v0.1.0 to v0.1.0

42 added, 17 removed. Audit A to A.

---

name: tao-train-optical-inspection

description: Optical Inspection for defect detection using Siamese networks. Compares image pairs to detect manufacturing

defects, anomalies, or quality issues. Use when training, evaluating, exporting, or running inference for a TAO Optical

Inspection model on AOI / quality-control data. Trigger phrases include "train optical inspection", "AOI defect

detection", "Siamese defect classifier", "PCB / manufacturing inspection".

license: Apache-2.0

compatibility: Requires docker + nvidia-container-toolkit.

metadata:

version: "0.1.0"

author: NVIDIA Corporation

allowed-tools: Read Bash

tags:

- defect

- detection

---

# Optical Inspection

Optical inspection for defect detection using Siamese networks. Compares image pairs to detect manufacturing defects, anomalies, or quality issues.

Set train.pretrained_model_path for pretrained Siamese weights.

- For TAO Deploy TensorRT actions (`gen_trt_engine`, TensorRT `evaluate`, and TensorRT `inference`), read `references/tao-deploy-optical-inspection.md` first. Deploy spec templates live in this skill's `references/` folder with the `spec_template_deploy_*.yaml` prefix.

+ For TAO Deploy TensorRT actions (`gen_trt_engine`, TensorRT `evaluate`, and TensorRT `inference`), read `references/tao-deploy-optical-inspection.md` first. The parent PyT container does not expose `optical_inspection gen_trt_engine`; TensorRT engine generation is deploy-only. 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.

+ 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:** optical_inspection

- **Formats:** default

- **Monitoring metric:** val_acc

### 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`.

```python

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

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

+ S3_INFERENCE = "s3://bucket/data/inference"

```

**train (mandatory data sources):**

```python

{

"train.num_epochs": 30,

"train.checkpoint_interval": 10,

"train.validation_interval": 10,

"train.num_gpus": 1,

+ "dataset.batch_size": 8,

"dataset.train_dataset.images_dir": f"{S3_TRAIN}/images.tar.gz",

"dataset.train_dataset.csv_path": f"{S3_TRAIN}/dataset.csv",

"dataset.validation_dataset.images_dir": f"{S3_EVAL}/images.tar.gz",

"dataset.validation_dataset.csv_path": f"{S3_EVAL}/dataset.csv",

"dataset.test_dataset.images_dir": f"{S3_EVAL}/images.tar.gz",

"dataset.test_dataset.csv_path": f"{S3_EVAL}/dataset.csv",

}

```

- **gen_trt_engine (mandatory data sources):**

+ **evaluate (mandatory data sources):**

```python

{

- "gen_trt_engine.tensorrt.data_type": "fp16",

- "gen_trt_engine.tensorrt.calibration.cal_image_dir": [f"{S3_TRAIN}/images.tar.gz"],

+ "evaluate.checkpoint": "<selected train/AutoML checkpoint>",

+ "dataset.test_dataset.images_dir": f"{S3_EVAL}/images.tar.gz",

+ "dataset.test_dataset.csv_path": f"{S3_EVAL}/dataset.csv",

}

```

- **evaluate (mandatory data sources):**

+ Use the workflow's checkpoint resolver for downstream actions instead of guessing a filename. For Optical Inspection smoke runs, AutoML may produce `model_epoch_000_step_00006.pth`; resume can then produce `model_epoch_001_step_00012.pth`. Best-checkpoint actions should use the AutoML best child job's selected checkpoint, epoch-specific actions should pass the exact epoch/step checkpoint requested, and only explicit "latest" requests should resolve to the latest checkpoint.

+ **export:**

```python

{

- "dataset.test_dataset.images_dir": f"{S3_EVAL}/images.tar.gz",

- "dataset.test_dataset.csv_path": f"{S3_EVAL}/dataset.csv",

+ "export.checkpoint": "<selected train/AutoML checkpoint>",

+ "export.onnx_file": "/results/optical_inspection.onnx",

+ "export.input_width": 128,

+ "export.input_height": 512,

+ "export.batch_size": 1,

}

```

**inference (mandatory data sources):**

```python

{

- "dataset.infer_dataset.images_dir": f"{S3_EVAL}/images.tar.gz",

- "dataset.infer_dataset.csv_path": f"{S3_EVAL}/dataset.csv",

+ "inference.checkpoint": "<selected train/AutoML checkpoint>",

+ "dataset.infer_dataset.images_dir": f"{S3_INFERENCE}/images.tar.gz",

+ "dataset.infer_dataset.csv_path": f"{S3_INFERENCE}/dataset.csv",

}

```

+ ## Dataset Convert

+ Dataset conversion is optional for Optical Inspection. If the dataset is already in TAO-ready Optical Inspection format, start directly from the `images.tar.gz` plus `dataset.csv` splits and run `train`, `evaluate`, `inference`, and downstream checkpoint/export/deploy actions on that converted data.

+ The PyT container exposes `optical_inspection dataset_convert`, but this model skill does not package a `dataset_convert` action/template. The converter expects the raw Factory PCB layout (`root_dataset_dir`, train/val/all PCB directories, `golden_csv_dir`, `project_name`, and `bot_top`). The S3 validation bucket currently contains preconverted Optical Inspection `images.tar.gz` plus `dataset.csv` splits, not the raw PCB/golden CSV source. Do not synthesize a fake PCB dataset. In model validation reports, mark dataset conversion as `not run: preconverted dataset provided` rather than failed or blocked when only converted data is available.

+ When using the preconverted S3 validation tarballs locally, verify the extracted directory before writing specs. The tarballs may unpack an `images/` wrapper directory; point `dataset.*.images_dir` at the inner directory that contains `golden/` and the board/image folders referenced by `dataset.csv`, for example `.../<split>/images/images`, not the outer wrapper.

## Eval Dataset

Optional. Eval dataset uses same format (images + CSV).

## Important Parameters

- **model.model_type**: Siamese variant. Options include Siamese, Siamese_3.

- **model.model_backbone**: Default custom.

- **model.embedding_vectors**: Number of embedding dimensions. Default 5.

- **train.optim.lr**: Learning rate. Default 5e-4.

+ - **dataset.batch_size**: Training batch size. Must be greater than 1; use `2` or higher for minimal smoke runs.

- **dataset.num_input**: Number of input images per comparison.

- **dataset.input_map**: Mapping of input channels / image pairs.

## 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 only

- Lightweight Siamese network, single GPU typically sufficient

## Hardware

Minimum 1 GPU(s), recommended 1 GPU(s). 8GB+ VRAM per GPU. Siamese networks for inspection are lightweight. Single GPU sufficient.

## Error Patterns

**CSV format error**: Ensure dataset.csv has the correct column format for image pair paths and labels.

+ **Extracted image root mismatch**: If train, evaluate, or inference cannot find paths from `dataset.csv`, inspect the extracted `images.tar.gz` tree. The TAO-ready root must contain `golden/` plus the board folders referenced in the CSV. For validation S3 tarballs this can be one level below the extraction target, such as `images/images`.

+ **Training batch size assertion**: The Optical Inspection dataloader rejects

+ `dataset.batch_size: 1` for train. Keep the template default of 8 for normal

+ runs, or set `dataset.batch_size: 2` for minimal AutoML smoke validation.

+ **PyTorch checkpoint load failure on downstream actions**: For checkpoints

+ produced by the same trusted TAO train/AutoML workflow, set

+ `TORCH_FORCE_NO_WEIGHTS_ONLY_LOAD=1` for evaluate, inference, export, and

+ resume jobs if the current PyTorch default blocks loading 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 `optical_inspection.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-optical-inspection](references/tao-deploy-optical-inspection.md) — Optical Inspection deploy workflow for TensorRT engine generation, TensorRT evaluation, and TensorRT inference using TAO Deploy.

+ - [tao-deploy-optical-inspection](references/tao-deploy-optical-inspection.md)