tao-train-ocrnet

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.

S3_TRAIN = "s3://bucket/data/train"
S3_EVAL = "s3://bucket/data/eval"

train (mandatory data sources):

{
    "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):

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

evaluate (mandatory data sources):

{
    "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):

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

inference (mandatory data sources):

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

prune (mandatory data sources):

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

quantize (mandatory data sources):

{
    "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):

{
    "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 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-ocrnet — OCRNet deploy workflow for TensorRT engine generation, TensorRT evaluation, and TensorRT inference using TAO Deploy.