRAIN}/train.tar.gz",
}

retrain (mandatory data sources):

python

{
    "dataset.train_dataset_dir": [f"{S3_TRAIN}/results/{dataset_convert_job_id}/dataset_convert/lmdb"],
    "dataset.val_dataset_dir": f"{S3_EVAL}/results/{dataset_convert_job_id}/dataset_convert/lmdb",
    "dataset.character_list_file": f"{S3_EVAL}/character_list",
}

Eval Dataset

Optional. Test data provided as separate tarball.

Important Parameters

dataset.character_list_file: Path to character list defining the supported character set. This determines the output vocabulary size.
model.backbone: Default ResNet.
model.prediction: Decoder type. CTC or Attn (attention-based).
train.optim.lr: Learning rate. Default 1.0 (Adadelta optimizer). High default is specific to Adadelta.
dataset.batch_size: Per-GPU batch size. Default 16.

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.distributed_strategy`	Strategy name	`auto`

Strategy:
```
auto
```
for single-GPU, reads
```
train.distributed_strategy
```
from config when multi-GPU
No explicit
```
num_nodes
```
in train script — single-node oriented
Lightweight model, single GPU typically sufficient

Hardware

Minimum 1 GPU(s), recommended 1 GPU(s). 8GB+ VRAM per GPU. OCR text recognition is lightweight. Single GPU is typically sufficient.

Error Patterns

dataset_convert required: If using raw images + gt files, run dataset_convert first to produce LMDB format.

Character list mismatch: All characters in training data must be present in the character_list file.

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

ocrnet.config.json

Action	Spec Field	Inference Function	Meaning
dataset_convert	`results_dir`	`output_dir`	current job results directory
evaluate	`encryption_key`	`key`	encryption key
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	`model.pruned_graph_path`	`pruned_model`	parent pruned model
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
gen_trt_engine	`encryption_key`	`key`	encryption key
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	`encryption_key`	`key`	encryption key
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	`model.pruned_graph_path`	`pruned_model`	parent pruned model
inference	`results_dir`	`output_dir`	current job results directory
prune	`encryption_key`	`key`	encryption key
prune	`prune.checkpoint`	`parent_model`	model file inferred from the parent job results folder
prune	`prune.pruned_file`	`create_pth_file`	output PTH path
prune	`results_dir`	`output_dir`	current job results directory
quantize	`encryption_key`	`key`	encryption key
quantize	`quantize.model_path`	`parent_model`	model file inferred from the parent job results folder
quantize	`results_dir`	`output_dir`	current job results directory
retrain	`encryption_key`	`key`	encryption key
retrain	`model.pruned_graph_path`	`parent_model`	model file inferred from the parent job results folder
retrain	`results_dir`	`output_dir`	current job results directory
train	`encryption_key`	`key`	encryption key
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-ocrnet — OCRNet deploy workflow for TensorRT engine generation, TensorRT evaluation, and TensorRT inference using TAO Deploy.

tao-train-ocrnet

NPX Install

Tags

SKILL.md Content

OCRNet

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